{"title":"Data Science \u0026 Big Data","description":null,"products":[{"product_id":"components-of-a-big-data-and-ai-solution-introduction","title":"Components of a Big Data and AI Solution Introduction","description":"\u003cdiv\u003e\n\u003cp\u003eUnlock the true potential of your business with our cutting-edge \u003cstrong\u003eComponents of a Data and AI Solution\u003c\/strong\u003e course! This hands-on introduction takes you on a transformative journey from raw data to invaluable insights, leveraging the power of data and AI. Gain a competitive edge by understanding what tools can do and how to extract real business value from their output.\u003c\/p\u003e\r\n\u003cp\u003eOur comprehensive training integrates an overarching view of the data-to-insights process with focused data science expertise, empowering you to store, manage, process, and analyze massive volumes of structured and unstructured data. Plus, decision-makers benefit significantly from exposure to available options and establishing a common vocabulary with technical practitioners.\u003c\/p\u003e\r\n\u003cp\u003eMaximize your potential with our Components of a Data and AI Solution training today!\u003c\/p\u003e\n\u003c\/div\u003e\u003cdiv\u003e\n\u003ch3\u003eComponents of a Big Data and AI Solution Introduction Benefits\u003c\/h3\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eIn this course, you will:\u003c\/strong\u003e\u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003eStore, manage, and analyze structured and unstructured data.\u003c\/li\u003e\n\u003cli\u003eSelect the appropriate storage type for different datasets.\u003c\/li\u003e\n\u003cli\u003eProcess large datasets efficiently using distributed systems like HDFS and Spark to extract valuable insights.\u003c\/li\u003e\n\u003cli\u003eApply common machine learning techniques such as clustering, classification, and regression using SparkML and Python.\u003c\/li\u003e\n\u003cli\u003eHarness the power of generative models like ChatGPT programmatically.\u003c\/li\u003e\n\u003cli\u003eBenefit from continued support with post-course one-on-one instructor coaching.\u003c\/li\u003e\n\u003cli\u003eAccess a computing sandbox for hands-on practice and experimentation.\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003ePrerequisites\u003c\/strong\u003e\u003c\/p\u003e\n\u003cp\u003eNone.\u003c\/p\u003e\n\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003c\/div\u003e\u003cdiv\u003e\u003ch3\u003eData and AI Solution Course Outline\u003c\/h3\u003e\u003c\/div\u003e\u003cdiv\u003e\n\u003ch4\u003eModule 1: Data and the Enterprise\u003c\/h4\u003e\n\u003cp\u003eDefine the importance of data and its analysis in today's data-driven world\u003c\/p\u003e\n\u003cp\u003eDifferentiate between different types of data\u003cstrong\u003e\u003c\/strong\u003e\u003c\/p\u003e\n\u003c\/div\u003e\u003cdiv\u003e\n\u003ch4\u003eModule 2: Storing and Querying Data\u003c\/h4\u003e\n\u003cp\u003eDescribe different types of data storage\u003c\/p\u003e\n\u003cp\u003eAssess the quality of data\u003c\/p\u003e\n\u003cp\u003eOutline the ETL and ELT processes\u003cstrong\u003e\u003c\/strong\u003e\u003c\/p\u003e\n\u003c\/div\u003e\u003cdiv\u003e\n\u003ch4\u003eModule 3: HDFS, Spark, and Kafka\u003c\/h4\u003e\n\u003cp\u003eDefine Hadoop and HDFS\u003c\/p\u003e\n\u003cp\u003eDescribe Spark\u003c\/p\u003e\n\u003cp\u003eWork with Kafka\u003cstrong\u003e\u003c\/strong\u003e\u003c\/p\u003e\n\u003c\/div\u003e\u003cdiv\u003e\n\u003ch4\u003eModule 4: NoSQL Databases\u003c\/h4\u003e\n\u003cp\u003eDefine NoSQL\u003c\/p\u003e\n\u003cp\u003eIntroduce the different types of Big Data data stores\u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003eKey-value\u003c\/li\u003e\n\u003cli\u003eDocument\u003c\/li\u003e\n\u003cli\u003eColumn family\u003c\/li\u003e\n\u003cli\u003eGraph\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cp\u003eGain experience using Big Data data stores, including\u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003eRedis\u003c\/li\u003e\n\u003cli\u003eMongoDB\u003c\/li\u003e\n\u003cli\u003eCassandra\u003c\/li\u003e\n\u003cli\u003eNeo4j\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cp\u003ePerform text searches with Lucene and Elasticsearch\u003cstrong\u003e\u003c\/strong\u003e\u003c\/p\u003e\n\u003c\/div\u003e\u003cdiv\u003e\n\u003ch4\u003eModule 5: Analyzing and Interpreting Data\u003c\/h4\u003e\n\u003cp\u003eDiscuss statistical analysis of Data\u003c\/p\u003e\n\u003cp\u003eExplore machine learning including\u003c\/p\u003e\n\u003cp\u003eRecommendations\u003c\/p\u003e\n\u003cp\u003eClustering\u003c\/p\u003e\n\u003cp\u003eClassification\u003cstrong\u003e\u003c\/strong\u003e\u003c\/p\u003e\n\u003c\/div\u003e\u003cdiv\u003e\n\u003ch4\u003eModule 6: Neural Networks\u003c\/h4\u003e\n\u003cp\u003eIntroduce key ideas behind neural networks\u003c\/p\u003e\n\u003cp\u003eUtilize deep neural networks for more complex problems\u003c\/p\u003e\n\u003cp\u003eExamine generational neural networks\u003cstrong\u003e\u003c\/strong\u003e\u003c\/p\u003e\n\u003c\/div\u003e\u003cdiv\u003e\n\u003ch4\u003eModule 7: Visualization\u003c\/h4\u003e\n\u003cp\u003eVisualize data to communicate results\u003c\/p\u003e\n\u003cp\u003eExamine plots used for different purposes\u003c\/p\u003e\n\u003c\/div\u003e","brand":"Learning Tree","offers":[{"title":"268A34US \/ 2026-08-26T09:00:00 \/ Herndon, VA","offer_id":47534217035995,"sku":"US-1250-IL","price":2228.0,"currency_code":"USD","in_stock":true},{"title":"267D01US \/ 2026-07-22T09:00:00 \/ Online","offer_id":48216544346331,"sku":"US-1250-IL","price":2228.0,"currency_code":"USD","in_stock":true},{"title":"26BA53US \/ 2026-11-23T09:00:00 \/ Herndon, VA","offer_id":48216544379099,"sku":"US-1250-IL","price":2228.0,"currency_code":"USD","in_stock":true},{"title":"271A74US \/ 2027-01-20T09:00:00 \/ Herndon, VA","offer_id":48216544411867,"sku":"US-1250-IL","price":2228.0,"currency_code":"USD","in_stock":true},{"title":"273A59US \/ 2027-03-23T09:00:00 \/ Herndon, VA","offer_id":48500533362907,"sku":"US-1250-IL","price":2228.0,"currency_code":"USD","in_stock":true},{"title":"275A54US \/ 2027-05-26T09:00:00 \/ Herndon, VA","offer_id":48805406998747,"sku":"US-1250-IL","price":2228.0,"currency_code":"USD","in_stock":true}]},{"product_id":"data-visualization-with-python-training","title":"Data Visualization with Python Training","description":"\u003cdiv\u003e\u003cp\u003eIn this \u003cstrong\u003eData Visualization with Python\u003c\/strong\u003e Training course, you’ll learn how to use Python’s data visualization libraries, including NumPy, Pandas, Matplotlib, and Seaborn to better understand data analytics.\u003c\/p\u003e\u003c\/div\u003e\u003cdiv\u003e\n\u003ch3\u003eData Visualization with Python Training Benefits\u003c\/h3\u003e\n\u003cul\u003e\n\u003cli\u003e\u003cp\u003eLearn how to use various plot types with Python\u003c\/p\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cp\u003eExplore and work with different libraries for data visualization\u003c\/p\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cp\u003eUnderstand and create effective visualizations\u003c\/p\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cp\u003eImprove your Python data wrangling skills\u003c\/p\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cp\u003eWork with industry-standard tools, including \u003ca href=\"https:\/\/matplotlib.org\/\" title=\"Matplotlib: Visualization with Python\" target=\"_blank\" rel=\"external nofollow noopener\"\u003eMatplotlib\u003c\/a\u003e, Seaborn, and Bokeh\u003c\/p\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cp\u003eLearn different data formats and representations\u003c\/p\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cp\u003eLearn how to use Geoplotlib and \u003ca href=\"https:\/\/bokeh.org\/\" title=\"Bokeh: Visualization with Python\" target=\"_blank\" rel=\"external nofollow noopener\"\u003eBokeh\u003c\/a\u003e\u003c\/p\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cp\u003eContinue learning and face new challenges with after-course one-on-one instructor coaching\u003c\/p\u003e\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003c\/div\u003e\u003cdiv\u003e\u003ch3\u003eData Visualization with Python Training Outline\u003c\/h3\u003e\u003c\/div\u003e\u003cdiv\u003e\n\u003ch4\u003eModule 1: Fundamentals of Python\u003c\/h4\u003e\n\u003cp\u003eIn this module, you will learn about:\u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003eImportance of Data Visualization\u003c\/li\u003e\n\u003cli\u003eVisualization Using Python\u003c\/li\u003e\n\u003cli\u003eData Cleaning\u003c\/li\u003e\n\u003cli\u003eData Wrangling\u003c\/li\u003e\n\u003cli\u003eTypes of Data\u003c\/li\u003e\n\u003cli\u003eStatistics\u003c\/li\u003e\n\u003cli\u003eProbability\u003c\/li\u003e\n\u003cli\u003eExploratory Data Analysis\u003c\/li\u003e\n\u003cli\u003ePython\u003c\/li\u003e\n\u003cli\u003eJupyter Notebook\u003c\/li\u003e\n\u003cli\u003eGoogle Colab and Kaggle Notebooks\u003c\/li\u003e\n\u003cli\u003eJupyterLab\u003c\/li\u003e\n\u003cli\u003eBasic Python Data Types\u003c\/li\u003e\n\u003cli\u003eFlow Control\u003c\/li\u003e\n\u003cli\u003eSlicing\u003c\/li\u003e\n\u003cli\u003eDefining Functions\u003c\/li\u003e\n\u003cli\u003eLambdas\u003c\/li\u003e\n\u003cli\u003eClasses\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003c\/div\u003e\u003cdiv\u003e\n\u003ch4\u003eModule 2: NumPy and Pandas\u003c\/h4\u003e\n\u003cp\u003eIn this module, you will learn about:\u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003eNumPy\u003c\/li\u003e\n\u003cli\u003eThe NumPy ndarray Object\u003c\/li\u003e\n\u003cli\u003eSlicing ndarrays\u003c\/li\u003e\n\u003cli\u003eBoolean Indexing\u003c\/li\u003e\n\u003cli\u003eElement-wise Arithmetic\u003c\/li\u003e\n\u003cli\u003eTranspose of a ndarray\u003c\/li\u003e\n\u003cli\u003eDot Products\u003c\/li\u003e\n\u003cli\u003eStacking\u003c\/li\u003e\n\u003cli\u003eSciPy\u003c\/li\u003e\n\u003cli\u003epandas\u003c\/li\u003e\n\u003cli\u003eSeries and DataFrames\u003c\/li\u003e\n\u003cli\u003eLoading and Saving Data With pandas\u003c\/li\u003e\n\u003cli\u003eCreating DataFrames\u003c\/li\u003e\n\u003cli\u003eInspecting Data\u003c\/li\u003e\n\u003cli\u003eSelecting Columns and Rows\u003c\/li\u003e\n\u003cli\u003eThe head() and tail() methods\u003c\/li\u003e\n\u003cli\u003eBasic Plots\u003c\/li\u003e\n\u003cli\u003eDescriptive Statistics From a DataFrame\u003c\/li\u003e\n\u003cli\u003eFiltering, Sorting, and Grouping\u003c\/li\u003e\n\u003cli\u003eReplacing Values and Renaming Columns\u003c\/li\u003e\n\u003cli\u003eJoining and Combining Dataframes\u003c\/li\u003e\n\u003cli\u003eReading Data From Files\u003c\/li\u003e\n\u003cli\u003eReading From a Relational Database\u003c\/li\u003e\n\u003cli\u003eLoading External Data From NoSQL Stores (MongoDB)\u003c\/li\u003e\n\u003cli\u003eSciPy\u003c\/li\u003e\n\u003cli\u003eSci-Kit Learn\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003c\/div\u003e\u003cdiv\u003e\n\u003ch4\u003eModule 3: Visualization with Matplotlib\u003c\/h4\u003e\n\u003cp\u003eIn this module, you will learn about:\u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003eMatplotlib\u003c\/li\u003e\n\u003cli\u003eArchitecture\u003c\/li\u003e\n\u003cli\u003eThe Figure Object\u003c\/li\u003e\n\u003cli\u003eAxes, Labels, Titles, Legends and Grids\u003c\/li\u003e\n\u003cli\u003eReading Data from Files and Other DataSources\u003c\/li\u003e\n\u003cli\u003eThe pyplot \u003cabbr title=\"Application Programming Interface\"\u003eAPI\u003c\/abbr\u003e\n\u003c\/li\u003e\n\u003cli\u003eThe plot() Method\u003c\/li\u003e\n\u003cli\u003eThe Format String\u003c\/li\u003e\n\u003cli\u003eMarkers and Line Styles\u003c\/li\u003e\n\u003cli\u003ePlotting Labelled Data\u003c\/li\u003e\n\u003cli\u003ePlotting Multiple Graphs on the Same Axes\u003c\/li\u003e\n\u003cli\u003eSaving Figures\u003c\/li\u003e\n\u003cli\u003eLabels and Titles\u003c\/li\u003e\n\u003cli\u003eAnnotations\u003c\/li\u003e\n\u003cli\u003eLegends\u003c\/li\u003e\n\u003cli\u003eLine Chart\u003c\/li\u003e\n\u003cli\u003eArea Chart\u003c\/li\u003e\n\u003cli\u003eStacked Area Chart\u003c\/li\u003e\n\u003cli\u003eScatter Plot\u003c\/li\u003e\n\u003cli\u003eBubble Chart\u003c\/li\u003e\n\u003cli\u003eHeat Map\u003c\/li\u003e\n\u003cli\u003eContour Plot\u003c\/li\u003e\n\u003cli\u003eHistogram\u003c\/li\u003e\n\u003cli\u003eKernel Density Estimate Plot\u003c\/li\u003e\n\u003cli\u003eBox Plots\u003c\/li\u003e\n\u003cli\u003eViolin Plots\u003c\/li\u003e\n\u003cli\u003eBar Plot\u003c\/li\u003e\n\u003cli\u003eGrouped bar or column chart\u003c\/li\u003e\n\u003cli\u003eStacked Bar Plots\u003c\/li\u003e\n\u003cli\u003eError bars\u003c\/li\u003e\n\u003cli\u003eRadar Plots\u003c\/li\u003e\n\u003cli\u003ePie Plots and Donuts\u003c\/li\u003e\n\u003cli\u003eTree Maps\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003c\/div\u003e\u003cdiv\u003e\n\u003ch4\u003eModule 4: Simplifying Visualization with Seaborn\u003c\/h4\u003e\n\u003cp\u003eIn this module, you will learn about: \u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003eSeaborn\u003c\/li\u003e\n\u003cli\u003eStyling\u003c\/li\u003e\n\u003cli\u003eScaling and the Plotting Context\u003c\/li\u003e\n\u003cli\u003eOverriding Context Settings with the rc Parameter\u003c\/li\u003e\n\u003cli\u003eThemes\u003c\/li\u003e\n\u003cli\u003eColors in Seaborn\u003c\/li\u003e\n\u003cli\u003eVarying Hue to Distinguish Categories\u003c\/li\u003e\n\u003cli\u003eVary Luminance to Represent Numbers\u003c\/li\u003e\n\u003cli\u003eChoosing a Palette with the color_palette() Function\u003c\/li\u003e\n\u003cli\u003eQualitative Color Palettes\u003c\/li\u003e\n\u003cli\u003eSequential Palettes\u003c\/li\u003e\n\u003cli\u003eDiverging Palettes\u003c\/li\u003e\n\u003cli\u003eHistograms\u003c\/li\u003e\n\u003cli\u003eMultiple Histograms on the Same Axes\u003c\/li\u003e\n\u003cli\u003eKernel Density Plots\u003c\/li\u003e\n\u003cli\u003eBox Plots\u003c\/li\u003e\n\u003cli\u003eViolin Plots\u003c\/li\u003e\n\u003cli\u003eContour Plots\u003c\/li\u003e\n\u003cli\u003eThe FacetGrid\u003c\/li\u003e\n\u003cli\u003eSome Functions that Return a FacetGrid\u003c\/li\u003e\n\u003cli\u003ePair Plots\u003c\/li\u003e\n\u003cli\u003eThe relplot() Function\u003c\/li\u003e\n\u003cli\u003eThe regplot() and implot() Functions\u003c\/li\u003e\n\u003cli\u003eCreating a Regression Plot\u003c\/li\u003e\n\u003cli\u003eVariables That Take Discrete Values\u003c\/li\u003e\n\u003cli\u003eUsing a Representative value\u003c\/li\u003e\n\u003cli\u003eSquarify\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003c\/div\u003e\u003cdiv\u003e\n\u003ch4\u003eModule 5: Plotting geospatial data with Geoplotlib\u003c\/h4\u003e\n\u003cp\u003eIn this module, you will learn about:\u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003eGeoplotlib\u003c\/li\u003e\n\u003cli\u003eInput and Output\u003c\/li\u003e\n\u003cli\u003eInteraction\u003c\/li\u003e\n\u003cli\u003eThe dot Visualization\u003c\/li\u003e\n\u003cli\u003eZooming\u003c\/li\u003e\n\u003cli\u003e2D Histogram\u003c\/li\u003e\n\u003cli\u003eHeat Map\u003c\/li\u003e\n\u003cli\u003eVoronoi Tessellation\u003c\/li\u003e\n\u003cli\u003eSeed Points\u003c\/li\u003e\n\u003cli\u003eDelaunay Triangulation\u003c\/li\u003e\n\u003cli\u003e\u003cabbr title=\"Geographic JavaScript Object Notation\"\u003eGeoJSON\u003c\/abbr\u003e\u003c\/li\u003e\n\u003cli\u003eAdding Color and Tooltips\u003c\/li\u003e\n\u003cli\u003eTile Providers\u003c\/li\u003e\n\u003cli\u003eThe DarkMatter Tiles\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003c\/div\u003e\u003cdiv\u003e\n\u003ch4\u003eModule 6: Adding interaction with Bokeh\u003c\/h4\u003e\n\u003cp\u003eIn this module, you will learn about:\u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003eHow Bokeh Works\u003c\/li\u003e\n\u003cli\u003eBokeh Server\u003c\/li\u003e\n\u003cli\u003eProgramming Interfaces\u003c\/li\u003e\n\u003cli\u003eThe Bokeh Models\u003c\/li\u003e\n\u003cli\u003eGlyphs, Plots, and Layouts\u003c\/li\u003e\n\u003cli\u003eThe bokeh.plotting Interface\u003c\/li\u003e\n\u003cli\u003eSome Glyph Methods on the Figure Object\u003c\/li\u003e\n\u003cli\u003eWidgets in Bokeh\u003c\/li\u003e\n\u003cli\u003eUsing Bokeh Server\u003c\/li\u003e\n\u003cli\u003eSetting Up the Widgets\u003c\/li\u003e\n\u003cli\u003eThe TextField Widget\u003c\/li\u003e\n\u003cli\u003eThe Other Widgets\u003c\/li\u003e\n\u003cli\u003eRunning Bokeh Server\u003c\/li\u003e\n\u003cli\u003eWidgets Using CustomJS\u003c\/li\u003e\n\u003cli\u003eWidgets with ipwidgets\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003c\/div\u003e","brand":"Learning Tree","offers":[{"title":"268A53US \/ 2026-08-12T09:00:00 \/ Herndon, 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It uses the IEC\/ISO 38505 governance of data standards, written for organizations that want to develop and implement a data strategy. For example, this strategy may be part of a plan to deliver new services or products to their customers.\u003c\/p\u003e\u003c\/div\u003e\u003cdiv\u003e\n\u003ch3\u003eIntroduction to Data Governance and Assessing Data Maturity Benefits\u003c\/h3\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eIn this course, you will:\u003c\/strong\u003e\u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003eLearn the different Data Terminology: OLTP and OLAP, Dashboards, and Scorecards.\u003c\/li\u003e\n\u003cli\u003eUnderstand what data is and the history of data.\u003c\/li\u003e\n\u003cli\u003eLearn about the different data maturity levels.\u003c\/li\u003e\n\u003cli\u003eUnderstand how to make decisions with data, report on the data, and distribute the data.\u003c\/li\u003e\n\u003cli\u003eUnderstand when to dispose of data and why.\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eTraining Prerequisites\u003c\/strong\u003e\u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003eAttendees should have a basic understanding of what data is and the function of data analysis.\u003c\/li\u003e\n\u003cli\u003eYou can gain this from attending course Learning Tree course 1290, Introduction to Data Analytics.\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eCertification Information\u003c\/strong\u003e\u003c\/p\u003e\n\u003cp\u003eLearning Tree End of Course Exam included\u003c\/p\u003e\n\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003c\/div\u003e\u003cdiv\u003e\u003ch3\u003eData Governance and Maturity Training Outline\u003c\/h3\u003e\u003c\/div\u003e\u003cdiv\u003e\n\u003ch4\u003eChapter 1: A World of Data\u003c\/h4\u003e\n\u003cp\u003eWhat Is Data?\u003cbr\u003eHealth and Census Data\u003cbr\u003eTraditional and Modern Data-Heavy Industries\u003cbr\u003eData Quality Disasters\u003cbr\u003eHow Good and Bad Data Impacts Your Business\u003cbr\u003eWhy Poor-Quality Data Exists\u003cbr\u003eData and the Digital Revolution\u003cbr\u003eFive Key Drivers for Data Awareness\u003cbr\u003eCauses of Poor Data Quality\u003cbr\u003eComponents of a Data Solution\u003cbr\u003eData Products: Terminology\u003cbr\u003eData Products: OLTP and OLAP\u003cbr\u003eData Products: Dashboards and Scorecards\u003cbr\u003eData Products: Extract, Transform, Load, and Reporting Tools\u003cbr\u003e“Big” Data\u003cbr\u003eData and Digital\u003c\/p\u003e\n\u003c\/div\u003e\u003cdiv\u003e\n\u003ch4\u003eChapter 2: Introducing Data Governance \u0026amp; Data Management\u003c\/h4\u003e\n\u003cp\u003eData Governance Or Data Management?\u003cbr\u003eData Governance Standards\u003cbr\u003eOther Definitions of Data Governance\u003cbr\u003eData Governance Responsibilities\u003cbr\u003eData Management\u003cbr\u003eISO Standards In This Course\u003cbr\u003eISO\/DIS 37000 Wheel: The Centrality Of Governance\u003cbr\u003eISO\/DIS 37000*:\u003cbr\u003eGuidance for the Governance of Organizations\u003cbr\u003eISO\/IEC 38500:2015\u003cbr\u003eGovernance of IT for The Organization\u003cbr\u003eData Governance\u003cbr\u003eThe Definitions and Focus of Data Governance\u003cbr\u003eOther Definitions of Data Governance\u003cbr\u003eSix Use Cases for Data Governance\u003cbr\u003eData Governance: Areas of Focus\u003cbr\u003eWho Manages Data Governance?\u003c\/p\u003e\n\u003c\/div\u003e\u003cdiv\u003e\n\u003ch4\u003eChapter 3: Working with Data\u003c\/h4\u003e\n\u003cp\u003eTrust Issues\u003cbr\u003eImpact On Directors\u003cbr\u003eOpportunities For Data-Driven Business\u003cbr\u003eThe Risk of Unintended Outcomes\u003cbr\u003eRegulation Of Data\u003cbr\u003eMaturity Assessment: Creating the Vision\u003cbr\u003eData Maturity Levels\u003cbr\u003eLevel 1: Data Aware\u003cbr\u003eLevel 2: Data Capable\u003cbr\u003eLevel 3: Data Adept\u003cbr\u003eLevel 4: Data Informed\u003cbr\u003eLevel 5: Data Pioneer\u003cbr\u003eDiscussion Point: Driving Data Initiatives Forward\u003cbr\u003eWhat Are Business Drivers?\u003cbr\u003eTop-Down Data Analysis\u003cbr\u003eEnterprise Information Architecture\u003cbr\u003eDesigning Standardized Business Metadata\u003cbr\u003eBottom-Up Source Data Analysis\u003cbr\u003eSource Data Selection\u003cbr\u003eData Selection Issues\u003cbr\u003eExtract, Transform, and Load (ETL)\u003cbr\u003eExamples: Specifying ETL Rules\u003cbr\u003eData Technologies\u003cbr\u003eEnabling Technologies That Generate Data\u003cbr\u003eMoving Data to a Data Warehouse\u003cbr\u003eDeveloping the Data Warehouse\u003cbr\u003eDenormalization\u003cbr\u003eOLTP vs. OLAP\u003cbr\u003eOLAP Terminology\u003cbr\u003eValue of OLAP\u003cbr\u003eIdentifying Enterprise Systems and Data\u003cbr\u003eHow Is Enterprise Data Stored?\u003cbr\u003eUnstructured Data\u003cbr\u003eTechnical Infrastructure\u003cbr\u003eIssues: Structured and Unstructured Data\u003c\/p\u003e\n\u003c\/div\u003e\u003cdiv\u003e\n\u003ch4\u003eChapter 4: Principles \u0026amp; Components of Data Governance\u003c\/h4\u003e\n\u003cp\u003ePrinciples \u0026amp; Components of Data Governance\u003cbr\u003eThe Principles of Data Governance\u003cbr\u003eComponents of Data Governance\u003cbr\u003eThe Data Governance Paradox\u003cbr\u003eThe Data Governance Paradox: The Hidden Data Factory\u003cbr\u003eBarriers to Data Governance\u003cbr\u003eWhen Data Governance Fails\u003cbr\u003eSetting Governance Goals and Objectives\u003cbr\u003eCreating Your Data Governance Vision\u003c\/p\u003e\n\u003c\/div\u003e\u003cdiv\u003e\n\u003ch4\u003eChapter 5: Building a Data Strategy\u003c\/h4\u003e\n\u003cp\u003eYour Key Data Challenges\u003cbr\u003eData Management Metrics\u003cbr\u003eBusiness Culture\u003cbr\u003eEffective Business Communication\u003cbr\u003eSelling Data Governance\u003cbr\u003eBusiness Culture Change and Data Governance\u003cbr\u003eCreating a Cultural Change Plan\u003cbr\u003eCommunications Strategies for a Cultural Change Plan\u003cbr\u003eUsing a Motivation Model\u003cbr\u003eUsing a Motivation Model\u003cbr\u003eTools \u0026amp; Technology\u003cbr\u003eBuilding a Data Governance Business Case\u003cbr\u003eWhat Are Business Drivers?\u003cbr\u003eBuilding a Data Governance Business Case:\u003cbr\u003eCosts and Benefits\u003cbr\u003eBuilding a Data Governance Business Case:\u003cbr\u003eImplementation Plan\u003cbr\u003eFive Data Governance Models\u003cbr\u003ePros \u0026amp; Cons of Each Model\u003cbr\u003eUsing The Right Model for Your Organization\u003c\/p\u003e\n\u003c\/div\u003e\u003cdiv\u003e\n\u003ch4\u003eChapter 6: Building or Adopting a Data Governance Framework\u003c\/h4\u003e\n\u003cp\u003eData Governance Framework\u003cbr\u003eData Governance Framework Examples: DGI\u003cbr\u003eData Governance Framework Examples: McKinsey\u003cbr\u003eData Governance Framework: Overview\u003cbr\u003eThe Data Governance Framework: Vision\u003cbr\u003eThe Data Governance Framework: Strategy\u003cbr\u003eThe Data Governance Framework: Organization \u0026amp; People\u003cbr\u003eImplementing a Data Governance Framework\u003cbr\u003eWhat Does a Data Governance Framework Provide?\u003cbr\u003eThe Benefits of a Data Governance Framework\u003cbr\u003eDGF Deliverables \u0026amp; Actions Checklist\u003cbr\u003eData Governance Framework\u003c\/p\u003e\n\u003c\/div\u003e\u003cdiv\u003e\n\u003ch4\u003eChapter 7: Data Management Processes\u003c\/h4\u003e\n\u003cp\u003eThe Data Governance Roadmap\u003cbr\u003eData Governance Capability Mapping\u003cbr\u003eThe Data Governance Roadmap Checklist\u003cbr\u003eHints \u0026amp; Tips for Developing Roadmaps\u003cbr\u003eOrganizing Practical Data Management\u003cbr\u003eIdentifying and Designing Data Management Processes\u003cbr\u003eData Ownership \u0026amp; Stewardship\u003cbr\u003eWhat are Stakeholders?\u003cbr\u003eTypes of Requirements Errors\u003cbr\u003eWhy Do We Care?\u003cbr\u003eDefining Organizational Goals and Objectives\u003cbr\u003eIdentifying Your Key Data\u003cbr\u003eData Management and Measurement\u003cbr\u003eQuality Measures for Data\u003cbr\u003eBaselines and Improvement Targets\u003cbr\u003eData Governance \u0026amp; Management Toolset\u003cbr\u003eData Improvement Plans\u003cbr\u003eThe Role of IT in Data Governance \u0026amp; 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Students will learn about modern data warehousing, real-time analytics, and data visualization.\u003c\/p\u003e\u003c\/div\u003e\u003cdiv\u003e\n\u003ch3\u003eMicrosoft Azure Data Fundamentals Training (DP-900) Benefits\u003c\/h3\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eIn this Azure Data Fundamentals course, you will learn how to:\u003c\/strong\u003e\u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003eDescribe core data concepts.\u003c\/li\u003e\n\u003cli\u003eIdentify considerations for relational data on Azure.\u003c\/li\u003e\n\u003cli\u003eDescribe considerations for working with non-relational data on Azure.\u003c\/li\u003e\n\u003cli\u003eDescribe an analytics workload on Azure.\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eAzure Data Fundamentals Prerequisites\u003c\/strong\u003e\u003c\/p\u003e\n\u003cp\u003ePrerequisite certification is not required before taking this course. However, successful Azure Data Fundamentals students start with essential knowledge of computing and Internet concepts and an interest in extracting insights from data.\u003c\/p\u003e\n\u003cp\u003eSpecifically:\u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003eExperience using a web browser, such as Microsoft Edge.\u003c\/li\u003e\n\u003cli\u003eFamiliarity with basic data-related concepts, such as working with data tables in a spreadsheet and visualizing data using charts.\u003c\/li\u003e\n\u003cli\u003eA willingness to learn through hands-on exploration.\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eDP-900 Certification Information \u003c\/strong\u003e\u003c\/p\u003e\n\u003cp\u003eThis course can help you prepare for the following Microsoft role-based certification exam — \u003ca title=\"Exam DP-900: Microsoft Azure Data Fundamentals\" target=\"_blank\" rel=\"external nofollow noopener\" href=\"https:\/\/docs.microsoft.com\/en-us\/learn\/certifications\/exams\/dp-900\"\u003e Exam DP-900: Microsoft Azure Data Fundamentals\u003c\/a\u003e.\u003c\/p\u003e\n\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003c\/div\u003e\u003cdiv\u003e\u003ch3\u003eAzure Data Fundamentals Instructor-Led Course Outline\u003c\/h3\u003e\u003c\/div\u003e\u003cdiv\u003e\n\u003ch4\u003eModule 1: Explore core data concepts\u003c\/h4\u003e\n\u003cp\u003eData powers the digital transformation sweeping across organizations and society in general. But what is \"data,\" and how is it represented and used?\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eIn this module, you will learn how to:\u003c\/strong\u003e\u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003eIdentify common data formats\u003c\/li\u003e\n\u003cli\u003eDescribe options for storing data in files\u003c\/li\u003e\n\u003cli\u003eDescribe options for storing data in databases\u003c\/li\u003e\n\u003cli\u003eDescribe the characteristics of transactional data processing solutions\u003c\/li\u003e\n\u003cli\u003eDescribe the characteristics of analytical data processing solutions\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003c\/div\u003e\u003cdiv\u003e\n\u003ch4\u003eModule 2: Explore data roles and services\u003c\/h4\u003e\n\u003cp\u003eData professionals perform distinct roles in building and managing software solutions and work with multiple technologies and services.\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eIn this module, you will learn how to:\u003c\/strong\u003e\u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003eIdentify common data professional roles\u003c\/li\u003e\n\u003cli\u003eIdentify standard cloud services used by data professionals\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003c\/div\u003e\u003cdiv\u003e\n\u003ch4\u003eModule 3: Explore fundamental relational data concepts\u003c\/h4\u003e\n\u003cp\u003eRelational database systems are a common way to store and manage transactional and analytical data in organizations of any size worldwide.\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eIn this module, you'll learn how to:\u003c\/strong\u003e\u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003eIdentify characteristics of relational data\u003c\/li\u003e\n\u003cli\u003eDefine normalization\u003c\/li\u003e\n\u003cli\u003eIdentify types of SQL statement\u003c\/li\u003e\n\u003cli\u003eIdentify everyday relational database objects\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003c\/div\u003e\u003cdiv\u003e\n\u003ch4\u003eModule 4: Explore relational database services in Azure\u003c\/h4\u003e\n\u003cp\u003eMicrosoft Azure provides multiple services for relational databases. You can choose the best relational database management system and host relational data in the cloud.\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eIn this module, you'll learn how to:\u003c\/strong\u003e\u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003eIdentify options for Azure SQL services\u003c\/li\u003e\n\u003cli\u003eIdentify options for open-source databases in Azure\u003c\/li\u003e\n\u003cli\u003eProvision a database service on Azure\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003c\/div\u003e\u003cdiv\u003e\n\u003ch4\u003eModule 5: Explore Azure Storage for non-relational data\u003c\/h4\u003e\n\u003cp\u003eAzure Storage is a core Microsoft Azure service commonly used to store non-relational data.\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eIn this module, you'll learn how to:\u003c\/strong\u003e\u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003eDescribe features and capabilities of Azure blob storage\u003c\/li\u003e\n\u003cli\u003eDescribe the features and capabilities of Azure Data Lake Gen2\u003c\/li\u003e\n\u003cli\u003eDescribe the features and capabilities of Azure file storage\u003c\/li\u003e\n\u003cli\u003eDescribe features and capabilities of Azure table storage\u003c\/li\u003e\n\u003cli\u003eProvision and use an Azure Storage account\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003c\/div\u003e\u003cdiv\u003e\n\u003ch4\u003eModule 6: Explore fundamentals of Azure Cosmos DB\u003c\/h4\u003e\n\u003cp\u003eAzure Cosmos DB provides a highly scalable store for non-relational data.\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eIn this module, you'll learn how to:\u003c\/strong\u003e\u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003eDescribe key features and capabilities of Azure Cosmos DB\u003c\/li\u003e\n\u003cli\u003eIdentify the APIs supported in Azure Cosmos DB\u003c\/li\u003e\n\u003cli\u003eProvision and use an Azure Cosmos DB instance\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003c\/div\u003e\u003cdiv\u003e\n\u003ch4\u003eModule 7: Explore fundamentals of large-scale data warehousing\u003c\/h4\u003e\n\u003cp\u003eOrganizations use modern data warehousing to build large-scale data analytics solutions that generate insights and drive success. 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Companies across industries are increasingly relying on data to inform their decision-making, and skilled data scientists are in high demand. In this comprehensive course, you'll learn the foundational skills and techniques you need to succeed in this exciting field.\u003c\/p\u003e\r\n\u003cp\u003eYou'll start by exploring the role of a data scientist and the lifecycle of data science efforts within an organization. Then, you'll dive into the technical skills you need, such as using Python and its relevant libraries for data analysis and visualization, preprocessing unstructured data, and building AI\/ML models.\u003c\/p\u003e\r\n\u003cp\u003eYou'll also explore key machine learning algorithms, including linear regression, decision tree classifiers, and clustering algorithms. And, you'll learn how to apply these techniques to real-world problems, such as predicting customer churn and building recommendation engines.\u003c\/p\u003e\r\n\u003cp\u003eThroughout the \u003cstrong\u003edata science training\u003c\/strong\u003e, you'll have the opportunity to work on hands-on exercises and projects, allowing you to practice your skills and build your portfolio. By the end of the course, you'll have a deep understanding of the data science process, the tools and techniques used by data scientists, and the ability to apply these skills to real-world problems.\u003c\/p\u003e\n\u003c\/div\u003e\u003cdiv\u003e\n\u003ch3\u003eIntroduction to AI, Data Science \u0026amp; Machine Learning with Python Benefits\u003c\/h3\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eIn this course, you will:\u003c\/strong\u003e\u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003eConsider how traditional Predictive Machine Learning techniques combine Generative AI, Agentic AI, Multimodal AI and Deepseek-R1 approaches in the current Data Science landscape.\u003c\/li\u003e\n\u003cli\u003eTranslate everyday business questions and problems into Machine Learning tasks to make data-driven decisions.\u003c\/li\u003e\n\u003cli\u003eUse Python Pandas, Matplotlib \u0026amp; Seaborn libraries to explore, analyze, and visualize data from various sources, including the web, word documents, email, NoSQL stores, databases, and data warehouses.\u003c\/li\u003e\n\u003cli\u003e\u003cspan xml:lang=\"EN-US\" data-contrast=\"auto\"\u003eExplore the concepts behind Foundation Models, Generative Pre-trained Transformers (GPTs), and Retrieval Augmented Generation (RAGs).\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003eTrain a Machine Learning Classifier using different algorithmic techniques from the Scikit-Learn library, such as Decision Trees, Logistic Regression, and Neural Networks.\u003c\/li\u003e\n\u003cli\u003eRe-segment your customer market using K-Means and Hierarchical algorithms to better align products and services to customer needs.\u003c\/li\u003e\n\u003cli\u003eDiscover hidden customer behaviors from Association Rules and build a Recommendation Engine based on behavioral patterns.\u003c\/li\u003e\n\u003cli\u003eInvestigate relationships \u0026amp; flows between people and business-relevant entities using Social Network Analysis.\u003c\/li\u003e\n\u003cli\u003eBuild predictive models of revenue and other numeric variables using Linear Regression.\u003c\/li\u003e\n\u003cli\u003eTest your knowledge with the included end-of-course exam.\u003c\/li\u003e\n\u003cli\u003eLeverage continued support with after-course one-on-one instructor coaching and computing sandbox.\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eTraining Prerequisites\u003c\/strong\u003e\u003c\/p\u003e\n\u003cp\u003eNone.\u003c\/p\u003e\n\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003c\/div\u003e\u003cdiv\u003e\u003ch3\u003eData Science Training in Python Course Outline\u003c\/h3\u003e\u003c\/div\u003e\u003cdiv\u003e\n\u003ch4\u003eModule 1: The Role of a Data Scientist: Combining Technical and Non-Technical Skills\u003c\/h4\u003e\n\u003cul\u003e\n\u003cli\u003eWhat is the required skillset of a Data Scientist?\u003c\/li\u003e\n\u003cli\u003eCombining the technical and non-technical roles of a Data Scientist\u003c\/li\u003e\n\u003cli\u003eThe difference between a Data Scientist and a Data Engineer\u003c\/li\u003e\n\u003cli\u003eExploring the entire lifecycle of Data Science efforts within the organization\u003c\/li\u003e\n\u003cli\u003eTurning business questions into Machine Learning (ML) and Artificial Intelligence (AI) models\u003c\/li\u003e\n\u003cli\u003eExploring diverse and wide-ranging data sources that you can use to answer business questions\u003c\/li\u003e\n\u003cli\u003e\n\u003cspan xml:lang=\"EN-US\" data-contrast=\"auto\"\u003eExplore the concepts behind Foundation Models, Generative Pre-trained Transformers (GPTs), and Retrieval Augmented Generation (RAGs)\u003c\/span\u003e\u003cspan data-ccp-props=\"{}\"\u003e \u003c\/span\u003e\n\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003c\/div\u003e\u003cdiv\u003e\n\u003ch4\u003eModule 2: Data Manipulation and Visualization using Python's Pandas and Matplotlib Libraries\u003c\/h4\u003e\n\u003cul\u003e\n\u003cli\u003eIntroducing the features of Python that are relevant to Data Scientists and Data Engineers\u003c\/li\u003e\n\u003cli\u003eViewing Data Sets using Python’s Pandas library\u003c\/li\u003e\n\u003cli\u003eImporting, exporting, and working with all forms of data, from Relational Databases to Google Images\u003c\/li\u003e\n\u003cli\u003eUsing Python Selecting, Filtering, Combining, Grouping, and Applying Functions from Python's Pandas library\u003c\/li\u003e\n\u003cli\u003eDealing with Duplicates, Missing Values, Rescaling, Standardizing, and Normalizing Data\u003c\/li\u003e\n\u003cli\u003eVisualizing data for both exploration and communication with the Pandas, Matplotlib, and Seaborn Python libraries\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003c\/div\u003e\u003cdiv\u003e\n\u003ch4\u003eModule 3: Preprocessing and Analyzing Unstructured Data with Natural Language Processing\u003c\/h4\u003e\n\u003cul\u003e\n\u003cli\u003ePreprocessing Unstructured Data such as web adverts, emails, and blog posts for AI\/ML models\u003c\/li\u003e\n\u003cli\u003eExploring the most popular approaches to Natural Language Processing (NLP), such as stemming and \"stop\" words\u003c\/li\u003e\n\u003cli\u003ePreparing a term-document matrix (TDM) of unstructured documents for analysis\u003c\/li\u003e\n\u003cli\u003e\u003cspan xml:lang=\"EN-US\" data-contrast=\"auto\"\u003eReview the architectures of Foundation Models, Generative Pre-trained Transformers (GPTs), and Retrieval Augmented Generation (RAGs)\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003eLook at how Data Scientists can integrate Large Language Models (LLMs) in their work\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003c\/div\u003e\u003cdiv\u003e\n\u003ch4\u003eModule 4: Linear Regression and Feature Engineering for Business Problem Solving\u003c\/h4\u003e\n\u003cul\u003e\n\u003cli\u003eExpressing a business problem, such as customer revenue prediction, as a linear regression task\u003c\/li\u003e\n\u003cli\u003eAssessing variables as potential Predictors of the required Target (e.g., Education as a predictor of Salary Build)\u003c\/li\u003e\n\u003cli\u003eInterpreting and Evaluating a Linear Regression model in Python using measures such as RMSE\u003c\/li\u003e\n\u003cli\u003eExploring the Feature Engineering possibilities to improve the Linear Regression model\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003c\/div\u003e\u003cdiv\u003e\n\u003ch4\u003eModule 5: Classification Models and Evaluation for Predictive Analysis\u003c\/h4\u003e\n\u003cul\u003e\n\u003cli\u003eLearning how AI\/ML Classifiers are built and used to make predictions such as Customer Churn\u003c\/li\u003e\n\u003cli\u003eExploring how AI\/ML Classification models are built using Training, Test, and Validation\u003c\/li\u003e\n\u003cli\u003eEvaluating the strength of a Decision Tree Classifier\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003c\/div\u003e\u003cdiv\u003e\n\u003ch4\u003eModule 6: Alternative Approaches to Classification and Model Evaluation\u003c\/h4\u003e\n\u003cul\u003e\n\u003cli\u003eExamining alternative approaches to classification\u003c\/li\u003e\n\u003cli\u003eConsidering how Activation Functions are integral to Logistic Regression Classifiers\u003c\/li\u003e\n\u003cli\u003e\n\u003cspan xml:lang=\"EN-US\" data-contrast=\"auto\"\u003eDelve into the architecture of Neural Networks and investigate the explosive growth of Deep Learning approaches in AI\u003c\/span\u003e\u003cspan data-ccp-props=\"{}\"\u003e \u003c\/span\u003e\n\u003c\/li\u003e\n\u003cli\u003eExploring the probability foundations of Naive Bayes classifiers\u003c\/li\u003e\n\u003cli\u003eReviewing different approaches to measuring the performance of AI\/ML Classification Models\u003c\/li\u003e\n\u003cli\u003eReviewing ROC curves, AUC measures, Precision, Recall, and Confusion Matrices\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003c\/div\u003e\u003cdiv\u003e\n\u003ch4\u003eModule 7: Clustering Techniques for Customer and Product Segmentation\u003c\/h4\u003e\n\u003cul\u003e\n\u003cli\u003eUncovering new ways of segmenting your customers, products, or services using clustering algorithms\u003c\/li\u003e\n\u003cli\u003eExploring what the concept of similarity means to humans and how you can implement it programmatically through distance measures on descriptive variables\u003c\/li\u003e\n\u003cli\u003ePerforming top-down clustering with Python’s Scikit-Learn K-Means algorithm\u003c\/li\u003e\n\u003cli\u003ePerforming bottom-up clustering with Scikit-Learn’s hierarchical clustering algorithm\u003c\/li\u003e\n\u003cli\u003eExamining clustering techniques on unstructured data (e.g., Tweets, Emails, Documents, etc.)\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003c\/div\u003e\u003cdiv\u003e\n\u003ch4\u003eModule 8: Association Rules and Recommender Systems for Business Applications\u003c\/h4\u003e\n\u003cul\u003e\n\u003cli\u003eBuilding models of customer behaviors or business events from logged data 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experience in configuring secure access to pipeline resources.\u003c\/li\u003e\n\u003cli\u003eUnderstand permissions configuration and validation.\u003c\/li\u003e\n\u003cli\u003eLearn how to extend pipelines using multiple templates for enhanced functionality.\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cb\u003eTraining Prerequisites\u003c\/b\u003e\u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003eBasic knowledge of Azure DevOps CI\/CD tools, including Azure Pipelines.\u003c\/li\u003e\n\u003cli\u003eBasic knowledge of Azure Portal to create resources.\u003c\/li\u003e\n\u003cli\u003eBasic knowledge of security concepts like identities and permissions.\u003c\/li\u003e\n\u003cli\u003eExperience using the Azure portal to create resources like Azure Key Vault and set permissions.\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003c\/div\u003e\u003cdiv\u003e\u003ch3\u003eAzure DevOps Pipeline Security Training 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permissions.\u003c\/li\u003e\n\u003cli\u003eConfigure and validate pipeline permissions.\u003c\/li\u003e\n\u003cli\u003eConfigure and validate approval and branch checks.\u003c\/li\u003e\n\u003cli\u003eManage and audit permissions in Azure DevOps.\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cp\u003e\u003cb\u003eExtend a pipeline to use multiple templates\u003c\/b\u003e\u003c\/p\u003e\n\u003cul type=\"disc\"\u003e\n\u003cli\u003eCreate nested templates.\u003c\/li\u003e\n\u003cli\u003eRewrite the main deployment pipeline.\u003c\/li\u003e\n\u003cli\u003eConfigure the pipeline and the application to use tokenization.\u003c\/li\u003e\n\u003cli\u003eRemove plain text secrets.\u003c\/li\u003e\n\u003cli\u003eRestrict agent logging.\u003c\/li\u003e\n\u003cli\u003eIdentify and conditionally remove script tasks in Azure DevOps.\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003c\/div\u003e","brand":"Microsoft","offers":[{"title":"267A68US \/ 2026-07-15T09:00:00 \/ 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(DP-600)","description":"\u003cdiv\u003e\u003cp\u003eThis Microsoft Fabric Analytics Engineer (DP-600) course covers methods and practices for implementing and managing enterprise-scale data analytics solutions using Microsoft Fabric. Students will build on existing analytics experience and will learn how to use Microsoft Fabric components, including lakehouses, data warehouses, notebooks, dataflows, data pipelines, and semantic models, to create and deploy analytics assets.\u003c\/p\u003e\u003c\/div\u003e\u003cdiv\u003e\n\u003ch3\u003eMicrosoft Fabric Analytics Engineer (DP-600) Benefits\u003c\/h3\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cb\u003eIn this course, you will learn how to:\u003c\/b\u003e\u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003ePlan, implement, and manage a solution for data analytics. \u003c\/li\u003e\n\u003cli\u003ePrepare and serve data. \u003c\/li\u003e\n\u003cli\u003eImplement and manage semantic models. \u003c\/li\u003e\n\u003cli\u003eExplore and analyze data. \u003c\/li\u003e\n\u003cli\u003ePrepare for the Implementing Analytics Solutions Using Microsoft Fabric certification exam. \u003c\/li\u003e\n\u003c\/ul\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cb\u003eTraining Prerequisites\u003c\/b\u003e\u003c\/p\u003e\n\u003cp\u003eWhile there are no required prerequisites for taking this course, it is recommended that students have: \u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003eA foundational knowledge of core data concepts and how they’re implemented using Microsoft data services (see: Introduction to AI in Azure (AI-901)\u003ca target=\"_blank\" href=\"https:\/\/www.learningtree.com\/courses\/microsoft-azure-ai-fundamentals-training-ai-900\/\" rel=\"noopener\"\u003e)\u003c\/a\u003e \u003c\/li\u003e\n\u003cli\u003eExperience designing and building scalable data models, cleaning and transforming data, and enabling advanced analytic capabilities that provide meaningful business value using Microsoft Power BI (see: Design and Manage Analytics Solutions Using Power BI (PL-300)) \u003c\/li\u003e\n\u003c\/ul\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cb\u003eCertification Information\u003c\/b\u003e\u003c\/p\u003e\n\u003cp\u003eThis course maps directly to and is intended to prepare students for the \u003ca target=\"_blank\" href=\"https:\/\/learn.microsoft.com\/en-us\/credentials\/certifications\/exams\/dp-600\/\" rel=\"nofollow noopener\" title=\"DP-600 | Microsoft Learn\"\u003eDP-600: Implementing Analytics Solutions Using Microsoft Fabric exam\u003c\/a\u003e \u003c\/p\u003e\n\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003c\/div\u003e\u003cdiv\u003e\u003ch3\u003eMicrosoft Fabric Analytics Engineer (DP-600) Training Outline\u003c\/h3\u003e\u003c\/div\u003e\u003cdiv\u003e\n\u003ch4\u003eCourse Introduction \u003c\/h4\u003e\n\u003cul\u003e\n\u003cli\u003eExplore end-to-end analytics with Microsoft Fabric \u003c\/li\u003e\n\u003cli\u003eAdminister Microsoft Fabric \u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cul\u003e\u003c\/ul\u003e\n\u003c\/div\u003e\u003cdiv\u003e\n\u003ch4\u003eData Ingestion \u003c\/h4\u003e\n\u003cul\u003e\n\u003cli\u003eIngest data with Dataflows (Gen2) in Microsoft Fabric \u003c\/li\u003e\n\u003cli\u003eIngest data Spark and Microsoft Fabric notebooks \u003c\/li\u003e\n\u003cli\u003eUse Data Factory pipelines in Microsoft Fabric \u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cul\u003e\u003c\/ul\u003e\n\u003c\/div\u003e\u003cdiv\u003e\n\u003ch4\u003eLakehouses in Microsoft Fabric \u003c\/h4\u003e\n\u003cul\u003e\n\u003cli\u003eGet started with lakehouses in Microsoft Fabric \u003c\/li\u003e\n\u003cli\u003eOrganize a Fabric lakehouse using medallion architecture \u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cul\u003e\u003c\/ul\u003e\n\u003c\/div\u003e\u003cdiv\u003e\n\u003ch4\u003eApache Spark in Microsoft Fabric \u003c\/h4\u003e\n\u003cul\u003e\n\u003cli\u003eUse Apache Spark in Microsoft Fabric \u003c\/li\u003e\n\u003cli\u003eWork with Delta Lake tables in Microsoft Fabric \u003c\/li\u003e\n\u003cli\u003eSecure your Fabric lakehouse \u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cul\u003e\u003c\/ul\u003e\n\u003c\/div\u003e\u003cdiv\u003e\n\u003ch4\u003eData Warehouses in Microsoft Fabric \u003c\/h4\u003e\n\u003cul\u003e\n\u003cli\u003eGet started with data warehouses in Microsoft Fabric \u003c\/li\u003e\n\u003cli\u003eLoad data into a Microsoft Fabric data warehouse \u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cul\u003e\u003c\/ul\u003e\n\u003c\/div\u003e\u003cdiv\u003e\n\u003ch4\u003eQuery and Optimization \u003c\/h4\u003e\n\u003cul\u003e\n\u003cli\u003eQuery a data warehouse in Microsoft Fabric \u003c\/li\u003e\n\u003cli\u003eMonitor and optimize a Microsoft Fabric data warehouse \u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cul\u003e\u003c\/ul\u003e\n\u003c\/div\u003e\u003cdiv\u003e\n\u003ch4\u003ePower BI Scalability \u003c\/h4\u003e\n\u003cul\u003e\n\u003cli\u003eUnderstand 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You’ll explore everything from data cleaning and machine learning to web scraping and interactive visualization—turning raw data into smart insights with real-world relevance.\u003c\/p\u003e\u003c\/div\u003e\u003cdiv\u003e\n\u003ch3\u003eBeginner Data Science with Python and Jupyter Benefits\u003c\/h3\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eIn this course, you will:\u003c\/strong\u003e\u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003eLearn by doing: practice with real datasets and coding exercises.\u003c\/li\u003e\n\u003cli\u003eGain fluency in Jupyter Notebooks, Python libraries, and machine learning tools.\u003c\/li\u003e\n\u003cli\u003eBuild models to solve classification problems and interpret results.\u003c\/li\u003e\n\u003cli\u003eScrape data from the web and visualize it like a pro.\u003c\/li\u003e\n\u003cli\u003eWalk away with a toolkit to tackle data-driven problems in any domain.\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003ePrerequisites\u003c\/strong\u003e\u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003eKnowledge of programming fundamentals and some experience with Python, including Python libraries, Pandas, Matplotlib, and scikit-learn.\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003c\/div\u003e\u003cdiv\u003e\u003ch3\u003eBeginner Data Science with Python and Jupyter Training Outline\u003c\/h3\u003e\u003c\/div\u003e\u003cdiv\u003e\n\u003ch4\u003eLearning Objectives\u003c\/h4\u003e\n\u003cp\u003e\u003cb\u003eLesson 1: Jupyter Fundamentals\u003c\/b\u003e\u003c\/p\u003e\n\u003cul type=\"disc\"\u003e\n\u003cli\u003eIntroduction to Jupyter Notebooks\u003c\/li\u003e\n\u003cli\u003eData exploration using the Boston Housing Dataset\u003c\/li\u003e\n\u003cli\u003eFirst steps in predictive analytics\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cp\u003e\u003cb\u003eLesson 2: Data Cleaning \u0026amp; Machine Learning\u003c\/b\u003e\u003c\/p\u003e\n\u003cul type=\"disc\"\u003e\n\u003cli\u003eData preprocessing strategies\u003c\/li\u003e\n\u003cli\u003eClassification models: SVM, KNN, Random Forests\u003c\/li\u003e\n\u003cli\u003eModel evaluation and dimensionality reduction (PCA)\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cp\u003e\u003cb\u003eLesson 3: Web Scraping \u0026amp; Interactive Visualizations\u003c\/b\u003e\u003c\/p\u003e\n\u003cul type=\"disc\"\u003e\n\u003cli\u003eHTTP requests \u0026amp; HTML parsing with Python\u003c\/li\u003e\n\u003cli\u003eData scraping with Beautiful Soup\u003c\/li\u003e\n\u003cli\u003eBuilding interactive plots with Bokeh\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cp\u003e\u003cb\u003eLesson 4: Applied Projects\u003c\/b\u003e\u003c\/p\u003e\n\u003cul type=\"disc\"\u003e\n\u003cli\u003eInvestigating datasets through EDA\u003c\/li\u003e\n\u003cli\u003eTraining and tuning classification models\u003c\/li\u003e\n\u003cli\u003eCreating impactful, interactive visualizations\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003c\/div\u003e","brand":"Learning Tree","offers":[{"title":"267A61US \/ 2026-07-20T09:00:00 \/ Online","offer_id":47534205731035,"sku":"US-1263-IL","price":716.0,"currency_code":"USD","in_stock":true},{"title":"269C25US \/ 2026-09-15T09:00:00 \/ Online","offer_id":48216584061147,"sku":"US-1263-IL","price":716.0,"currency_code":"USD","in_stock":true},{"title":"26BA67US \/ 2026-11-09T09:00:00 \/ Online","offer_id":48216584093915,"sku":"US-1263-IL","price":716.0,"currency_code":"USD","in_stock":true},{"title":"271A79US \/ 2027-01-19T09:00:00 \/ Online","offer_id":48216584126683,"sku":"US-1263-IL","price":716.0,"currency_code":"USD","in_stock":true},{"title":"273A63US \/ 2027-03-16T09:00:00 \/ Online","offer_id":48377298944219,"sku":"US-1263-IL","price":716.0,"currency_code":"USD","in_stock":true},{"title":"275A58US \/ 2027-05-17T09:00:00 \/ Online","offer_id":48778590290139,"sku":"US-1263-IL","price":716.0,"currency_code":"USD","in_stock":true}]},{"product_id":"python-data-wrangling-training","title":"Python Data Wrangling Training","description":"\u003cdiv\u003e\n\u003cp\u003eIn this \u003cstrong\u003ePython Data Wrangling\u003c\/strong\u003e course, you will learn how to use Python to extract\/transform data from various sources, including large database vaults and Excel financial tables.\u003c\/p\u003e\r\n\u003cp\u003eYou will also explore insights into why you should avoid traditional data cleaning methods, as done in other languages, and take advantage of the specialized functions from \u003ca href=\"https:\/\/numpy.org\/\" title=\"NumPy\" target=\"_blank\" rel=\"external nofollow noopener\"\u003eNumPy\u003c\/a\u003e and Pandas.\u003c\/p\u003e\n\u003c\/div\u003e\u003cdiv\u003e\n\u003ch3\u003ePython Data Wrangling Training Benefits\u003c\/h3\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eIn this Python Wrangling course, you will learn how to do the following:\u003c\/strong\u003e\u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003cspan\u003eExtract and parse data from various sources.\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003eTransform and clean data using Numpy and Pandas.\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003eSummarize and visualize data with\u003cspan\u003e \u003c\/span\u003e\u003ca href=\"https:\/\/matplotlib.org\/\" title=\"Matplotlib | Visualization with Python\" target=\"_blank\" rel=\"external nofollow noopener\"\u003eMatplotlib.\u003c\/a\u003e\n\u003c\/li\u003e\n\u003cli\u003eRead\u003cspan\u003e \u003c\/span\u003e\u003cabbr title=\"HyperText Markup Language\"\u003eHTML\u003c\/abbr\u003e,\u003cspan\u003e \u003c\/span\u003e\u003cabbr title=\"eXtensible Markup Language\"\u003eXML\u003c\/abbr\u003e, and\u003cspan\u003e \u003c\/span\u003e\u003cabbr title=\"JavaScript Object Notation\"\u003eJSON\u003c\/abbr\u003e\u003cspan\u003e \u003c\/span\u003edata from internet resources.\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003eSearch and filter data sets.\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003eApply Python tools and techniques to process data sets efficiently.\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003eContinue learning and face new challenges with after-course one-on-one instructor coaching.\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003ePrerequisites:\u003c\/strong\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003eYou should know Python basics, including data structures, importing and using modules, creating functions, and using the Jupyter Notebook platform.\u003c\/span\u003e\u003c\/p\u003e\n\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003c\/div\u003e\u003cdiv\u003e\u003ch3\u003ePython Data Wrangling Training Outline\u003c\/h3\u003e\u003c\/div\u003e\u003cdiv\u003e\n\u003ch4\u003eModule 1: Introduction to Data Structure Using Python\u003c\/h4\u003e\n\u003cp\u003eIn this module, you will learn about the following:\u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003ePython for Data Wrangling\u003c\/li\u003e\n\u003cli\u003eLists, Sets, Strings, Tuples, and Dictionaries\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003c\/div\u003e\u003cdiv\u003e\n\u003ch4\u003eModule 2: Advanced Operations on Built-In Data Structure\u003c\/h4\u003e\n\u003cp\u003eIn this module, you will learn about the following:\u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003eAdvanced Data Structures\u003c\/li\u003e\n\u003cli\u003eBasic File Operations in Python\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003c\/div\u003e\u003cdiv\u003e\n\u003ch4\u003eModule 3: Introduction to NumPy, Pandas, and Matplotlib\u003c\/h4\u003e\n\u003cp\u003eIn this module, you will learn about the following: \u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003eNumPy Arrays \u003c\/li\u003e\n\u003cli\u003ePandas DataFrames \u003c\/li\u003e\n\u003cli\u003eStatistics and Visualization with NumPy and Pandas \u003c\/li\u003e\n\u003cli\u003eUsing NumPy and Pandas to Calculate Basic Descriptive Statistics on the DataFrame \u003c\/li\u003e\n\u003c\/ul\u003e\n\u003c\/div\u003e\u003cdiv\u003e\n\u003ch4\u003eModule 4: Deep Dive into Data Wrangling with Python\u003c\/h4\u003e\n\u003cp\u003eIn this module, you will learn about the following: \u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003eSubsetting, Filtering, and Grouping \u003c\/li\u003e\n\u003cli\u003eDetecting Outliers and Handling Missing Values \u003c\/li\u003e\n\u003cli\u003eConcatenating, Merging, and Joining \u003c\/li\u003e\n\u003cli\u003eUseful Methods of Pandas \u003c\/li\u003e\n\u003c\/ul\u003e\n\u003c\/div\u003e\u003cdiv\u003e\n\u003ch4\u003eModule 5: Getting Comfortable with Different Data Sources\u003c\/h4\u003e\n\u003cp\u003eIn this module, you will learn about the following: \u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003eReading Data from Different Text-Based (and Non-Text-Based) Sources \u003c\/li\u003e\n\u003cli\u003eIntroduction to BeautifulSoup4 and Web Page Parsing \u003c\/li\u003e\n\u003c\/ul\u003e\n\u003c\/div\u003e\u003cdiv\u003e\n\u003ch4\u003eModule 6: Learning the Hidden Secrets of Data Wrangling\u003c\/h4\u003e\n\u003cp\u003eIn this module, you will learn about the following: \u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003eAdvanced List Comprehension and the zip function \u003c\/li\u003e\n\u003cli\u003eData Formatting \u003c\/li\u003e\n\u003c\/ul\u003e\n\u003c\/div\u003e\u003cdiv\u003e\n\u003ch4\u003eModule 7: Advanced Web Scraping and Data Gathering\u003c\/h4\u003e\n\u003cp\u003eIn this module, you will learn about the following: \u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003eBasics of Web Scraping and BeautifulSoup libraries \u003c\/li\u003e\n\u003cli\u003eReading Data from XML \u003c\/li\u003e\n\u003c\/ul\u003e\n\u003c\/div\u003e\u003cdiv\u003e\n\u003ch4\u003eModule 8: RDBMS and SQL\u003c\/h4\u003e\n\u003cp\u003eIn this module, you will learn about the following:\u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003eRefresher of \u003cabbr title=\"Relational DataBase Management System\"\u003eRDBMS\u003c\/abbr\u003e and \u003cabbr title=\"Structured Query Language\"\u003eSQL\u003c\/abbr\u003e\n\u003c\/li\u003e\n\u003cli\u003eUsing an RDBMS (MySQL\/PostgreSQL\/SQLite)\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003c\/div\u003e\u003cdiv\u003e\n\u003ch4\u003eModule 9: Application in Real Life and Conclusion of Course\u003c\/h4\u003e\n\u003cp\u003eIn this module, you will learn about the following:\u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003eApplying Your Knowledge to a Real-life Data Wrangling Task\u003c\/li\u003e\n\u003cli\u003eAn Extension to Data Wrangling\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003c\/div\u003e","brand":"Learning Tree","offers":[{"title":"266A03CN \/ 2026-06-24T09:00:00 \/ Online","offer_id":47534199963867,"sku":"US-1273-IL","price":2228.0,"currency_code":"USD","in_stock":true},{"title":"267A78US \/ 2026-07-29T09:00:00 \/ Herndon, VA","offer_id":47534199996635,"sku":"US-1273-IL","price":2228.0,"currency_code":"USD","in_stock":true},{"title":"268C77US \/ 2026-08-26T09:00:00 \/ New York","offer_id":48216565973211,"sku":"US-1273-IL","price":2228.0,"currency_code":"USD","in_stock":true},{"title":"269C31US \/ 2026-09-16T09:00:00 \/ Austin","offer_id":48216566005979,"sku":"US-1273-IL","price":2228.0,"currency_code":"USD","in_stock":true},{"title":"26BA18CN \/ 2026-11-04T09:00:00 \/ Ottawa","offer_id":48216566038747,"sku":"US-1273-IL","price":2228.0,"currency_code":"USD","in_stock":true},{"title":"26CA75US \/ 2026-12-16T09:00:00 \/ Herndon, VA","offer_id":48216566071515,"sku":"US-1273-IL","price":2228.0,"currency_code":"USD","in_stock":true},{"title":"271B01US \/ 2027-01-20T09:00:00 \/ New York","offer_id":48216566137051,"sku":"US-1273-IL","price":2228.0,"currency_code":"USD","in_stock":true},{"title":"272A62US \/ 2027-02-10T09:00:00 \/ Austin","offer_id":48236925059291,"sku":"US-1273-IL","price":2228.0,"currency_code":"USD","in_stock":true},{"title":"273A15CN \/ 2027-03-31T09:00:00 \/ Ottawa","offer_id":48525735493851,"sku":"US-1273-IL","price":2228.0,"currency_code":"USD","in_stock":true},{"title":"275A86US \/ 2027-05-12T09:00:00 \/ Herndon, VA","offer_id":48762892583131,"sku":"US-1273-IL","price":2228.0,"currency_code":"USD","in_stock":true}]},{"product_id":"comptia-data-®-certification-training","title":"CompTIA Data+® Certification Training","description":"\u003cdiv\u003e\n\u003cp\u003eUnlock the power of data-driven decision-making with the \u003cstrong\u003eCompTIA Data+\u003c\/strong\u003e Certification Training! As the importance of data analytics continues to grow, more job roles are being created to set a context and better communicate vital business intelligence. This comprehensive course will teach you the skills required to facilitate data-driven business decisions, including mining, manipulating, visualizing, and reporting data. \u003c\/p\u003e\r\n\u003cp\u003eLearn to apply basic statistical methods and analyze complex datasets while adhering to governance and quality standards throughout the entire data life cycle. The course also covers data integration and collection methods, data governance tools, and how to express business requirements in a report format. \u003c\/p\u003e\r\n\u003cp\u003eUpon completing the course, you will receive a CompTIA Data+ Exam Voucher and the opportunity to work with an instructor for after-course one-on-one coaching. If you're a data analyst, reporting analyst, business intelligence analyst, clinical analyst, marketing analyst, operations analyst, or simply someone interested in data analytics, this course is for you!\u003c\/p\u003e\n\u003c\/div\u003e\u003cdiv\u003e\n\u003ch3\u003eCompTIA Data+® Certification Training Benefits\u003c\/h3\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eIn this course, you will:\u003c\/strong\u003e\u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003eParticipate in Instruction from CompTIA-certifiedData+ Certification preparation course.\u003c\/li\u003e\n\u003cli\u003eReceive a CompTIA Data+ Exam Voucher included upon completion of the course.\u003c\/li\u003e\n\u003cli\u003eIdentify Data Concepts and Environments important in analytics.\u003c\/li\u003e\n\u003cli\u003eExecute techniques in Data Mining, Data Mining, and Visualization.\u003c\/li\u003e\n\u003cli\u003eSummarize the importance of Data Governance, Quality, and Controls.\u003c\/li\u003e\n\u003cli\u003eContinue learning and face new challenges with after-course one-on-one instructor coaching.\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eTraining Prerequisites\u003c\/strong\u003e\u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003eCompTIA recommends 18–24 months (about 2 years) of experience in a report\/business analyst job to succeed in this course.\u003c\/li\u003e\n\u003cli\u003eExposure to databases and analytical tools, a basic understanding of statistics, and data visualization experiences, such as Excel, Power BI (Business Intelligence), and Tableau.\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eCertification Information\u003c\/strong\u003e\u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003eCompTIA DA0-001\u003c\/li\u003e\n\u003cli\u003eTesting through Pearson VUE\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003c\/div\u003e\u003cdiv\u003e\u003ch3\u003eCompTIA Data+ Instructor-Led Course Outline\u003c\/h3\u003e\u003c\/div\u003e\u003cdiv\u003e\n\u003ch4\u003eModule 1: Identifying Basic Concepts of Data Schemas\u003c\/h4\u003e\n\u003cp\u003eIn this module, you will learn how to:\u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003eIdentify Relational and Non-Relational Databases\u003c\/li\u003e\n\u003cli\u003eUnderstand the Way We Use Tables, Primary Keys, and Normalization\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003c\/div\u003e\u003cdiv\u003e\n\u003ch4\u003eModule 2: Understanding Different Data Systems\u003c\/h4\u003e\n\u003cp\u003eIn this module, you will learn how to:\u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003eDescribe Types of Data Processing and Storage Systems\u003c\/li\u003e\n\u003cli\u003eExplain How Data Changes\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003c\/div\u003e\u003cdiv\u003e\n\u003ch4\u003eModule 3: Understanding Types and Characteristics of Data\u003c\/h4\u003e\n\u003cp\u003eIn this module, you will learn how to:\u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003eUnderstand Types of Data\u003c\/li\u003e\n\u003cli\u003eBreak Down the Field Data Types\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003c\/div\u003e\u003cdiv\u003e\n\u003ch4\u003eModule 4: Comparing and Contrasting Different Data Structures, Formats, and Markup Languages\u003c\/h4\u003e\n\u003cp\u003eIn this module, you will learn how to:\u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003eDifferentiate between Structured Data and Unstructured Data\u003c\/li\u003e\n\u003cli\u003eRecognize Different File Formats\u003c\/li\u003e\n\u003cli\u003eUnderstand the Different Code Languages Used for Data\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003c\/div\u003e\u003cdiv\u003e\n\u003ch4\u003eModule 5: Explaining Data Integration and Collection Methods\u003c\/h4\u003e\n\u003cp\u003eIn this module, you will learn how to:\u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003eUnderstand the Processes of Extracting, Transforming, and Loading Data\u003c\/li\u003e\n\u003cli\u003eExplain \u003cabbr title=\"Application Programming Interface\"\u003eAPI\u003c\/abbr\u003e\/Web Scraping and Other Collection Methods\u003c\/li\u003e\n\u003cli\u003eCollect and Use Public and Publicly-Available Data\u003c\/li\u003e\n\u003cli\u003eUse and Collect Survey Data\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003c\/div\u003e\u003cdiv\u003e\n\u003ch4\u003eModule 6: Identifying Common Reasons for Cleansing and Profiling Data\u003c\/h4\u003e\n\u003cp\u003eIn this module, you will learn how to:\u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003eLearn to Profile Data\u003c\/li\u003e\n\u003cli\u003eAddress Redundant, Duplicated, and Unnecessary Data\u003c\/li\u003e\n\u003cli\u003eWork with Missing Values\u003c\/li\u003e\n\u003cli\u003eAddress Invalid Data\u003c\/li\u003e\n\u003cli\u003eConvert Data to Meet Specifications\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003c\/div\u003e\u003cdiv\u003e\n\u003ch4\u003eModule 7: Executing Different Data Manipulation Techniques\u003c\/h4\u003e\n\u003cp\u003eIn this module, you will learn how to:\u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003eManipulate Field Data and Create Variables\u003c\/li\u003e\n\u003cli\u003eTranspose and Append Data\u003c\/li\u003e\n\u003cli\u003eQuery Data\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003c\/div\u003e\u003cdiv\u003e\n\u003ch4\u003eModule 8: Explaining Common Techniques for Data Manipulation and Optimization\u003c\/h4\u003e\n\u003cp\u003eIn this module, you will learn how to:\u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003eUse Functions to Manipulate Data\u003c\/li\u003e\n\u003cli\u003eUse Common Techniques for Query Optimization\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003c\/div\u003e\u003cdiv\u003e\n\u003ch4\u003eModule 9: Applying Descriptive Statistical Methods\u003c\/h4\u003e\n\u003cp\u003eIn this module, you will learn how to:\u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003eUse Measures of Central Tendency\u003c\/li\u003e\n\u003cli\u003eUse Measures of Dispersion\u003c\/li\u003e\n\u003cli\u003eUse Frequency and Percentages\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003c\/div\u003e\u003cdiv\u003e\n\u003ch4\u003eModule 10: Describing Key Analysis Techniques\u003c\/h4\u003e\n\u003cp\u003eIn this module, you will learn how to:\u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003eGet Started with Analysis\u003c\/li\u003e\n\u003cli\u003eRecognize Types of Analysis\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003c\/div\u003e\u003cdiv\u003e\n\u003ch4\u003eModule 11: Understanding the Use of Different Statistical Methods\u003c\/h4\u003e\n\u003cp\u003eIn this module, you will learn how to:\u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003eUnderstand the Importance of Statistical Tests\u003c\/li\u003e\n\u003cli\u003eBreak Down the Hypothesis Test\u003c\/li\u003e\n\u003cli\u003eUnderstand Tests and Methods to Determine Relationships Between Variables\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003c\/div\u003e\u003cdiv\u003e\n\u003ch4\u003eModule 12: Using the Appropriate Type of Visualization\u003c\/h4\u003e\n\u003cp\u003eIn this module, you will learn how to:\u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003eUse Basic Visuals\u003c\/li\u003e\n\u003cli\u003eBuild Advanced Visuals\u003c\/li\u003e\n\u003cli\u003eBuild Maps with Geographical Data\u003c\/li\u003e\n\u003cli\u003eUse Visuals to Tell a Story\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003c\/div\u003e\u003cdiv\u003e\n\u003ch4\u003eModule 13: Expressing Business Requirements in a Report Format\u003c\/h4\u003e\n\u003cp\u003eIn this module, you will learn how to:\u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003eConsider Audience Needs When Developing a Report\u003c\/li\u003e\n\u003cli\u003eDescribe Data Source Considerations for Reporting\u003c\/li\u003e\n\u003cli\u003eDescribe Considerations for Delivering Reports and Dashboards\u003c\/li\u003e\n\u003cli\u003eDevelop Reports or Dashboards\u003c\/li\u003e\n\u003cli\u003eUnderstand Ways to Sort and Filter Data\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003c\/div\u003e\u003cdiv\u003e\n\u003ch4\u003eModule 14: Designing Components for Reports and Dashboards\u003c\/h4\u003e\n\u003cp\u003eIn this module, you will learn how to:\u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003eDesign Elements for Reports and Dashboards\u003c\/li\u003e\n\u003cli\u003eUtilize Standard Elements\u003c\/li\u003e\n\u003cli\u003eCreating a Narrative and Other Written Elements\u003c\/li\u003e\n\u003cli\u003eUnderstand Deployment Considerations\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003c\/div\u003e\u003cdiv\u003e\n\u003ch4\u003eModule 15: Distinguishing Different Report Types\u003c\/h4\u003e\n\u003cp\u003eIn this module, you will learn how to:\u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003eUnderstand How Updates and Timing Affect Reporting\u003c\/li\u003e\n\u003cli\u003eDifferentiate Between Types of Reports\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003c\/div\u003e\u003cdiv\u003e\n\u003ch4\u003eModule 16: Summarizing the Importance of Data Governance\u003c\/h4\u003e\n\u003cp\u003eIn this module, you will learn how to:\u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003eDefine Data Governance\u003c\/li\u003e\n\u003cli\u003eUnderstand Access Requirements and Policies\u003c\/li\u003e\n\u003cli\u003eUnderstand Security Requirements\u003c\/li\u003e\n\u003cli\u003eUnderstand Entity Relationship Requirements\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003c\/div\u003e\u003cdiv\u003e\n\u003ch4\u003eModule 17: Applying Quality Control to Data\u003c\/h4\u003e\n\u003cp\u003eIn this module, you will learn how to:\u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003eDescribe Characteristics, Rules, and Metrics of Data Quality\u003c\/li\u003e\n\u003cli\u003eIdentify Reasons to Quality Check Data and Methods of Data Validation\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003c\/div\u003e\u003cdiv\u003e\n\u003ch4\u003eModule 18: Explaining Master Data Management Concepts\u003c\/h4\u003e\n\u003cp\u003eIn this module, you will learn how to:\u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003eExplain the Basics of Master Data Management\u003c\/li\u003e\n\u003cli\u003eDescribe Master Data Management Processes\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003c\/div\u003e","brand":"Learning Tree","offers":[{"title":"268A81US \/ 2026-08-31T09:00:00 \/ Herndon, VA","offer_id":47534191345883,"sku":"US-2043-IL","price":2680.0,"currency_code":"USD","in_stock":true},{"title":"26AB69US \/ 2026-10-19T09:00:00 \/ Herndon, VA","offer_id":48216570298587,"sku":"US-2043-IL","price":2680.0,"currency_code":"USD","in_stock":true},{"title":"26CB53US \/ 2026-12-14T09:00:00 \/ Herndon, VA","offer_id":48216570331355,"sku":"US-2043-IL","price":2680.0,"currency_code":"USD","in_stock":true},{"title":"272B46US \/ 2027-02-01T09:00:00 \/ Herndon, VA","offer_id":48216570364123,"sku":"US-2043-IL","price":2680.0,"currency_code":"USD","in_stock":true},{"title":"274B66US \/ 2027-04-12T09:00:00 \/ Herndon, VA","offer_id":48586425630939,"sku":"US-2043-IL","price":2680.0,"currency_code":"USD","in_stock":true}]},{"product_id":"microsoft-azure-ai-fundamentals-training-ai-900","title":"Introduction to AI in Azure (AI-901)","description":"\u003cdiv\u003e\u003cp\u003eThis course introduces core concepts related to artificial intelligence (AI), and the services in Microsoft Azure that can be used to create AI solutions.\u003c\/p\u003e\u003c\/div\u003e\u003cdiv\u003e\n\u003ch3\u003eIntroduction to AI in Azure (AI-901) Benefits\u003c\/h3\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cb\u003eIn this course, you will:\u003c\/b\u003e\u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003eGain a solid foundation in AI concepts including machine learning, NLP, computer vision, and generative AI.\u003c\/li\u003e\n\u003cli\u003eLearn hands-on with Azure AI Foundry to build and test intelligent applications.\u003c\/li\u003e\n\u003cli\u003eUnderstand real-world applications of AI such as text analysis, speech recognition, image analysis, and information extraction.\u003c\/li\u003e\n\u003cli\u003eExplore responsible AI practices to design solutions that are ethical and trustworthy.\u003c\/li\u003e\n\u003cli\u003ePrepare for more advanced AI projects by establishing a strong base in both theory and Azure tools.\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cb\u003ePrerequisites:\u003c\/b\u003e\u003c\/p\u003e\n\u003cp\u003ePrerequisite certification is not required before taking this course. However, successful Azure AI Fundamental students start with essential knowledge of computing and internet concepts and an interest in using Azure AI services.\u003c\/p\u003e\n\u003cp\u003eBefore taking this course, students should have:\u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003eExperience using computers and the internet.\u003c\/li\u003e\n\u003cli\u003eInterest in use cases for AI applications and machine learning models.\u003c\/li\u003e\n\u003cli\u003eA willingness to learn through hands-on exploration.\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cb\u003eExam Information:\u003c\/b\u003e\u003c\/p\u003e\n\u003cp\u003eThis course can help you prepare for the following Microsoft role-based certification exam — \u003ca href=\"https:\/\/docs.microsoft.com\/en-us\/learn\/certifications\/exams\/ai-900\" title=\"Exam AI-901: Microsoft Certified: Azure AI Fundamentals\" target=\"_blank\" rel=\"nofollow noopener\"\u003eExam AI-901: Microsoft Certified: Azure AI Fundamentals\u003c\/a\u003e.\u003c\/p\u003e\n\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003c\/div\u003e\u003cdiv\u003e\u003ch3\u003eIntroduction to AI in Azure Training Outline\u003c\/h3\u003e\u003c\/div\u003e\u003cdiv\u003e\n\u003ch4\u003eLearning Objectives\u003c\/h4\u003e\n\u003cp\u003eChapter 1: Introduction to AI Concepts\u003c\/p\u003e\n\u003cul type=\"disc\"\u003e\n\u003cli\u003eExplore core AI topics including generative AI, computer vision, speech, natural language processing, and responsible AI.\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cp\u003eChapter 2: Introduction to Machine Learning Concepts\u003c\/p\u003e\n\u003cul type=\"disc\"\u003e\n\u003cli\u003eUnderstand machine learning types, regression, classification, clustering, deep learning, and transformers.\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cp\u003eChapter 3: Get Started with Machine Learning in Azure\u003c\/p\u003e\n\u003cul type=\"disc\"\u003e\n\u003cli\u003eLearn to define problems, prepare data, train models, and use Azure Machine Learning Studio.\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cp\u003eChapter 4: Introduction to Generative AI Concepts\u003c\/p\u003e\n\u003cul type=\"disc\"\u003e\n\u003cli\u003eDiscover how language models and transformers generate original content and apply responsible practices.\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cp\u003eChapter 5: Get Started with Generative AI in Azure\u003c\/p\u003e\n\u003cul type=\"disc\"\u003e\n\u003cli\u003eUse Azure AI Foundry’s tools, model catalog, and playgrounds to build generative AI apps.\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cp\u003eChapter 6: Introduction to Natural Language Processing Concepts\u003c\/p\u003e\n\u003cul type=\"disc\"\u003e\n\u003cli\u003eLearn the fundamentals of NLP, from statistical techniques to semantic models.\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cp\u003eChapter 7: Get Started with Natural Language Processing in Azure\u003c\/p\u003e\n\u003cul type=\"disc\"\u003e\n\u003cli\u003eApply Azure AI Language services for sentiment analysis, conversational AI, and translation.\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cp\u003eChapter 8: Get Started with Speech in Azure\u003c\/p\u003e\n\u003cul type=\"disc\"\u003e\n\u003cli\u003eRecognize and synthesize speech with Azure AI Speech services.\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cp\u003eChapter 9: Introduction to Computer Vision Concepts\u003c\/p\u003e\n\u003cul type=\"disc\"\u003e\n\u003cli\u003eExplore image processing, modern vision models, and machine learning for vision.\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cp\u003eChapter 10: Get Started with Computer Vision in Azure\u003c\/p\u003e\n\u003cul type=\"disc\"\u003e\n\u003cli\u003eUse Azure AI Vision services to analyze images and detect faces.\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cp\u003eChapter 11: Introduction to AI-Powered Information Extraction Concepts\u003c\/p\u003e\n\u003cul type=\"disc\"\u003e\n\u003cli\u003eUnderstand how to extract data from images, forms, and multimodal sources.\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cp\u003eChapter 12: Get Started with AI-Powered Information Extraction in Azure\u003c\/p\u003e\n\u003cul type=\"disc\"\u003e\n\u003cli\u003eUse Azure AI services to extract insights, build knowledge mining solutions, and analyze documents.\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003c\/div\u003e","brand":"Microsoft","offers":[{"title":"266B79US \/ 2026-06-29T09:00:00 \/ Online","offer_id":47534175846619,"sku":"US-8580-IL","price":716.0,"currency_code":"USD","in_stock":true},{"title":"268B96US \/ 2026-08-31T09:00:00 \/ Online","offer_id":47534175912155,"sku":"US-8580-IL","price":716.0,"currency_code":"USD","in_stock":true},{"title":"26AA17US \/ 2026-10-01T09:00:00 \/ Online","offer_id":47638415933659,"sku":"US-8580-IL","price":716.0,"currency_code":"USD","in_stock":true},{"title":"268B01CN \/ 2026-08-10T09:00:00 \/ Online","offer_id":48216544149723,"sku":"US-8580-IL","price":716.0,"currency_code":"USD","in_stock":true},{"title":"26AA42CN \/ 2026-10-26T09:00:00 \/ Online","offer_id":48216544182491,"sku":"US-8580-IL","price":716.0,"currency_code":"USD","in_stock":true},{"title":"26BB37US \/ 2026-11-10T09:00:00 \/ Online","offer_id":48216544215259,"sku":"US-8580-IL","price":716.0,"currency_code":"USD","in_stock":true},{"title":"26BB38US \/ 2026-11-24T09:00:00 \/ Online","offer_id":48216544248027,"sku":"US-8580-IL","price":716.0,"currency_code":"USD","in_stock":true},{"title":"26CB02US \/ 2026-12-07T09:00:00 \/ Online","offer_id":48216544280795,"sku":"US-8580-IL","price":716.0,"currency_code":"USD","in_stock":true},{"title":"271A61CN \/ 2027-01-12T09:00:00 \/ Online","offer_id":48216544313563,"sku":"US-8580-IL","price":716.0,"currency_code":"USD","in_stock":true},{"title":"272A93US \/ 2027-02-22T09:00:00 \/ Online","offer_id":48291026960603,"sku":"US-8580-IL","price":716.0,"currency_code":"USD","in_stock":true},{"title":"273A55CN \/ 2027-03-22T09:00:00 \/ Online","offer_id":48498605719771,"sku":"US-8580-IL","price":716.0,"currency_code":"USD","in_stock":true},{"title":"273B17US \/ 2027-03-31T09:00:00 \/ Online","offer_id":48525735362779,"sku":"US-8580-IL","price":716.0,"currency_code":"USD","in_stock":true},{"title":"274B03US \/ 2027-04-12T09:00:00 \/ Online","offer_id":48586421895387,"sku":"US-8580-IL","price":716.0,"currency_code":"USD","in_stock":true},{"title":"275B27US \/ 2027-05-11T09:00:00 \/ Online","offer_id":48759292985563,"sku":"US-8580-IL","price":716.0,"currency_code":"USD","in_stock":true},{"title":"275B28US \/ 2027-05-25T09:00:00 \/ Online","offer_id":48804199104731,"sku":"US-8580-IL","price":716.0,"currency_code":"USD","in_stock":true},{"title":"269D46US \/ 2026-09-16T09:00:00 \/ Online","offer_id":48837148016859,"sku":"US-8580-IL","price":716.0,"currency_code":"USD","in_stock":true}]},{"product_id":"implementing-a-lakehouse-with-microsoft-fabric-dp-601","title":"Implementing a Lakehouse with Microsoft Fabric (DP-601)","description":"\u003cdiv\u003e\n\u003cp\u003eThis course is designed to build your foundational skills in data engineering on Microsoft Fabric, focusing on the Lakehouse concept. This \u003cb\u003eMicrosoft Fabric (DP-601) Training\u003c\/b\u003e course will explore the powerful capabilities of Apache Spark for distributed data processing and the essential techniques for efficient data management, versioning, and reliability by working with Delta Lake tables. This course will also explore data ingestion and orchestration using Dataflows Gen2 and Data Factory pipelines.\u003c\/p\u003e\r\n\u003cp\u003eThis course includes a combination of lectures and hands-on exercises that will prepare you to work with Lakehouses in Microsoft Fabric.\u003c\/p\u003e\n\u003c\/div\u003e\u003cdiv\u003e\n\u003ch3\u003eImplementing a Lakehouse with Microsoft Fabric (DP-601) Benefits\u003c\/h3\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cb\u003eIn this course, you will:\u003c\/b\u003e\u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003eDiscover how Microsoft Fabric can meet your enterprise's analytics needs in one platform.\u003c\/li\u003e\n\u003cli\u003eDescribe core features and capabilities of lakehouses in Microsoft Fabric.\u003c\/li\u003e\n\u003cli\u003eAnalyze and process data in a Lakehouse at scale.\u003c\/li\u003e\n\u003cli\u003eCreate advanced analytics solutions using the enhanced capabilities of delta tables.\u003c\/li\u003e\n\u003cli\u003eVisually create multi-step data ingestion and transformation using Power Query Online with Dataflows (Gen2).\u003c\/li\u003e\n\u003cli\u003eCreate pipelines that orchestrate data ingestion and transformation tasks with Data Factory capabilities within Microsoft Fabric.\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cb\u003eTraining Prerequisites\u003c\/b\u003e\u003c\/p\u003e\n\u003cp\u003eYou should be familiar with basic data concepts and terminology.\u003c\/p\u003e\n\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003c\/div\u003e\u003cdiv\u003e\u003ch3\u003eMicrosoft Fabric (DP-601) Training Outline\u003c\/h3\u003e\u003c\/div\u003e\u003cdiv\u003e\n\u003ch4\u003eModule 1: Introduction to end-to-end analytics using Microsoft Fabric\u003c\/h4\u003e\n\u003cp\u003eIn this module, you'll learn how to:\u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003eDescribe end-to-end analytics in Microsoft Fabric\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003c\/div\u003e\u003cdiv\u003e\n\u003ch4\u003eModule 2: Get started with lakehouses in Microsoft Fabric\u003c\/h4\u003e\n\u003cp\u003eIn this module, you'll learn how to:\u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003eDescribe core features and capabilities of lakehouses in Microsoft Fabric\u003c\/li\u003e\n\u003cli\u003eCreate a lakehouse\u003c\/li\u003e\n\u003cli\u003eIngest data into files and tables in a lakehouse\u003c\/li\u003e\n\u003cli\u003eQuery lakehouse tables with SQL\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003c\/div\u003e\u003cdiv\u003e\n\u003ch4\u003eModule 3: Use Apache Spark in Microsoft Fabric\u003c\/h4\u003e\n\u003cp\u003eIn this module, you'll learn how to:\u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003eConfigure Spark in a Microsoft Fabric workspace\u003c\/li\u003e\n\u003cli\u003eIdentify suitable scenarios for Spark notebooks and Spark jobs\u003c\/li\u003e\n\u003cli\u003eUse Spark dataframes to analyze and transform data\u003c\/li\u003e\n\u003cli\u003eUse Spark SQL to query data in tables and views\u003c\/li\u003e\n\u003cli\u003eVisualize data in a Spark notebook\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003c\/div\u003e\u003cdiv\u003e\n\u003ch4\u003eModule 4: Work with Delta Lake tables in Microsoft Fabric\u003c\/h4\u003e\n\u003cp\u003eIn this module, you'll learn how to:\u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003eUnderstand Delta Lake and delta tables in Microsoft Fabric\u003c\/li\u003e\n\u003cli\u003eCreate and manage delta tables using Spark\u003c\/li\u003e\n\u003cli\u003eUse Spark to query and transform data in delta tables\u003c\/li\u003e\n\u003cli\u003eUse delta tables with Spark structured streaming\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003c\/div\u003e\u003cdiv\u003e\n\u003ch4\u003eModule 5: Ingest Data with Dataflows Gen2 in Microsoft Fabric\u003c\/h4\u003e\n\u003cp\u003eIn this module, you'll learn how to:\u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003eDescribe Dataflow (Gen2) capabilities in Microsoft Fabric\u003c\/li\u003e\n\u003cli\u003eCreate Dataflow (Gen2) solutions to ingest and transform data\u003c\/li\u003e\n\u003cli\u003eInclude a Dataflow (Gen2) in a pipeline\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003c\/div\u003e\u003cdiv\u003e\n\u003ch4\u003eModule 6: Use Data Factory pipelines in Microsoft Fabric\u003c\/h4\u003e\n\u003cp\u003eIn this module, you'll learn how to:\u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003eDescribe pipeline capabilities in Microsoft Fabric\u003c\/li\u003e\n\u003cli\u003eUse the Copy Data activity in a pipeline\u003c\/li\u003e\n\u003cli\u003eCreate pipelines based on predefined templates\u003c\/li\u003e\n\u003cli\u003eRun and monitor 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performance\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eLimited Understanding of Jupyter Features\u003c\/strong\u003e – Many users only scratch the surface, missing out on powerful features like magic commands, interactive widgets, and automation with papermill\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eData Handling Challenges\u003c\/strong\u003e – Loading, cleaning, and visualizing data efficiently in Jupyter can be overwhelming without structured guidance\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eCollaboration \u0026amp; Sharing Issues\u003c\/strong\u003e – Struggling to share notebooks effectively, use version control with GitHub, or deploy notebooks for remote access\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003ePerformance \u0026amp; Scalability Problems\u003c\/strong\u003e – Running into slow execution times, memory overload, or difficulty deploying notebooks to cloud platforms like Google Colab\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eBest Practices for Reproducibility\u003c\/strong\u003e – Many users create cluttered notebooks that are hard to maintain, debug, or reuse later\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003ePrerequisites\u003c\/strong\u003e\u003c\/p\u003e\n\u003cp\u003eNo advanced programming knowledge is required, but familiarity with Python and basic data handling will be beneficial.\u003c\/p\u003e\n\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003c\/div\u003e\u003cdiv\u003e\u003ch3\u003eMastering Jupyter Notebooks Course Outline\u003c\/h3\u003e\u003c\/div\u003e\u003cdiv\u003e\n\u003ch4\u003eLearning Objectives\u003c\/h4\u003e\n\u003cp\u003e\u003cb\u003eChapter 1: Introduction to Jupyter Notebooks\u003c\/b\u003e\u003c\/p\u003e\n\u003cul type=\"disc\"\u003e\n\u003cli\u003eWhat is Jupyter?\u003c\/li\u003e\n\u003cli\u003eInstallation \u0026amp; Setup (Anaconda vs. pip)\u003c\/li\u003e\n\u003cli\u003eNavigating the Jupyter Interface\u003c\/li\u003e\n\u003cli\u003eJupyterLab vs. Jupyter Notebook\u003c\/li\u003e\n\u003cli\u003eCreating \u0026amp; Managing Notebooks\u003c\/li\u003e\n\u003cli\u003eJupyter Kernels\u003c\/li\u003e\n\u003cli\u003eMarkdown Basics for Documentation\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cp\u003e\u003cb\u003eChapter 2: Python and Jupyter Fundamentals\u003c\/b\u003e\u003c\/p\u003e\n\u003cul type=\"disc\"\u003e\n\u003cli\u003eRunning Python Code in Jupyter\u003c\/li\u003e\n\u003cli\u003eUnderstanding Cells: Code vs. Markdown\u003c\/li\u003e\n\u003cli\u003eUsing Magic Commands (%timeit, %matplotlib inline, etc.)\u003c\/li\u003e\n\u003cli\u003eImporting Libraries and Data\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cul type=\"disc\"\u003e\n\u003cli\u003eLoading and Displaying Data (pandas, numpy)\u003c\/li\u003e\n\u003cli\u003eData Visualization (matplotlib, seaborn)\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cp\u003e\u003cb\u003eChapter 3: Advanced Jupyter Features\u003c\/b\u003e\u003c\/p\u003e\n\u003cul type=\"disc\"\u003e\n\u003cli\u003eRunning Shell Commands in Jupyter\u003c\/li\u003e\n\u003cli\u003eCreating and Using Custom Functions \u0026amp; Modules\u003c\/li\u003e\n\u003cli\u003eAutomating Workflows with Jupyter (\u003ca href=\"https:\/\/papermill.readthedocs.io\/en\/latest\/extending-overview.html\" rel=\"nofollow noopener\" target=\"_blank\"\u003epapermill\u003c\/a\u003e)  \u003c\/li\u003e\n\u003cli\u003eInteractive Widgets (ipywidgets)\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cp\u003e\u003cb\u003eChapter 4: Jupyter for Collaboration \u0026amp; Deployment\u003c\/b\u003e\u003c\/p\u003e\n\u003cul type=\"disc\"\u003e\n\u003cli\u003eSharing Results with Notebooks\u003c\/li\u003e\n\u003cli\u003eExporting Notebooks (HTML, PDF, Markdown)\u003c\/li\u003e\n\u003cli\u003eSharing Notebooks with GitHub\u003c\/li\u003e\n\u003cli\u003eUsing JupyterHub for Team Collaboration\u003c\/li\u003e\n\u003cli\u003eDeploying Notebooks on Cloud Services (Google Colab, Binder)\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cp\u003e\u003cb\u003eChapter 5: Jupyter Best Practices\u003c\/b\u003e\u003c\/p\u003e\n\u003cul type=\"disc\"\u003e\n\u003cli\u003eBest Practices for Efficient Notebook Usage\u003cbr\u003e\u003ca href=\"https:\/\/coderpad.io\/blog\/data-science\/mastering-jupyter-notebooks-best-practices-for-data-science\/\" rel=\"nofollow noopener\" target=\"_blank\"\u003eResource 1\u003c\/a\u003e\u003cbr\u003e\u003ca href=\"https:\/\/medium.com\/towards-data-science\/jupyter-notebook-best-practices-f430a6ba8c69\" rel=\"nofollow noopener\" target=\"_blank\"\u003eResource 2\u003c\/a\u003e \u003c\/li\u003e\n\u003c\/ul\u003e\n\u003c\/div\u003e","brand":"Learning Tree","offers":[{"title":"267A17US \/ 2026-07-14T09:00:00 \/ Online","offer_id":47260370960603,"sku":"US-4741-IL","price":716.0,"currency_code":"USD","in_stock":true},{"title":"269A17US \/ 2026-09-01T09:00:00 \/ 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Online","offer_id":48809989177563,"sku":"US-4741-IL","price":716.0,"currency_code":"USD","in_stock":true}]},{"product_id":"accelerate-app-development-by-using-github-copilot-az-2007","title":"Accelerate App Development by Using GitHub Copilot (AZ-2007)","description":"\u003cdiv\u003e\u003cp\u003eLearn how to accelerate app development by using GitHub Copilot and GitHub Copilot Chat in a Visual Studio Code environment.\u003c\/p\u003e\u003c\/div\u003e\u003cdiv\u003e\n\u003ch3\u003eAccelerate App Development by Using GitHub Copilot (AZ-2007) Benefits\u003c\/h3\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eIn this course you will:\u003c\/strong\u003e\u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003eBoost development speed with AI assistance: Accelerate code writing, testing, and documentation tasks using GitHub Copilot and Copilot Chat.\u003c\/li\u003e\n\u003cli\u003eEnhance code quality and maintainability: Use AI-driven suggestions to improve readability, performance, reliability, and security.\u003c\/li\u003e\n\u003cli\u003eGain hands-on, real-world experience: Complete interactive exercises and a guided project that reflect real development scenarios.\u003c\/li\u003e\n\u003cli\u003eMaster GitHub Copilot extensions in Visual Studio Code: Learn to install, configure, and effectively use GitHub Copilot and Copilot Chat within VS Code.\u003c\/li\u003e\n\u003cli\u003eStay competitive with AI-driven development skills: Build modern coding skills that give you an edge in today’s fast-evolving tech landscape.\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003ePrerequisites:\u003c\/strong\u003e\u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003eOne or more years of code development experience is recommended.\u003c\/li\u003e\n\u003cli\u003eSome experience developing C# applications in Visual Studio Code with the C# Dev Kit extension is recommended.\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eExam Information\u003c\/strong\u003e\u003c\/p\u003e\n\u003cp\u003eN\/A\u003c\/p\u003e\n\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003c\/div\u003e\u003cdiv\u003e\u003ch3\u003eGitHub Copilot for App Dev (AZ-2007) Course Outline\u003c\/h3\u003e\u003c\/div\u003e\u003cdiv\u003e\n\u003ch4\u003eLearning Objectives\u003c\/h4\u003e\n\u003cp\u003e\u003cstrong\u003eGet Started with GitHub Copilot\u003c\/strong\u003e\u003c\/p\u003e\n\u003cul type=\"disc\"\u003e\n\u003cli\u003eIntroduction to GitHub Copilot and related AI tools from GitHub, OpenAI, and Microsoft\u003c\/li\u003e\n\u003cli\u003eOverview of GitHub Copilot tools, benefits, and limitations\u003c\/li\u003e\n\u003cli\u003eInstall GitHub Copilot extensions for Visual Studio Code\u003c\/li\u003e\n\u003cli\u003eExplore autocomplete features of the GitHub Copilot extension\u003c\/li\u003e\n\u003cli\u003eExplore AI assistance features of the GitHub Copilot Chat extension\u003c\/li\u003e\n\u003cli\u003eConfigure GitHub Copilot extensions for Visual Studio Code\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cp\u003e \u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eGenerate Documentation Using GitHub Copilot Tools\u003c\/strong\u003e\u003c\/p\u003e\n\u003cul type=\"disc\"\u003e\n\u003cli\u003eIntroduction to documentation capabilities with GitHub Copilot Chat\u003c\/li\u003e\n\u003cli\u003eUse the “document” and “explain” features\u003c\/li\u003e\n\u003cli\u003eGenerate code explanations\u003c\/li\u003e\n\u003cli\u003eGenerate project documentation\u003c\/li\u003e\n\u003cli\u003eGenerate inline code documentation\u003c\/li\u003e\n\u003cli\u003eComplete a documentation challenge\u003c\/li\u003e\n\u003cli\u003eReview the challenge solution\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cp\u003e \u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eDevelop Code Features Using GitHub Copilot Tools\u003c\/strong\u003e\u003c\/p\u003e\n\u003cul 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conditions\u003c\/li\u003e\n\u003cli\u003eComplete a \"create unit tests\" challenge\u003c\/li\u003e\n\u003cli\u003eReview the challenge solution\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cp\u003e \u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eImplement Code Improvements Using GitHub Copilot Tools\u003c\/strong\u003e\u003c\/p\u003e\n\u003cul type=\"disc\"\u003e\n\u003cli\u003eUnderstand Copilot’s support for improving code\u003c\/li\u003e\n\u003cli\u003eImprove code quality\u003c\/li\u003e\n\u003cli\u003eImprove reliability and performance\u003c\/li\u003e\n\u003cli\u003eImprove security\u003c\/li\u003e\n\u003cli\u003eComplete an “app improvement” challenge\u003c\/li\u003e\n\u003cli\u003eReview the challenge solution\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cp\u003e \u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eGuided Project – Accelerate App Development Using GitHub Copilot Tools\u003c\/strong\u003e\u003c\/p\u003e\n\u003cul type=\"disc\"\u003e\n\u003cli\u003eIntroduction to the guided 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By the end, you'll have a firm grasp on the concepts and practical applications tested on the AWS Certified Machine Learning Engineer - Associate (MLA-C01) exam.\u003c\/p\u003e\u003c\/div\u003e\u003cdiv\u003e\n\u003ch3\u003eMachine Learning Engineering on AWS Benefits\u003c\/h3\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eUpon completing this course, students should be able to:\u003c\/strong\u003e\u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003eIdentify the scope and content tested by the AWS Certified Machine Learning Engineer - Associate (MLA-C01) exam.\u003c\/li\u003e\n\u003cli\u003ePractice exam-style questions and evaluate your preparation strategy.\u003c\/li\u003e\n\u003cli\u003eExamine use cases and differentiate between them.\u003cb\u003e\u003c\/b\u003e\n\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cb\u003ePrerequisites\u003c\/b\u003e\u003c\/p\u003e\n\u003cp\u003eLearners are recommended to have the following:\u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003eSuggested 1 year of experience in a related role such as a backend software developer, DevOps developer, data engineer, or data scientist.\u003c\/li\u003e\n\u003cli\u003eBasic understanding of common ML algorithms and their use cases\u003c\/li\u003e\n\u003cli\u003eData engineering fundamentals, including knowledge of common data formats, ingestion, and transformation to work with ML data pipelines\u003c\/li\u003e\n\u003cli\u003eKnowledge of querying and transforming data\u003c\/li\u003e\n\u003cli\u003eKnowledge of software engineering best practices for modular, reusable code development, deployment, and debugging\u003c\/li\u003e\n\u003cli\u003eFamiliarity with provisioning and monitoring cloud and on-premises ML resources\u003c\/li\u003e\n\u003cli\u003eExperience with continuous integration and continuous delivery (CI\/CD) pipelines and infrastructure as code (IaC)\u003c\/li\u003e\n\u003cli\u003eExperience with code repositories for version control and CI\/CD pipelines.\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cp\u003e \u003c\/p\u003e\n\u003cp\u003eLearners are recommended to be able to do the following:\u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003eSuggested 1 year of experience using Amazon SageMaker AI and other AWS services for ML engineering.\u003c\/li\u003e\n\u003cli\u003eKnowledge of Amazon SageMaker AI capabilities and algorithms for model building and deployment\u003c\/li\u003e\n\u003cli\u003eKnowledge of AWS data storage and processing services for preparing data for modeling\u003c\/li\u003e\n\u003cli\u003eFamiliarity with deploying applications and infrastructure on AWS\u003c\/li\u003e\n\u003cli\u003eKnowledge of monitoring tools for logging and troubleshooting ML systems\u003c\/li\u003e\n\u003cli\u003eKnowledge of AWS services for the automation and orchestration of CI\/CD pipelines\u003c\/li\u003e\n\u003cli\u003eUnderstanding of AWS security best practices for identity and access management, encryption, and data protection\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003c\/div\u003e\u003cdiv\u003e\u003ch3\u003eMachine Learning Engineering on AWS Course Outline\u003c\/h3\u003e\u003c\/div\u003e\u003cdiv\u003e\n\u003ch4\u003eLearning Objectives\u003c\/h4\u003e\n\u003cp\u003e\u003cb\u003eDomain 1: \u003c\/b\u003eData Preparation for Machine Learning (ML)\u003c\/p\u003e\n\u003cp\u003e1.1 Ingest and store data.\u003c\/p\u003e\n\u003cp\u003e1.2 Transform data and perform feature engineering.\u003c\/p\u003e\n\u003cp\u003e1.3 Ensure data integrity and prepare data for modeling\u003c\/p\u003e\n\u003cp\u003e\u003cb\u003e \u003c\/b\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cb\u003eDomain 2:\u003c\/b\u003e ML Model Development\u003c\/p\u003e\n\u003cp\u003e2.1 Choose a modeling approach.\u003c\/p\u003e\n\u003cp\u003e2.2 Train and refine models.\u003c\/p\u003e\n\u003cp\u003e2.3 Analyze model 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This course introduces the concept of a data lakehouse and the Google Cloud products used to create one. A lakehouse architecture uses open-standard data sources and combines the best features of data lakes and data warehouses, which addresses many of their shortcomings.\u003c\/p\u003e\u003c\/div\u003e\u003cdiv\u003e\n\u003ch3\u003eBuild Data Lakes and Warehouses on Google Cloud Benefits\u003c\/h3\u003e\n\u003cul\u003e\u003cli\u003e\n\u003cp\u003e\u003cb\u003eImportant Course Information:\u003c\/b\u003e\u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003eDifferentiate between a data lake, data warehouse, data lakehouse\u003c\/li\u003e\n\u003cli\u003eExplain the data lakehouse concept and how it addresses the limitations of traditional data lakes and data warehouses\u003c\/li\u003e\n\u003cli\u003eIdentify various data sources that BigQuery can query\u003c\/li\u003e\n\u003cli\u003eRecognize the capabilities of using BigQuery to create and access AI models\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cp\u003e\u003cb\u003ePrerequisites:\u003c\/b\u003e\u003c\/p\u003e\n\u003cp\u003eTo benefit from this course participants should have familiarity, training, or experience with the principles and activities associated with data engineering, data warehouse or data lake architecture, SQL query language, or data management principles.\u003c\/p\u003e\n\u003c\/li\u003e\u003c\/ul\u003e\n\u003c\/div\u003e\u003cdiv\u003e\u003ch3\u003eGoogle Data Lakes and Warehouses Course Outline\u003c\/h3\u003e\u003c\/div\u003e\u003cdiv\u003e\n\u003ch4\u003eLearning Objectives\u003c\/h4\u003e\n\u003cp\u003e\u003cb\u003eModule 01 \u003c\/b\u003e\u003c\/p\u003e\n\u003cp\u003eIntroduce the learner to the topics that will be covered in the course and the skills they will learn.\u003c\/p\u003e\n\u003cp\u003e\u003cb\u003eModule 02\u003c\/b\u003e\u003c\/p\u003e\n\u003cp\u003eThis module introduces the foundational concepts of data lakes and data warehouses, setting the stage for modern architectures on Google Cloud.\u003c\/p\u003e\n\u003cp\u003e\u003cb\u003eModule 03 \u003c\/b\u003e\u003c\/p\u003e\n\u003cp\u003eThis module details the concept of a lakehouse and introduces the Google Cloud products most commonly used to build a modern data lakehouse using open-source formats.\u003c\/p\u003e\n\u003cp\u003e\u003cb\u003eModule  04 \u003c\/b\u003e\u003c\/p\u003e\n\u003cp\u003eThis module explores BigQuery as the cornerstone of a modern data warehouse and introduces BigLake for unifying access across the data lake and warehouse.\u003c\/p\u003e\n\u003cp\u003e\u003cb\u003eModule 05 \u003c\/b\u003e\u003c\/p\u003e\n\u003cp\u003eThis module focuses on advanced architectural patterns for the lakehouse, including data processing, orchestration, and comprehensive data governance across BigQuery, Cloud Storage, and BigLake.\u003c\/p\u003e\n\u003cp\u003e\u003cb\u003eModule 06 \u003c\/b\u003e\u003c\/p\u003e\n\u003cp\u003eThis module provides labs to deepen skills in the tools and technologies used by a lakehouse on Google Cloud and an overview of best practices, common mistakes, and future trends\u003c\/p\u003e\n\u003cp\u003e\u003cb\u003eModule 07 \u003c\/b\u003e\u003c\/p\u003e\n\u003cp\u003eSummarize the architectural and operational capabilities of the BigQuery-centric data lakehouse, covering governance, advanced analytics, and machine learning\u003c\/p\u003e\n\u003c\/div\u003e","brand":"Learning Tree","offers":[{"title":"269C06US \/ 2026-09-09T09:00:00 \/ Online","offer_id":48101401985243,"sku":"US-1492-IL","price":716.0,"currency_code":"USD","in_stock":true},{"title":"26CA27US \/ 2026-12-08T09:00:00 \/ Online","offer_id":48101402018011,"sku":"US-1492-IL","price":716.0,"currency_code":"USD","in_stock":true},{"title":"273A20US \/ 2027-03-09T09:00:00 \/ Online","offer_id":48331763482843,"sku":"US-1492-IL","price":716.0,"currency_code":"USD","in_stock":true}]},{"product_id":"data-integration-with-cloud-data-fusion","title":"Data Integration with Cloud Data Fusion","description":"\u003cdiv\u003e\u003cp\u003eThis 2-day course introduces learners to Google Cloud’s data integration capability using Cloud Data Fusion. In this course, we discuss challenges with data integration and the need for a data integration platform (middleware). We then discuss how Cloud Data Fusion can help to effectively integrate data from a variety of sources and formats and generate insights. We take a look at Cloud Data Fusion’s main components and how they work, how to process batch data and real time streaming data with visual pipeline design, rich tracking of metadata and data lineage, and how to deploy data pipelines on various execution engines.\u003c\/p\u003e\u003c\/div\u003e\u003cdiv\u003e\n\u003ch3\u003eData Integration with Cloud Data Fusion Benefits\u003c\/h3\u003e\n\u003cul\u003e\u003cli\u003e\n\u003cp\u003e\u003cb\u003eImportant Course Information:\u003c\/b\u003e\u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003eIdentify the need of data integration,\u003c\/li\u003e\n\u003cli\u003eUnderstand the capabilities Cloud Data Fusion provides as a data integration platform,\u003c\/li\u003e\n\u003cli\u003eIdentify use cases for possible implementation with Cloud Data Fusion,\u003c\/li\u003e\n\u003cli\u003e List the core components of Cloud Data Fusion,\u003c\/li\u003e\n\u003cli\u003eDesign and execute batch and real time data processing pipelines,\u003c\/li\u003e\n\u003cli\u003eWork with Wrangler to build data transformations\u003c\/li\u003e\n\u003cli\u003eUse connectors to integrate data from various sources and formats,\u003c\/li\u003e\n\u003cli\u003eConfigure execution environment; Monitor and Troubleshoot pipeline execution,\u003c\/li\u003e\n\u003cli\u003eUnderstand the relationship between metadata and data lineage.\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cp\u003e\u003cb\u003ePrerequisites:\u003c\/b\u003e\u003c\/p\u003e\n\u003cp\u003eTo get the most out of this course, participants are encouraged to have completed “Introduction to Data Engineering”\u003cb\u003e\u003c\/b\u003e\u003c\/p\u003e\n\u003c\/li\u003e\u003c\/ul\u003e\n\u003c\/div\u003e\u003cdiv\u003e\u003ch3\u003eData Integration with Google Cloud Data Fusion Training Outline\u003c\/h3\u003e\u003c\/div\u003e\u003cdiv\u003e\n\u003ch4\u003eLearning Objectives\u003c\/h4\u003e\n\u003cp\u003eModule 1) Introduction to data integration and Cloud Data Fusion  \u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003eUnderstand the need for data integration,\u003c\/li\u003e\n\u003cli\u003eList the situations\/cases where data integration can help businesses,\u003c\/li\u003e\n\u003cli\u003eList the available data integration platforms and tools,\u003c\/li\u003e\n\u003cli\u003eIdentify the challenges with data integration\u003c\/li\u003e\n\u003cli\u003eUnderstand the use of Cloud Data Fusion as a data integration platform\u003c\/li\u003e\n\u003cli\u003eCreate a Cloud Data Fusion instance,\u003c\/li\u003e\n\u003cli\u003eFamiliarize with core framework and major components in Cloud Data Fusion\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cp\u003eModule 2) Building Pipelines\u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003eUnderstand Cloud Data Fusion architecture\u003c\/li\u003e\n\u003cli\u003eDefine what a data pipeline is\u003c\/li\u003e\n\u003cli\u003eUnderstand the DAG representation of a data pipeline,\u003c\/li\u003e\n\u003cli\u003eLearn to use Pipeline Studio and its components\u003c\/li\u003e\n\u003cli\u003eDesign a simple pipeline using Pipeline Studio,\u003c\/li\u003e\n\u003cli\u003eDeploy and execute a pipeline\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cp\u003eModule 3) Designing complex pipelines\u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003ePerform branching, merging, and join operations.\u003c\/li\u003e\n\u003cli\u003eExecute pipeline with runtime arguments using macros.\u003c\/li\u003e\n\u003cli\u003eWork with error handlers.\u003c\/li\u003e\n\u003cli\u003eExecute pre- and post-pipeline executions with help of actions and notifications.\u003c\/li\u003e\n\u003cli\u003eSchedule pipelines for execution.\u003c\/li\u003e\n\u003cli\u003eImport and export existing pipelines.\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cp\u003eModule 4) Pipeline execution environment\u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003eUnderstand the composition of an execution environment.\u003c\/li\u003e\n\u003cli\u003eConfigure your pipeline’s execution environment, logging, and metrics.\u003c\/li\u003e\n\u003cli\u003eUnderstand concepts like compute profile and provisioner.\u003c\/li\u003e\n\u003cli\u003eCreate a compute profile.\u003c\/li\u003e\n\u003cli\u003eCreate pipeline alerts.\u003c\/li\u003e\n\u003cli\u003eMonitor the pipeline under execution.\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cp\u003eModule 5) Building Transformations and Preparing Data with Wrangler\u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003eUnderstand the use of Wrangler and its main components.\u003c\/li\u003e\n\u003cli\u003eTransform data using Wrangler UI.\u003c\/li\u003e\n\u003cli\u003eTransform data using directives\/CLI methods.\u003c\/li\u003e\n\u003cli\u003eCreate and use user-defined directives.\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cp\u003eModule 6) Connectors and streaming pipelines\u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003eConnectors\u003c\/li\u003e\n\u003cli\u003eDLP\u003c\/li\u003e\n\u003cli\u003eReference architecture for streaming applications Building streaming pipelines\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cp\u003eModule 7) Metadata and data lineage\u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003eList types of metadata.\u003c\/li\u003e\n\u003cli\u003eDifferentiate between business, technical, and operational metadata.\u003c\/li\u003e\n\u003cli\u003eUnderstand what data lineage is. 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