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Attendees will need to know how to use Microsoft Windows to edit and copy files both in Windows Explorer and via a command prompt. Prior programming experience is not needed.\u003c\/p\u003e\n\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003c\/div\u003e\u003cdiv\u003e\u003ch3\u003ePython Training for Non-Programmers Course Outline\u003c\/h3\u003e\u003c\/div\u003e\u003cdiv\u003e\n\u003ch4\u003eChapter 1 – Starting to Program\u003c\/h4\u003e\n\u003cp\u003ePrinciples of Programming\u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003eHow computers solve problems\u003c\/li\u003e\n\u003cli\u003eLanguage types and evolution\u003c\/li\u003e\n\u003cli\u003eProcedural logic\u003c\/li\u003e\n\u003cli\u003eObject Orientation\u003c\/li\u003e\n\u003cli\u003eBugs and other challenges\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cp\u003eSyntax and Semantics\u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003eAbout Python\u003c\/li\u003e\n\u003cli\u003eStatements and comments\u003c\/li\u003e\n\u003cli\u003eLiterals, Variables and Data Types\u003c\/li\u003e\n\u003cli\u003eCollection Types\u003c\/li\u003e\n\u003cli\u003eExpressions and 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in\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cp\u003eRepeating Program Logic with Loops\u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003eCounting loops and for\/Range\u003c\/li\u003e\n\u003cli\u003eFor-each Loops\u003c\/li\u003e\n\u003cli\u003eIterating a List\u003c\/li\u003e\n\u003cli\u003eLoop control\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cp\u003eWriting and Calling Functions\u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003eFunction definition\u003c\/li\u003e\n\u003cli\u003ereturn statement\u003c\/li\u003e\n\u003cli\u003eAccepting parameters\u003c\/li\u003e\n\u003cli\u003eReturning results\u003c\/li\u003e\n\u003cli\u003eImporting modules and functions\u003c\/li\u003e\n\u003cli\u003eCross-module calls\u003c\/li\u003e\n\u003cli\u003eCalling library functions\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003c\/div\u003e\u003cdiv\u003e\n\u003ch4\u003eChapter 4 – Object-Oriented Programming\u003c\/h4\u003e\n\u003cp\u003eWhy Object Oriented?\u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003eChallenges with 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packages.\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\u003eExperience with another procedural or object-oriented programming language, such as C, C++, Java, or VB.NET\u003c\/li\u003e\n\u003cli\u003eFamiliarity with concepts, such as variables, loops, and branches with some experience using a text editor to edit program code\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003c\/div\u003e\u003cdiv\u003e\u003ch3\u003eIntroduction to Python Instructor-Led Course Outline\u003c\/h3\u003e\u003c\/div\u003e\u003cdiv\u003e\n\u003ch4\u003eModule 1: Python Overview\u003c\/h4\u003e\n\u003cp\u003eIn this module, you learn about:\u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003eEnter statements into the Python Console\u003c\/li\u003e\n\u003cli\u003eIdentify and access 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VA","offer_id":48805407817947,"sku":"US-1905-IL","price":2228.0,"currency_code":"USD","in_stock":true},{"title":"269D35US \/ 2026-09-02T09:00:00 \/ Online","offer_id":48837146968283,"sku":"US-1905-IL","price":2228.0,"currency_code":"USD","in_stock":true},{"title":"269D36US \/ 2026-09-09T09:00:00 \/ Online","offer_id":48837147001051,"sku":"US-1905-IL","price":2228.0,"currency_code":"USD","in_stock":true},{"title":"269D37US \/ 2026-09-16T09:00:00 \/ Online","offer_id":48837147033819,"sku":"US-1905-IL","price":2228.0,"currency_code":"USD","in_stock":true},{"title":"269D38US \/ 2026-09-23T09:00:00 \/ Online","offer_id":48837147066587,"sku":"US-1905-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 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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 using Association Rules\u003c\/li\u003e\n\u003cli\u003eEvaluating the strength of these models through probability measures of support, confidence, and lift\u003c\/li\u003e\n\u003cli\u003eEmploying feature engineering approaches to improve the models\u003c\/li\u003e\n\u003cli\u003eBuilding a recommender for your customers that is unique to your product\/service offering\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003c\/div\u003e\u003cdiv\u003e\n\u003ch4\u003eModule 9: Network Analysis for Organizational Insights\u003c\/h4\u003e\n\u003cul\u003e\n\u003cli\u003eAnalyzing your organization, its people, and its environment as a network of inter-relationships\u003c\/li\u003e\n\u003cli\u003eVisualizing these relationships to uncover previously unseen business insights\u003c\/li\u003e\n\u003cli\u003eExploring ego-centric and socio-centric methods of analyzing connections critical to your organization\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003c\/div\u003e\u003cdiv\u003e\n\u003ch4\u003eModule 10: Big 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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 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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 \/ 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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":"advanced-python-best-practices-and-design-patterns","title":"Advanced Python: Best Practices and Design Patterns","description":"\u003cdiv\u003e\n\u003cp\u003eThis \u003cstrong\u003eadvanced Python training course\u003c\/strong\u003e will expand your foundational Python programming skills to build reliable and stable applications. In this course, you will learn how to:\u003c\/p\u003e\r\n\u003cul\u003e\r\n\u003cli\u003eEmploy design patterns and best practices in Python applications\u003c\/li\u003e\r\n\u003cli\u003eExploit the object-oriented programming features in Python for stable, reliable programs\u003c\/li\u003e\r\n\u003cli\u003eCreate and manage concurrent threads of control\u003c\/li\u003e\r\n\u003cli\u003eGenerate and consume \u003cabbr title=\"REpresentational State Transfer\"\u003eREST\u003c\/abbr\u003e web service requests and responses\u003c\/li\u003e\r\n\u003cli\u003eImplement Gang of Four (\u003cabbr title=\"Gang Of Four\"\u003eGoF\u003c\/abbr\u003e) design patterns to solve commonly recurring software design problems\u003c\/li\u003e\r\n\u003c\/ul\u003e\n\u003c\/div\u003e\u003cdiv\u003e\n\u003ch3\u003eAdvanced Python: Best Practices and Design Patterns Benefits\u003c\/h3\u003e\n\u003cul\u003e\n\u003cli\u003e\u003cp\u003eUnit test, debug, and install Python programs and modules\u003c\/p\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cp\u003eProfile program execution and improve performance\u003c\/p\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cp\u003eApply advanced Python programming features for efficient, reliable, and maintainable programs\u003c\/p\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cp\u003eGain knowledge and skills applicable to all Python environments, including Microsoft Windows, macOS, and all Linux and UNIX distributions\u003c\/p\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cp\u003eTest your knowledge in the included end-of-course exam\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\u003eAdvanced Python Course Outline\u003c\/h3\u003e\u003c\/div\u003e\u003cdiv\u003e\n\u003ch4\u003eModule 1: Object-Oriented Programming in Python\u003c\/h4\u003e\n\u003cp\u003eIn this module, you will learn how to:\u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003eExtend classes to define subclasses\u003c\/li\u003e\n\u003cli\u003eAdd properties to a class\u003c\/li\u003e\n\u003cli\u003eDefine abstract base classes\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003c\/div\u003e\u003cdiv\u003e\n\u003ch4\u003eModule 2: Exploring Python Features\u003c\/h4\u003e\n\u003cp\u003eIn this module, you will learn how to:\u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003eWrite \"Pythonic\" code\u003c\/li\u003e\n\u003cli\u003eModify code dynamically with monkey patching\u003c\/li\u003e\n\u003cli\u003eProcess large data structures efficiently with generators\u003c\/li\u003e\n\u003cli\u003eHandle exceptions\u003c\/li\u003e\n\u003cli\u003eRaise user-defined exceptions\u003c\/li\u003e\n\u003cli\u003eReduce code complexity with context managers and the \"with\" statement\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003c\/div\u003e\u003cdiv\u003e\n\u003ch4\u003eModule 3: Verifying Code and Unit Testing\u003c\/h4\u003e\n\u003cp\u003eIn this module, you will learn how to:\u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003eDevelop and run Python unit tests\u003c\/li\u003e\n\u003cli\u003eSimplify automated testing with the Pytest package\u003c\/li\u003e\n\u003cli\u003eVerify code behavior\u003c\/li\u003e\n\u003cli\u003eMock dependent objects with the Mock package\u003c\/li\u003e\n\u003cli\u003eUse mock objects to verify code behavior when exceptions occur\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003c\/div\u003e\u003cdiv\u003e\n\u003ch4\u003eModule 4: Detecting Errors and Debugging Techniques\u003c\/h4\u003e\n\u003cp\u003eIn this module, you will learn how to:\u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003eLog messages for auditing and debugging\u003c\/li\u003e\n\u003cli\u003eCheck your code for potential bugs with Pylint and Flake8\u003c\/li\u003e\n\u003cli\u003eDebug your Python code\u003c\/li\u003e\n\u003cli\u003eExtract error information from exceptions\u003c\/li\u003e\n\u003cli\u003eTrace program execution with the \u003ca href=\"https:\/\/www.jetbrains.com\/pycharm\/\" title=\"PyCharm: the Python IDE for Professional Developers by JetBrains\" target=\"_blank\" rel=\"external nofollow noopener\"\u003ePyCharm \u003cabbr title=\"Integrated Development Environment\"\u003eIDE\u003c\/abbr\u003e\u003c\/a\u003e\n\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003c\/div\u003e\u003cdiv\u003e\n\u003ch4\u003eModule 5: Implementing Python Design Patterns\u003c\/h4\u003e\n\u003cp\u003eIn this module, you will learn how to:\u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003eImplement the Decorator pattern using @decorator\u003c\/li\u003e\n\u003cli\u003eControl access to an object with the Proxy pattern\u003c\/li\u003e\n\u003cli\u003eLay out a skeleton algorithm in the Template Method pattern\u003c\/li\u003e\n\u003cli\u003eEnable loose coupling between classes with the Observer pattern\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003c\/div\u003e\u003cdiv\u003e\n\u003ch4\u003eModule 6: Interfacing with REST Web Services and Clients\u003c\/h4\u003e\n\u003cp\u003eIn this module, you will learn how to:\u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003eBuild a REST service\u003c\/li\u003e\n\u003cli\u003eGenerate \u003cabbr title=\"JavaScript Object Notation\"\u003eJSON\u003c\/abbr\u003e responses to support Ajax clients\u003c\/li\u003e\n\u003cli\u003eSend REST requests from a Python client\u003c\/li\u003e\n\u003cli\u003eConsume JSON and \u003cabbr title=\"eXtensible Markup Language\"\u003eXML\u003c\/abbr\u003e response data\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003c\/div\u003e\u003cdiv\u003e\n\u003ch4\u003eModule 7: Measuring and Improving Application Performance\u003c\/h4\u003e\n\u003cp\u003eIn this module, you will learn how to:\u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003eTime execution of functions with the \"timeit\" module\u003c\/li\u003e\n\u003cli\u003eProfile program execution using \"cProfile\"\u003c\/li\u003e\n\u003cli\u003eManipulate an execution profile interactively with \"pstats\"\u003c\/li\u003e\n\u003cli\u003eEfficiently apply data structures, including lists, dictionaries, and tuples\u003c\/li\u003e\n\u003cli\u003eMap and filter data sets using comprehensions\u003c\/li\u003e\n\u003cli\u003eReplace the standard Python interpreter with PyPy\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003c\/div\u003e\u003cdiv\u003e\n\u003ch4\u003eModule 8: Installing and Distributing Modules\u003c\/h4\u003e\n\u003cp\u003eIn this module, you will learn how to:\u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003eInstall modules from the \u003ca href=\"https:\/\/pypi.org\/\" title=\"PyPI: The Python Package Index\" target=\"_blank\" rel=\"external nofollow noopener\"\u003ePyPi\u003c\/a\u003e repository using \"pip\"\u003c\/li\u003e\n\u003cli\u003ePackage Python modules and applications\u003c\/li\u003e\n\u003cli\u003eEstablish isolated Python environments with the \"venv\" 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