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Python in Excel: Data Outputs in Custom Data Visualizations and Algorithms

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Excel is a powerful tool for data and business analysis, and Python is one of the world’s most popular and dynamic programming languages. Python in Excel works as a sandbox environment. It enables developers and business users to test small parts of code by creating visuals and running algorithms on existing data. In this course, data analytics and business analysis expert Helen Wall focuses on how Python can expand the existing capabilities of Excel. Explore the process and framework of setting up Python to create DataFrame objects and other outputs in Excel. Dive into ways you can use these outputs and objects in custom data visualizations and algorithms that Excel does not have natively, but which Python can create with code. This course highlights ways you can harness the strengths of both Excel and Python in one interface.


01 - Introduction
  • 01 - Introducing the power of Python in Excel
  • 02 - What you should know
  • 03 - Enabling Python in Excel

  • 02 - 1. Introducing Excel and Python
  • 01 - Breaking down Excel and Python processes
  • 02 - Leveraging Power Query
  • 03 - Using the PY Excel function
  • 04 - Using the XL Excel function and Python variables
  • 05 - Determining calculation order
  • 06 - Importing Python libraries into Excel
  • 07 - Managing errors
  • 08 - Working with Python objects
  • 09 - Transforming DataFrame objects
  • 10 - Challenge Creating table objects in Excel
  • 11 - Solution Creating table objects in Excel

  • 03 - 2. Applying Algorithms
  • 01 - Introducing AI and machine learning algorithms
  • 02 - Determining trends for linear regression with Excel functions
  • 03 - Leveraging Excel Solver for logistic regression
  • 04 - Determining trends for logistic regression with Python code
  • 05 - Grouping data with hierarchical clustering
  • 06 - Grouping data with the K-Means algorithm
  • 07 - Determining anomalies with anomaly detection algorithms
  • 08 - Challenge Running algorithms with Python in Excel
  • 09 - Solution Running algorithms with Python in Excel

  • 04 - 3. Creating Visuals
  • 01 - Visualizing data
  • 02 - Leveraging Excel line charts
  • 03 - Leveraging Excel scatter plots
  • 04 - Configuring Python in Excel with dynamic parameters
  • 05 - Creating Python visuals
  • 06 - Visualizing hierarchical clustering with dendrograms
  • 07 - Breaking down time series models into components
  • 08 - Challenge Comparing time series components to anomalies
  • 09 - Solution Comparing time series components to anomalies

  • 05 - Conclusion
  • 01 - Continuing on with Python in Excel
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    تاریخ انتشار: ۷ دی ۱۴۰۳
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