English | MP4 | AVC 1280×720 | AAC 48KHz 2ch | 2h 2m | 284 MB
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.
Table of Contents
Introduction
1 Introducing the power of Python in Excel
2 What you should know
3 Enabling Python in Excel
Introducing Excel and Python
4 Breaking down Excel and Python processes
5 Leveraging Power Query
6 Using the PY Excel function
7 Using the XL Excel function and Python variables
8 Determining calculation order
9 Importing Python libraries into Excel
10 Managing errors
11 Working with Python objects
12 Transforming DataFrame objects
13 Challenge Creating table objects in Excel
14 Solution Creating table objects in Excel
Applying Algorithms
15 Introducing AI and machine learning algorithms
16 Determining trends for linear regression with Excel functions
17 Leveraging Excel Solver for logistic regression
18 Determining trends for logistic regression with Python code
19 Grouping data with hierarchical clustering
20 Grouping data with the K-Means algorithm
21 Determining anomalies with anomaly detection algorithms
22 Challenge Running algorithms with Python in Excel
23 Solution Running algorithms with Python in Excel
Creating Visuals
24 Visualizing data
25 Leveraging Excel line charts
26 Leveraging Excel scatter plots
27 Configuring Python in Excel with dynamic parameters
28 Creating Python visuals
29 Visualizing hierarchical clustering with dendrograms
30 Breaking down time series models into components
31 Challenge Comparing time series components to anomalies
32 Solution Comparing time series components to anomalies
Conclusion
33 Continuing on with Python in Excel
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