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Python in Excel 2025 Masterclass for Data Science

Name: Python in Excel 2025 Masterclass for Data Science
Rating: 4.2 (244 reviews)

Super-exciting BRAND-NEW Excel Feature for early adopters! Use Pandas, Seaborn, Scikit-Learn & more directly in Excel!

Created byAlexander Hagmann

Last updated 12/2025

English

What you'll learn

Set up and use the brand-new Python in Excel Feature
Use powerful Python Data Science tools (Pandas, Seaborn, Scikit-learn) directly in Excel
Explanatory Data Analysis (EDA) and Data Visualization with Python in Excel
Statistics, Hypothesis Tests and Machine Learning with Python in Excel
Financial Data and Time Series Analysis with Pandas in Excel
Merging, Aggregating and Manipulating Tabular Data with Pandas in Excel
Combining the brand new Python in Excel Feature with other powerful Tools (Xlwings)

Course content

9 sections • 66 lectures • 7h 34m total length

Welcome2:03
Explore Python in Excel during this masterclass, writing Python code in Excel cells and using pandas, seaborn, scipy, statsmodels, and scikit-learn for data science tasks.
How to get the most out of this course10:22
Sneak Preview: Merging and joining Data with Pandas in Excel5:43
Explore merging and joining tabular data with Python in Excel using Pandas, demonstrated through outer, inner, left, and right joins on Beijing 2008 and London 2012 athlete data.
Python in Excel: How it works & what you need2:48
Explain how the Python in Excel feature works in the background and that code executes in the Microsoft cloud, with Anaconda installed there, requiring Microsoft 365 with Excel and internet.
Python in Excel: Features & alternative Tools (xlwings)10:17
Learn how python in excel compares to xlwings, including setup, cloud execution, data limits, and use cases, and discover how to use both tools complementarily for data science.
Important Preps before you can set up Python in Excel0:29
How to set up Python in Excel (BETA)3:11
Troubleshooting5:36

Introduction1:57
Explore the essentials of Python in Excel, outlining must-know basics and common pitfalls. Download accompanying notebooks and workbooks from resources for hands-on practice.
Simple Operations using the PY Function12:04
Python Variables vs. Excel Cell References7:14
Python Objects and Data Types12:30
Explore how Python objects and data types work in Excel, including lists, numpy arrays, integers, floats, dictionaries, pandas dataframe and series, datetime and timestamp objects, and booleans.
NoneType Objects9:17
Output Types: Python Output vs. Excel Value8:21
Extracting Data from Python Objects to Excel6:09
Diagnostics4:57
Calculation Order and Recalculation13:31
Initialization Settings and Customizations8:23
User-defined Functions and Classes3:02
Python Plots (Matplotlib & Seaborn)8:31
Learn to create Python plots in Excel with Matplotlib and Seaborn, building a pandas DataFrame from stock data, setting a datetime index, and rendering customizable price charts.

Project Introduction and Downloads3:49
Explore explanatory data analysis of a movies dataset in Excel using Python tools like Pandas, Matplotlib, and Seaborn; download, unzip, and compare Excel and Jupyter notebook code.
Instructions and Hints8:23
Excursus: Project Solution in a Jupyter Notebook3:55
Task 1: How to load and import the Dataset7:47
Import movies.csv into an Excel worksheet via the from text or CSV option, inspect budget, revenue, vote average, and runtime, and prepare for a pandas data frame.
Task 2: Initial Data Inspection13:04
Task 3: Univariate Data Analysis 18:40
Task 4: Univariate Data Analysis 25:12
Task 5: Multivariate Data Analysis 16:15
Task 6: Multivariate Data Analysis 27:27
Create pairwise regression plots with Seaborn to analyze revenue against numerical features like budget, using regression lines and confidence intervals to assess relationships.
Task 7: Multivariate Data Analysis 37:39
Analyze revenue with numerical and categorical movie features using seaborn catplots, compare mean revenues across categories, and visualize with dynamic plots while noting multicollinearity and future regression directions.

Project Introduction and Downloads0:59
Instructions and Hints6:33
Excursus: Project Solution in a Jupyter Notebook2:21
Task 1: How to load the Dataset and Initial Data Inspection6:08
Load the stocks dataset from stocks.csv into Excel, create a pandas dataframe, and set a datetime index from the date column. Use info to check missing values.
Task 2: How to create dynamic Price Charts11:03
Task 3: Dynamic Resampling and Financial Returns9:17
Task 4: Statistics and Performance Analysis9:17
Task 5: Covariance and Correlation Matrix and Heatmaps5:21

Project Introduction and Downloads1:14
Instructions and Hints5:26
Excursus: Project Solution in a Jupyter Notebook3:16
Task 1: Creating and Aggregating DataFrames9:41
Load 2008 and 2012 datasets into pandas dataframes, group by athlete and medal, unstack to gold-silver-bronze, and sort to reveal athletes in Python in Excel 2024 Masterclass for Data Science.
Task 2: Creating a tool to join/merge Datasets with full flexibility9:16

Project Introduction, Downloads and Hints9:34
Coding Solution (Part 1)7:15
Coding Solution (Part 2)4:45
Convert a NumPy array into a data frame by extracting one-hot encoded feature names from a pipeline, cleaning headers, and combining with numerical features for regression analysis and revenue insights.
Coding Solution (Part 3)9:47
Explore multiple regression analysis and hypothesis testing using ordinary least squares, addressing multicollinearity and dummy variables, interpreting p-values and regression coefficients with statsmodels and Excel.
Implementation & Feature Elimination in Excel (Part 1)12:33
Implementation & Feature Elimination in Excel (Part 2)10:42

Introduction and Downloads2:59
Set up and Installation of xlwings1:38
How to use xlwings as a Data Viewer7:24
Learn how xlwings acts as a data viewer by exporting numpy arrays and pandas dataframes to Excel, enabling full dataset inspection through created workbooks.
Data Viewer - Update1:26
How to connect to an Excel Workbook7:28
How to read and write single Values5:56
Introduction to Running Python Scripts in Excel & Downloads1:14
Installing the xlwings add-in and other preparations8:44
Install the xlwings add-in via Anaconda prompt or terminal, restart Excel, enable the developer ribbon, and verify the conda path and environment for one-time setup.
Running Python Scripts with "Run main"4:41
Run your first Python script in Excel using run main to execute a Python function defined in a module and write to a cell.
Troubleshooting4:32
Bonus Project: Introduction & Downloads7:31
Instruction and Hints6:19
Solution6:39

Requirements

A Windows desktop computer with a valid Microsoft 365 Subscription installed (MAC & Linux not supported)
An internet connection capable of streaming HD videos.
Basic Excel and Python Coding skills

Description

Are you ready to take your data analysis and visualization skills to the next level? Welcome to the "Python in Excel 2025 Masterclass for Data Science," the ultimate course that empowers Excel users to seamlessly integrate Python into their workflow for enhanced data manipulation, analysis, visualization, and machine learning.

Course Highlights:

Harness the Synergy: Dive into the future of data science by merging Excel's familiar interface with the limitless possibilities of Python programming.

Data Transformation: Learn how to effortlessly load, clean, and transform your data using Python libraries, supercharging your data preparation processes.

Advanced Analytics: Master the art of statistical analysis and machine learning within Excel using Python's powerful libraries, opening up new horizons for predictive modeling and decision-making.

Data Visualization: Create stunning charts, graphs, and interactive dashboards using Python's data visualization libraries to tell compelling data stories.

Financial Analytics: Perform more complex Finance and Investment workflows within Excel using Python's powerful libraries

Seamless Integration: Discover how to seamlessly integrate Python scripts into your Excel workbooks and automate repetitive tasks, saving you time and effort.

Combination with other powerful Tools: Complementary usage of the brand-new Python in Excel together with xlwings will boost your projects.

Who Is This Course For?

Excel enthusiasts looking to expand their skill set and explore Python's data analysis capabilities.
Data analysts, business analysts, and finance professionals wanting to leverage Python's advanced analytics tools without leaving the Excel environment.
Data science aspirants eager to gain hands-on experience in using Python for real-world data projects.
Anyone seeking to enhance their career prospects by mastering the latest data analysis techniques.

Why Choose This Course?

Up-to-date Content: Stay ahead of the curve with the latest Python integration features in Excel 2023.
Practical Learning: Dive into hands-on projects and exercises that reinforce your skills.
Expert Guidance: Benefit from the knowledge of experienced instructors who simplify complex concepts.
Certificate of Completion: Showcase your newfound skills with a Udemy certificate upon course completion.

Instructor Profile:

Your course instructor, Alexander Hagmann, is a seasoned data scientist and finance professional with >15 years of experience in both Excel and Python. He has designed this course to help you bridge the gap between Excel and Python, making data analysis and visualization more accessible and powerful than ever before.

Note: This course assumes a basic understanding of Excel and some prior knowledge of Python. A valid Microsoft 365 Subscription on a Windows machine is needed (MAC and Linux are currently not supported!)

Who this course is for:

Data Scientists and Finance Professionals seeking to use Excel as Frontend and Python as analytical Backend in their Projects.
Python Data Scientists seeking to perform their work in Excel
Frequent Excel Users seeking to boost their work results with Python Code in Excel

Python in Excel 2025 Masterclass for Data Science

What you'll learn

Explore related topics

Course content

Getting started8 lectures • 40min

Python in Excel - First Steps and Must Knows12 lectures • 1hr 36min

Project 1: Explanatory Data Analysis (EDA) with Python and Excel10 lectures • 1hr 12min

Project 2: Time Series and Financial Data Analysis with Python in Excel8 lectures • 51min

Project 3: Merging and aggregating Data with Pandas in Excel5 lectures • 29min

Project 4: Multiple Regression Analysis, Hypothesis Testing and Preprocessing6 lectures • 55min

Bonus Project: Using Python in Excel and xlwings together - the ultimate Boost13 lectures • 1hr 7min

Appendix: Installing and working with Anaconda and Jupyter Notebooks3 lectures • 37min

What´s next? (outlook and additional resources)1 lecture • 7min

Requirements

Description

Who this course is for: