Python for Excel: Use xlwings for Data Science and Finance

Integrate Excel and Python and get the best of two Worlds! Python Beginners welcome. Use Pandas, Seaborn & co. in Excel

4.56 (1078 reviews)

Udemy

platform

English

language

Microsoft

What you will learn

Automate Excel with clean and powerful Python Code

Learn and master the xlwings library from 0 to 100

Use Excel as Graphical User Interface (GUI) and run your Python code with Excel

Create powerful Dashboard Apps with Excel (frontend) and Python (backend)

Use powerful Data Visualization Tools (Matplotlib, Seaborn) in Excel

Learn Python from scratch with a taylor-made Crash Course (For Python beginners)

Write UDFs (user defined functions) and use Numpy, Pandas and Machine Learning Libraries directly in Excel

Write Excel tools with Python instead of VBA and call your code directly from within Excel

Use xlwings to automate Excel reports with Python

Prototype Web apps

Write and use Dynamic Arrays with xlwings

Run your financial model 10,000 times & more with a Python Monte Carlo Simulation

Load (financial) data from Web APIs directly into Excel

Run Python Scripts from within Excel with Run main and RunPython

Replace VBA macros with clean and powerful Python code

Charts

Students

Price

Rating & Reviews

Enrollment Distribution

Comidoc Review

Our Verdict

This course provides good value with a strong emphasis on combining Excel's user-friendly interface and Python's powerful features through the xlwings library. While there are areas for improvement, data analysts and financial professionals will find this advanced introduction helpful for workload automation and enhancing productivity.

What We Liked

Comprehensive coverage of Python for Excel integration using the xlwings library
Instructor's clear teaching style and helpful crash course on Python
Useful for data analysts, financial analysts, and accountants seeking to automate tasks
Exposure to useful libraries like Pandas, Seaborn, Numpy, and Matplotlib
Easy-to-understand implementation of Monte Carlo simulation

Potential Drawbacks

Content organization could be improved with more practical examples and fewer theoretical ones
Some outdated references to libraries, causing challenges for learners
Limited focus on advanced xlwings features, hindering deeper understanding
Occasional technical issues affecting overall learning experience