Python for Excel: Use xlwings for Data Science and Finance course is a comprehensive training program about xlwings. xlwings is used to integrate Python and Excel tools to run Python code in Excel. With xlwings, candidates can use Python libraries like Numpy, Pandas, Scipy, Matplotlib, Seaborn, and Scikit-learn in Excel.
The course is suitable for Python beginners as well as experienced developers who want to integrate Python with Excel to boost their productivity. The course includes a crash course on the basics of Python programming language. The course covers 3 comprehensive real-world projects. The course is usually available for INR 3,299 on Udemy but you can click now to get the Python for Excel: Use xlwings for Data Science and Finance Course for INR 499.
Who all can opt for this course?
- Data Scientists and Finance Professionals looking forward to using Excel as Frontend and Python as the backend in their projects.
- Professionals seeking to write Excel tools with clean Python code instead of VBA/Marcos.
- Python Beginners
- Python Developers seeking to work with Excel as GUI (Graphical User Interface).
Course Highlights
| Key Highlights | Details |
|---|---|
| Registration Link | Apply Now! |
| Price | INR 449 ( |
| Duration | 16 hours |
| Rating | 4.4/5 |
| Student Enrollment | 7,622 students |
| Instructor | Alexander Hagmann https://www.linkedin.com/in/alexanderhagmann |
| Topics Covered | xlwings installation and setup, running Python scripts in Excel, Matplotlib & Seaborn with Excel, etc. |
| Course Level | Beginner |
| Total Student Reviews | 738 |
Learning Outcomes
- Automate Excel functions with clean Python code
- Learn and master the xlwings library from 0 to 100
- Utilize Excel as a Graphical User Interface (GUI) and use it to execute Python code
- Create robust dashboard applications using Python and Excel’s frontend (backend)
- Utilize Excel’s powerful data visualization tools (Matplotlib, Seaborn)
- A customized crash course will teach you Python from scratch (for Python beginners)
- Utilize Numpy, Pandas, and Machine Learning Libraries directly in Excel by creating UDFs (user-specified functions)
- Python may be used to create Excel tools instead of VBA, and Excel can use your code directly
- Python can automate Excel reports with xlwings
- Web apps in prototype
- With xlwings, you can create and use dynamic arrays
- With a Python Monte Carlo Simulation, run your financial model 10,000 times or more
- Directly import financial data into Excel from Web APIs
- Using Run main and RunPython, Excel users can execute Python scripts
- Python code is a powerful and clean replacement for VBA macros
Course Content
| S.No. | Module (Duration) | Topics |
|---|---|---|
| 1. | Getting Started (44 minutes) | Introduction (don´t skip!) |
| Course Overview (don´t skip!) | ||
| Tips: How to get the most out of this Course (don´t skip!) | ||
| FAQ / Your Questions answered | ||
| How to download and install Anaconda for Python coding | ||
| Jupyter Notebooks – let´s get started | ||
| How to work with Jupyter Notebooks | ||
| 2. | First Steps with xlwings (Reading and Writing Elements) (45 minutes) | Introduction and Downloads |
| How to install xlwings | ||
| How to use xlwings as a Data Viewer | ||
| Data Viewer – Update | ||
| How to connect to an Excel Workbook | ||
| How to read and write single Values | ||
| How to assign a name | ||
| How to write Excel Functions with Python | ||
| Range Shortcuts | ||
| Case Study – Bringing it all together | ||
| Homework | ||
| 3. | Reading and writing many Values (26 minutes) | Section Downloads |
| One-dimensional Data Structures | ||
| How to write Values vertically | ||
| Rows and Columns (1dim vs. 2dim) | ||
| How to read two-dimensional Data Structures | ||
| Advanced Reading with expand | ||
| How to write two-dimensional Data Structures | ||
| Range Indexing and Slicing | ||
| Efficiency | ||
| Homework | ||
| 4. | Project 1: Monte Carlo Simulations in Excel with Python (Part 1) (46 minutes) | Introduction |
| Section Downloads | ||
| The Excel Model explained (Part 1) | ||
| The Excel Model explained (Part 2) | ||
| Running a simple Monte Carlo Simulation | ||
| A more advanced and realistic Monte Carlo Simulation | ||
| Final Considerations | ||
| 5. | Running Python Scripts in Excel – RunPython (50 minutes) | Introduction and Downloads |
| Installing the xlwings add-in and other preparations | ||
| Running Python Scripts with “Run main” | ||
| Troubleshooting (Part 1) | ||
| All you need to know about VBA Macros | ||
| Running Python Scripts with “RunPython” | ||
| Troubleshooting (Part 2) | ||
| Run main vs RunPython | ||
| Excursus: Converting Jupyter Notebooks to .py | ||
| Homework | ||
| 6. | Project 1: Monte Carlo Simulations in Excel with Python (Part 2) (17 minutes) | Introduction and Downloads |
| Monte Carlo Simulation with RunPython (Part 1) | ||
| Monte Carlo Simulation with RunPython (Part 2) | ||
| 7. | Using Matplotlib and Seaborn in Excel with xlwings (29 minutes) | Introduction and Downloads |
| How to write a Matplotlib Plot into Excel | ||
| How to update the Plot | ||
| How to change Size and Position (Part 1) | ||
| How to change Size and Position (Part 2) | ||
| How write a Seaborn Plot into Excel | ||
| How to create Excel Charts with Python | ||
| Homework: Adding a Plot to the Monte Carlo Simulation (Project 1) | ||
| 8. | Project 2: Build Dashboard Apps with Excel (GUI) and Python (analytical backend) (01 hour 01 minutes) | Introduction and Downloads |
| IMPORTANT NOTICE (Update January 23) | ||
| Stock Performance Analysis during COVID-19 with Python & Pandas (Part 1) | ||
| Stock Performance Analysis during COVID-19 with Python & Pandas (Part 2) | ||
| Stock Performance Analysis during COVID-19 with Python & Pandas (Part 3) | ||
| Building a Stock Performance Dashboard App (Part 1) | ||
| Building a Stock Performance Dashboard App (Part 2) | ||
| Improving the Source Code and Errors | ||
| 9. | Reading and Writing Data Structures (Numpy, Pandas) & Converters (59 minutes) | Section Downloads |
| (Default) Converters | ||
| The Numpy Converter | ||
| The Dictionary Converter | ||
| The DataFrame Converter (Part 1) | ||
| The DataFrame Converter (Part 2) | ||
| Data Science Application: Inspecting and Manipulating DataFrames in Excel | ||
| The Pandas Series Converter | ||
| Excursus: How to load Data from Excel into Pandas with pd.read_excel() | ||
| Excursus: Advanced import with pd.read_excel() | ||
| Excursus: How to load Financial Data / Time Series with pd.read_excel() | ||
| 10. | User-defined Functions (UDF) and Dynamic Arrays with xlwings (Windows only) (58 minutes) | Introduction and Downloads |
| Preparations and your first UDF | ||
| How to change the Name and Location of the Python Module | ||
| Troubleshooting (UDF) | ||
| UDFs – Behind the Scenes | ||
| More complex UDFs and the @xw.arg Decorator | ||
| How to create Numpy UDFs | ||
| UDFs and Array Formulas | ||
| How to create Dynamic Arrays with xlwings UDFs | ||
| How to create Pandas UDFs | ||
| How to add Docstrings | ||
| Homework | ||
| 11. | Project 3: Use Pandas UDFs in Excel for Data Science and Finance (Windows only) (38 minutes) | Introduction and Downloads |
| IMPORTANT NOTICE (Update January 23) | ||
| How to load Financial Data from the Web into Excel with the DataReader UDF | ||
| How to resample Time Series in Excel with the resample UDF | ||
| How to calculate Financial Returns with a Pandas/Numpy UDF | ||
| How to get Summary Statistics of a Dataset with the describe UDF | ||
| How to create a Dataset´s Correlation Matrix with the corr UDF | ||
| Taken all together – the Super UDF | ||
| How to perform inner/outer/left/right joins with the merge UDF | ||
| 12. | APPENDIX 1: Python Crash Course for Excel Users (04 hours 45 minutes) | Introduction and Overview |
| Section Downloads | ||
| Intro to the Time Value of Money (TVM) Concept (Theory) | ||
| Calculate Future Values (FV) with Python / Compounding | ||
| Calculate Present Values (FV) with Python / Discounting | ||
| Interest Rates and Returns (Theory) | ||
| Calculate Interest Rates and Returns with Python | ||
| Introduction to Variables | ||
| Excursus: How to add inline comments | ||
| Variables and Memory (Theory) | ||
| More on Variables and Memory | ||
| Variables – Dos, Don´ts and Conventions | ||
| The print() Function | ||
| Coding Exercise 1 | ||
| TVM Problems with many Cashflows | ||
| Intro to Python Lists | ||
| Zero-based Indexing and negative Indexing in Python (Theory) | ||
| Indexing Lists | ||
| For Loops – Iterating over Lists | ||
| The range Object – another Iterable | ||
| Calculate FV and PV for many Cashflows | ||
| The Net Present Value – NPV (Theory) | ||
| Calculate an Investment Project´s NPV | ||
| Coding Exercise 2 | ||
| Data Types in Action | ||
| The Data Type Hierarchy (Theory) | ||
| Excursus: Dynamic Typing in Python | ||
| Build-in Functions | ||
| Integers | ||
| Floats | ||
| How to round Floats (and Integers) with round() | ||
| More on Lists | ||
| Lists and Element-wise Operations | ||
| Slicing Lists | ||
| Slicing Cheat Sheet | ||
| Changing Elements in Lists | ||
| Sorting and Reversing Lists | ||
| Adding and removing Elements from/to Lists | ||
| Mutable vs. immutable Objects (Part 1) | ||
| Mutable vs. immutable Objects (Part 2) | ||
| Coding Exercise 3 | ||
| Tuples | ||
| Dictionaries | ||
| Intro to Strings | ||
| String Replacement | ||
| Booleans | ||
| Operators (Theory) | ||
| Comparison, Logical and Membership Operators in Action | ||
| Coding Exercise 4 | ||
| Conditional Statements | ||
| Keywords pass, continue and break | ||
| Calculate a Project´s Payback Period | ||
| Defining your first user-defined Function | ||
| What´s the difference between Positional Arguments vs. Keyword Arguments? | ||
| How to work with Default Arguments | ||
| Coding Exercise 5 | ||
| 13. | APPENDIX 2: Matplotlib, Numpy, Pandas and Seaborn Crash Course (03 hours 26 minutes) | Downloads for this Section |
| Matplotlib Introduction | ||
| Line Plots | ||
| Scatter Plots | ||
| Customizing Plots (Part 1) | ||
| Customizing Plots (Part 2) | ||
| Coding Exercise 6 | ||
| Modules, Packages and Libraries – No need to reinvent the Wheel | ||
| Numpy Arrays | ||
| Indexing and Slicing Numpy Arrays | ||
| Vectorized Operations with Numpy Arrays | ||
| Changing Elements in Numpy Arrays & Mutability | ||
| View vs. copy – potential Pitfalls when slicing Numpy Arrays | ||
| Numpy Array Methods and Attributes | ||
| Numpy Universal Functions | ||
| Boolean Arrays and Conditional Filtering | ||
| Coding Exercise 7 | ||
| How to work with nested Lists | ||
| 2-dimensional Numpy Arrays | ||
| How to slice 2-dim Numpy Arrays (Part 1) | ||
| How to slice 2-dim Numpy Arrays (Part 2) | ||
| Recap: Changing Elements in a Numpy Array / slice | ||
| How to perform row-wise and column-wise Operations | ||
| Coding Exercise 8 | ||
| Intro to Tabular Data / Pandas | ||
| Create your very first Pandas DataFrame (from csv) | ||
| Pandas Display Options and the methods head() & tail() | ||
| First Data Inspection | ||
| Coding Exercise 9 | ||
| Selecting Columns | ||
| Selecting one Column with the “dot notation” | ||
| Zero-based Indexing and Negative Indexing | ||
| Selecting Rows with iloc (position-based indexing) | ||
| Slicing Rows and Columns with iloc (position-based indexing) | ||
| Position-based Indexing Cheat Sheets | ||
| Selecting Rows with loc (label-based indexing) | ||
| Slicing Rows and Columns with loc (label-based indexing) | ||
| Label-based Indexing Cheat Sheets | ||
| Summary, Best Practices and Outlook | ||
| Coding Exercise 10 | ||
| First Steps with Pandas Series | ||
| First Steps with Pandas Index Objects | ||
| Importing Time Series Data from csv-files | ||
| Initial Analysis / Visualization of Time Series | ||
| Seaborn Introduction | ||
| 14. | What´s next? (outlook and additional resources) (04 minutes) | Bonus Lecture |
Resources Required
- A desktop computer capable of storing and running Python/Anaconda.
- Stable internet connection
- Microsoft Excel
Featured Review
Alexander Meneikis (5/5): This is a GREAT course, hands-on, practical, with relevant examples that pretty much anyone can do. He claims that his introduction to Python is the best one that exists. It certainly is one of the best I have seen.
Pros
- Mark Lutton (5/5): It was my introduction to Anaconda which is an excellent development environment.
- Elina Pulke (5/5): Very good course for Finance Professionals who work with Excel every day.
- Jim Powell (5/5): Great course for learning how to use python to power excel workbooks! Also a good introduction to numpy, pandas, and matplotlib.
- Kamil Wilczak (5/5): It’s a really great course and I have learnt a lot.
Cons
- Alok T. (3.5/5): good content, informative but little bit slow
- CH K. (3/5): the accent is really really weird and too difficult to understand
- Rajarshi B. (3/5): The topic covered is important in Python and Excel, but the course delivery can be more engaging.
- Brian M. (1/5): Too much time spent on topics not related to xlwings. Too much content labeled as “appendix” was the majority of the length of the course. Free YouTube content is significantly better than this. My rating is this low even after purchasing the course at a steep discount.
About the Author
The instructor of this course is Alexander Hagmann. He is a Data Scientist, Finance Professional, and Entrepreneur. With a 4.6 instructor rating and 10,535 reviews on Udemy, he offers 16 courses and has taught 91,211 students so far.
- Alexander has more than 10 years of experience in the finance and investment industry as a data scientist and finance professional.
- Additionally, he is a top-rated Udemy instructor for data analysis and manipulation using pandas.
- Alexander began his career in the conventional finance industry before advancing gradually into data-driven and AI-driven finance roles.
- He presently develops solutions for Robo Investing and Algorithmic Trading while working on cutting-edge Fintech initiatives.
- His graduates hold positions in some of the biggest and most well-known IT and banking firms worldwide.
- One thing all of Alexander’s courses have in common is their practical, real-world-proven content and principles.
- The emphasis is clearly on understanding concepts and developing abilities rather than memorization.
- Alexander has a Master’s in Finance and has successfully completed all three CFA Exams (he is currently no active member of the CFA Institute).
Comparison Table
| Parameters | Python for Excel: Use xlwings for Data Science and Finance | Performance Optimization and Risk Management for Trading | The Complete Pandas Bootcamp 2023: Data Science with Python |
|---|---|---|---|
| Offers | INR 455 ( | INR 455 ( | INR 455 ( |
| Duration | 16.5 hours | 19.5 hours | 34 hours |
| Rating | 4.4/5 | 4.3/5 | 4.7/5 |
| Student Enrollments | 7,624 | 4,413 | 22,206 |
| Instructors | Alexander Hagmann | Alexander Hagmann | Alexander Hagmann |
| Register Here | Apply Now! | Apply Now! | Apply Now! |
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