Python for Excel: Use xlwings for Data Science and Finance from Udemy

Newly added in Oct 2023: The NEW Python in Excel Feature.

(Python in Excel and xlwings are complementary tools - get the best of both tools. )

Excel vs. Python - what is the best tool for Data Science, Business, and Finance?

The answer is: Use Excel and Python together and integrate both tools with xlwings. Get the best of two worlds.

With xlwings, you can use Python Data Science libraries like Numpy, Pandas, Scipy, Matplotlib, Seaborn, and Scikit-learn directly in Excel. You can run Python code in Excel and boost your Excel projects. More and more Professionals and Developers use

Excel as Frontend

Python as analytical Backend.

This course is the perfect choice for

Experienced Python Coders: Use Excel as Graphical User Interphase (GUI) | Run your Python scripts with Excel | Present your results with Excel Dashboards

Excel Users and complete Python Beginners: Boost your Excel projects with clean and powerful Python code.

Mixed Groups: Non-Coders can run and use Python code simply by clicking on buttons in Excel.

Why take this course?

You will learn and master the xlwings library from scratch
For Excel Users and complete Python Beginners: This course includes a Python Crash Course that is tailor-made for you.
It´s the most comprehensive and practical (hands-on) xlwings course on the web
It covers three comprehensive real-world projects.
Project 1: You will learn how to boost your financial model in Excel by adding a Python Monte Carlo Simulation - Run your Excel calculation 10,000 times with different sets of inputs and analyze the results.
Project 2: You will learn to create Bloomberg-like Stock Dashboard Apps with Excel (Graphical User Interface) and Python (analytical Backend).
Project 3: You will learn how to use Pandas methods and functions on your datasets directly in Excel.

Why use Excel?

There is no better Graphical User Interface (GUI) and Reporting tool than Excel. Excel is

widely spread (750 million users)
standardized
intuitive to use
most users are well-trained
it requires low/zero setup
it requires low/zero maintenance
and it´s still the best choice for financial models & spreadsheet calculations

Why use Python?

With hundreds of powerful Libraries, Python is the first choice for Data Science, Machine Learning, and advanced analytics in Business and Finance. The Python Ecosystem is way more powerful and versatile than VBA. And it´s cleaner and easier to learn and apply.

Why learn and master xlwings?

xlwings is the perfect tool to integrate Excel and Python. xlwings allows you to

Automate Excel from Python e.g. to produce reports or to interact with Jupyter Notebooks.
Write macros in Python that you can run from buttons in Excel, e.g. to load data from a database or an external API.
Write UDFs (user-defined functions) and leverage the power from NumPy, Pandas, and machine learning libraries.
Leverage Python's scientific stack for interactive data analysis using Jupyter Notebooks, NumPy, Pandas, scikit-learn, etc.
Use xlwings to automate Excel reports with Python.
Write Excel tools with Python instead of VBA and call your code directly from within Excel, e.g. via a button on the sheet.
This also works great for prototyping web apps.
Write (array) UDFs in a breeze by taking advantage of all the functionality already available in libraries like NumPy and Pandas.
Dynamic array formulas are supported.

As always, there is no risk for you as I offer a 30-day money-back guarantee. I am looking forward to seeing you in the course.

(Mac users are welcome. However, please note that 10%-15% of the course content (UDFs) is currently unavailable on Mac. )

What's inside

Learning objectives

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
Show more
Show less

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
Show more
Show less

Syllabus

Getting Started

Introduction (don´t skip!)

Course Overview (don´t skip!)

Tips: How to get the most out of this Course (don´t skip!)

NEW: xlwings vs. the new Python in Excel Feature (added in August 2023)

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

First Steps with xlwings (Reading and Writing Elements)

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

Reading and writing many Values

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

Project 1: Monte Carlo Simulations in Excel with Python (Part 1)

Introduction

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

Running Python Scripts in Excel - RunPython

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

Project 1: Monte Carlo Simulations in Excel with Python (Part 2)

Monte Carlo Simulation with RunPython (Part 1)

Monte Carlo Simulation with RunPython (Part 2)

Using Matplotlib and Seaborn in Excel with xlwings

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)

Project 2: Build Dashboard Apps with Excel (GUI) and Python (analytical backend)

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

Reading and Writing Data Structures (Numpy, Pandas) & Converters

(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()

User-defined Functions (UDF) and Dynamic Arrays with xlwings (Windows only)

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

Project 3: Use Pandas UDFs in Excel for Data Science and Finance (Windows only)

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Integrates Python into Excel, creating a powerful environment for data analysis and visualization

Designed for learners with a wide range of experience levels, from Python beginners to experienced Excel users

Covers practical and real-world applications through three comprehensive projects, providing hands-on experience

Offers a comprehensive and practical approach to leveraging the power of Python and Excel together for data science, business, and finance professionals

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Python for Excel: Use xlwings for Data Science and Finance with these activities:

Review xlwings Module

Show steps

Review the xlwings module to build a stronger foundation for the course.

Show steps

Visit the xlwings documentation website
Review the xlwings module overview
Install the xlwings module

Basic Python Exercises

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Complete basic Python exercises to improve your understanding of the language and prepare for the course.

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Set up a Python development environment
Practice writing simple Python programs
Solve simple coding challenges

NumPy Tutorial

Show steps

Complete a NumPy tutorial to become familiar with its features and capabilities.

Browse courses on NumPy

Show steps

Visit the NumPy website or documentation
Review the NumPy basics
Practice using NumPy arrays

Five other activities

Expand to see all activities and additional details

Show all eight activities

Pandas Exercises

Show steps

Complete Pandas exercises to gain proficiency in data manipulation and analysis.

Browse courses on Pandas

Show steps

Set up a Pandas development environment
Practice creating and manipulating DataFrames
Solve data analysis challenges using Pandas

Course Project Proposal

Show steps

Create a course project proposal to outline your plans for a project that will apply the skills and knowledge learned in the course.

Show steps

Identify a project topic
Define the project goals
Outline the project methodology
Identify potential resources

Kaggle Competition

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Participate in a Kaggle competition to apply your skills and knowledge in a real-world setting.

Browse courses on Kaggle

Show steps

Identify a suitable Kaggle competition
Download the competition data
Build and train a model
Submit your results

Mentoring Junior Students

Show steps

Share your knowledge and skills by mentoring junior students in the subject area.

Show steps

Identify potential mentees
Establish a mentoring relationship
Provide guidance and support
Monitor progress and provide feedback

Contribute to Open-Source Project

Show steps

Contribute to an open-source project related to the course material to gain practical experience and enhance your skills.

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Identify a suitable open-source project
Review the project documentation
Make a code contribution
Submit a pull request

Career center

Learners who complete Python for Excel: Use xlwings for Data Science and Finance will develop knowledge and skills that may be useful to these careers:

Quantitative Analyst

Quantitative Analyst use mathematical and statistical models to analyze financial data. They develop and use these models to make predictions about the future performance of financial markets. This course provides a strong foundation in Python, xlwings, and financial modeling, which are essential tools for quantitative analysts. The course also covers data visualization, which is a key skill for communicating insights to stakeholders.

See salaries and explore the career path for Quantitative Analyst

Financial Analyst

Financial Analysts use data to make recommendations about investments. They analyze financial data, such as company reports and economic indicators, to assess the risks and rewards of different investments. This course provides a strong foundation in Python, xlwings, and financial modeling, which are essential tools for financial analysts. The course also covers data visualization, which is a key skill for communicating insights to stakeholders. In addition, the course's focus on using Python and xlwings to automate tasks will be beneficial for financial analysts who want to improve their efficiency.

See salaries and explore the career path for Financial Analyst

Data Scientist

Data Scientist use data to solve business problems. They work with data to develop models, algorithms, and other solutions that can help businesses make better decisions. This course provides a strong foundation in Python and xlwings, which are essential tools for data scientist. The course also covers data visualization, which is a key skill for communicating insights to stakeholders. In addition, the course's focus on using Python and xlwings to automate tasks will be beneficial for data scientist who want to improve their efficiency.

See salaries and explore the career path for Data Scientist

Business Analyst

Business Analysts use data to understand business processes and improve decision-making. They work with stakeholders to gather requirements, analyze data, and develop recommendations. This course provides a solid foundation in Python and xlwings, which are essential tools for business analysts. The course also covers data visualization, which is a key skill for communicating insights to stakeholders. In addition, the course's focus on using Python and xlwings to automate tasks will be beneficial for business analysts who want to improve their efficiency.

See salaries and explore the career path for Business Analyst

Data Analyst

Data Analysts collect, transform, and analyze data to help businesses understand their customers, improve their products and services, and make better decisions. This course provides a solid foundation in Python and xlwings, which are essential tools for data analysts. The course also covers data visualization, which is a key skill for communicating insights to stakeholders. In addition, the financial modeling component of the course will be beneficial for data analysts who work in the financial industry.

See salaries and explore the career path for Data Analyst

Software Engineer

Software Engineers design, develop, and maintain software applications. This course provides a solid foundation in Python, which is a popular programming language for software development. The course also covers data visualization, which is a key skill for communicating insights to stakeholders. In addition, the course's focus on using Python and xlwings to automate tasks will be beneficial for software engineers who want to improve their efficiency.

See salaries and explore the career path for Software Engineer

Market Researcher

Market Researchers study consumer behavior and trends. They use this information to help businesses develop new products and services. This course provides a solid foundation in Python and data visualization, which are essential tools for market researchers.

See salaries and explore the career path for Market Researcher

Operations Research Analyst

Operations Research Analysts use mathematical models to improve the efficiency of business operations. This course provides a solid foundation in Python, which is a popular programming language for operations research. The course also covers data visualization, which is a key skill for communicating insights to stakeholders.

See salaries and explore the career path for Operations Research Analyst

Risk Analyst

Risk Analysts identify and assess risks to businesses. They develop and implement strategies to mitigate these risks. This course provides a solid foundation in Python and financial modeling, which are essential tools for risk analysts. The course also covers data visualization, which is a key skill for communicating insights to stakeholders.

See salaries and explore the career path for Risk Analyst

Data Engineer

Data Engineers design, build, and maintain data pipelines and infrastructure. This course provides a solid foundation in Python, which is a popular programming language for data engineering. The course also covers data visualization, which is a key skill for communicating insights to stakeholders.

See salaries and explore the career path for Data Engineer

Machine Learning Engineer

Machine Learning Engineers design, develop, and maintain machine learning models. This course provides a solid foundation in Python, which is a popular programming language for machine learning. The course also covers data visualization, which is a key skill for communicating insights to stakeholders.

See salaries and explore the career path for Machine Learning Engineer

Actuary

Actuaries assess the financial risk of insurance policies. They use mathematical models to calculate the probability of events and to determine the appropriate premiums. This course provides a solid foundation in Python and financial modeling, which are essential tools for actuaries. The course also covers data visualization, which is a key skill for communicating insights to stakeholders.

See salaries and explore the career path for Actuary

Business Intelligence Analyst

Business Intelligence Analysts use data to help businesses make better decisions. They work with data to develop reports, dashboards, and other tools that can help businesses understand their performance and identify opportunities for improvement. This course provides a solid foundation in Python and data visualization, which are essential tools for business intelligence analysts.

See salaries and explore the career path for Business Intelligence Analyst

Financial Planner

Financial Planners help clients plan for their financial future. They analyze clients' financial situation and develop plans to help them reach their financial goals. This course provides a strong foundation in Python and financial modeling, which are essential tools for financial planners. The course also covers data visualization, which is a key skill for communicating insights to stakeholders.

See salaries and explore the career path for Financial Planner

Statistician

Statisticians collect, analyze, and interpret data. They use statistical methods to draw conclusions about the world around them. This course provides a solid foundation in Python, which is a popular programming language for statistics. The course also covers data visualization, which is a key skill for communicating insights to stakeholders.

See salaries and explore the career path for Statistician