Skill Path

Python Data Analysis - Learn how to do Data Analysis with Machine Learning, Python and ChatGPT

Advanced15h 32min6 chaptersLast updated 08/2025

Unlock the power of data with our comprehensive Python Data Analysis Skill Path, designed for those eager to master the art of extracting actionable insights from raw data. This Skill Path will guide you through the nuances of data analysis, starting with the basics of Python programming and leading up to sophisticated Machine Learning techniques and AI-driven analytics with ChatGPT.
...

By Chris Koch · Instructional designer, Instructor and Sr. Software Engineer

Skill objectives

Chapter 01

Introduction to Data Analysis

In this first chapter you will learn what is Data Analysis and why it matters when we talk about data. We also define the difference between data engineers and data scientists.

Skills: Data Analysis Tools, Data Analysis

Link25min

Data Analysis - Wikipedia

Data analysis in the most basic sense is simple: You take some data and you try to get insights from it. This can be done via graphs, statistical analysis or you could go further and use ai or machine learning to understand a dataset. 

Video6min

The complete data science roadmap

Python has a standardized path to data science, machine learning and AI. You use Numpy and Pandas to organize the data, SciKit-Learn, Pytorch and Tensorflow for machine learning and AI. If you add some math and practice you can use these tools to create complex neural networks and data science tools. 

Link25min

Data engineer vs Data scientist

It is very easy to confuse data engineering with analyzing data, and the fields are connected and have some overlaps. However, in the most basic sense, a data engineer extracts and prepares the data while a data analyst uses that data to get deeper insights. 

Chapter 02

Introduction to Business Intelligence

Business Intelligence (BI) refers to the strategies and technologies used by enterprises for data analysis of business information to support decision-making. In this chapter we start learning more about it and get introduce to Power BI by Microsoft.

Skills: Business Intelligence, PowerBI, Python

Link10min

What is Business Intelligence

Business intelligence is a key tool for companies to organize and store data. Especially in a business setting you will encounter some kind of software like this quite often. 

Video28min

Microsoft Power BI Desktop for beginners

Microsoft Power BI is the most popular BI software and this video gives you a good overview of the main
features. 

Video12min

How to create a forecast model in Power BI with Python

You can also use Python in Power BI for more advanced data analytics projects. In this video, for example, you can learn how to create a forecast model. 

Link10min

9 Power BI alternatives

Power BI is not the only BI software. In fact, there are lots of competitors, some of which are free while
others focus on more specific things. 

Chapter 03

Common basic Python data tools

A set of famous and very effective Python libraries you can use to manage and analyze data. Some of them are quite famous, while other might sound new.

Skills: Python, NumPy, SciPy, Pandas

Video25min

Jupyter notebooks

When working with data you usually don't use a traditional code editor like VS code but rather Jupyter notebooks: These allow you to separate your code into chunks and run each of those separately, they also include nice visual features to display graphs and tables. 

Video14min

Learn Numpy in 5 minutes

The single most important tool in Python for data analysis is an array, which you get from Numpy. An Array is basically a list that is much faster and more powerful. 

Video39min

A gentle introduction to Pandas

Numpy arrays are only for single sets of data but usually you are working with lots of data that you need to organize. For that you have Pandas, which gives you a DataFrame. This combines multiple Numpy arrays into a larger DataFrame that lets you  organize things efficiently. 

Link20min

Pyplot tutorial

An introduction to creating graphs in Python using Pyplot, a submodule of Matplotlib. 

Video29min

Introduction to Plotly

Pyplot is primarily used for static graphs but very often you want things to be more interactive and for that you can use Plotly: It displays data just like Matplotlib but it is much more geared towards interactivity and gives you lots of options to display data with user input in mind. 

Link20min

Statsmodels user guide

Analyzing data, especially for more advanced topics, will require knowledge of statistics. The Statsmodels module simplifies this process by giving you access to popular math operations like linear regressions or time series analysis. 

Video8min

NumPy vs SciPy

To go into even more advanced data analysis modules, SciPy is an extension of NumPy that gives you
access to scientific data operations that will help you enormously to go into more depth. For example,
it offers you optimization algorithms, linear regression and forecasting methods. 

Link25min

SciPy user guide

SciPy, somewhat like NumPy, is a massive library with a huge range of tools. To get an idea of what you 
can do with it read through the documentation and get a sense of what can be done. After that, you can
always come back and explore more specific parts for your projects. 

Chapter 04

ChatGPT for Data Analysis

It's time to start experimenting with ChatGPT to work on data analysis. In this chapter you will learn how you can use ChatGPT to analyze data but also the PROs and CONs of this approach compared to classic methods.

Skills: ChatGPT, Generative AI Tools, Generative AI Fundamentals

Video5min

Chat GPT explained in 5 minutes

ChatGPT is a large language model (LLM) that can analyze and summarize large amounts of text. This
can function as a google search, help you analyze data, review code and even generate graphs. 

Video14min

ChatGPT for Data analysis for beginners in 2024

ChatGPT is capable of creating a broad range of graphs if you feed it the right kind of data and prompt. 
Especially for a first impression this can be great as it only takes seconds. 

Video10min

Is Excel dead? Meet the ChatGPT Data Analyst

Via plugins, like Data Analyst, ChatGPT can even accept Excel files as input and analyze that information
to provide you with insights and graphs. You can also go the other way to get ChatGPT into Excel! 

Video17min

10x your code with ChatGPT

Beyond data analysis ChatGPT can also review and write code. You can ask it through the website or
have a VS code plugin. Especially for simple tasks this can be an easy way to speed up your work. 

Link25min

AI Platforms like ChatGPT are easy to use but also potentially dangerous

LLMs, like ChatGPT, can be very useful but they also have limitations: Their answers can be incomplete or simply false, they can even invent information; they are confident when providing these answers and - when it comes to code - the solutions can be inefficient. Make sure to always doublecheck the  work of an LLM. 

Chapter 05

Machine learning for Data Science

If you are exploring data analysis for the first time and you are a software engineer, you probably don't need to learn all the specific of Machine Learning but understanding its fundamentals will give you lots of advantages.

Skills: Data Science Fundamentals, AI and Machine Learning, Scikit-learn, Statistical Skills, Mathematical Problem-Solving Skills

Video7min

How I'd learn machine learning in 2024

There is a common path to machine learning in Python: You start with Numpy and Pandas to organize the
data, then you use SciKit-Learn to get started with machine learning operations. Beyond that, libraries
like Pytorch and TensorFlow let you go into even more depth. 

Video50min

Python machine learning tutorial

An overview of machine learning in Python and how you can get started. 

Link30min

Machine learning mathematics

Machine learning relies on a number of mathematical concepts. You don't have to know them in too much detail since the Python modules abstract most of that work away but it is still a good idea to know what a linear regression is or how Vectors and Matrices work. 

Video4min

Learning Scikit-learn

A short history and overview of SciKit-Learn along with a basic example of a classification tool. 

Video2h 9min

SciKit-Learn crash course

A crash course on SciKit-Learn that covers all the basics to get started, how to select an appropriate 
model for your data and how to use pre and post processing to optimize your model. 

Link1h

Various exercises in machine learning using Scikit-learn

A page with lots of practice projects to sharpen your skills and ensure that you understood that basics covered in the course. 

Chapter 06

TensorFlow & Deep learning

TensorFlow is an open-source library by Google for numerical computation and machine learning. It simplifies creating complex ML models, enhancing AI applications by enabling advanced data analyses and automation.

Skills: TensorFlow, Deep Learning

Video3min

TensorFlow in 100 seconds

TensorFlow is one of the most common libraries used in Python for machine learning and AI tasks, although not the only one. It can be used to build neural networks which can do anything from recognizing natural speech to driving cars. 

Video2h 13min

TensorFlow 2.0 crash course

A crash course on the basics of TensorFlow to understand what a neural network is and how to use 
TensorFlow to create a model to create them and make predictions. 

Video14min

All learning algorithms explained in 14 minutes

There are a lot of machine learning algorithms that you need to understand, this video covers the 
basics like linear regressions, decision trees etc. 

Link1h

TensorFlow examples

A range of practice projects to master TensorFlow. This Github page contains a broad range of practice projects that start from very simple and progress to more advanced topics. 

Video1h 14min

Build a deep learning model that can lip read

A really cool project that uses TensorFlow to create an ai that can lip read. 