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Skill Path

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

Advanced15h 32minLast 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. Whether you are looking to boost your career, spearhead data-driven projects, or simply gain a competitive edge in the digital economy, this Skill Path provides all the tools you need to become proficient in Python data analysis and beyond. Enroll now and start transforming data into powerful insights that drive decisions.
Content (31)
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.
Data Analysis
Data Analysis Tools
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.
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.
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.
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.
Business Intelligence
PowerBI
Python
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.
Microsoft Power BI is the most popular BI software and this video gives you a good overview of the main features.
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.
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.
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.
NumPy
Pandas
Python
SciPy
Video
Jupyter notebooks
25minBasic
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.
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.
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.
Link
Pyplot tutorial
20minIntermediate
An introduction to creating graphs in Python using Pyplot, a submodule of Matplotlib.
step image Introduction to Plotly
Video
Introduction to Plotly
29minIntermediate
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.
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.
step image NumPy vs SciPy
Video
NumPy vs SciPy
8minBasic
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.
Link
SciPy user guide
25minIntermediate
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.
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.
ChatGPT
Generative AI Fundamentals
Generative AI Tools
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.
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.
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!
step image 10x your code with ChatGPT
Video
10x your code with ChatGPT
17minIntermediate
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.
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.
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.
AI and Machine Learning
Data Science Fundamentals
Mathematical Problem-Solving Skills
Scikit-learn
Statistical Skills
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.
step image Python machine learning tutorial
Video
Python machine learning tutorial
50minIntermediate
An overview of machine learning in Python and how you can get started.
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.
step image Learning Scikit-learn
Video
Learning Scikit-learn
4minIntermediate
A short history and overview of SciKit-Learn along with a basic example of a classification tool.
step image SciKit-Learn crash course
Video
SciKit-Learn crash course
2h 9minAdvanced
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.
A page with lots of practice projects to sharpen your skills and ensure that you understood that basics covered in the course.
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.
Deep Learning
TensorFlow
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.
step image TensorFlow 2.0 crash course
Video
TensorFlow 2.0 crash course
2h 13minIntermediate
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.
There are a lot of machine learning algorithms that you need to understand, this video covers the basics like linear regressions, decision trees etc.
step image TensorFlow examples
Link
TensorFlow examples
1hIntermediate
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.
A really cool project that uses TensorFlow to create an ai that can lip read.
Introduction
This path has been curated by the Anthropos team in collaboration with Chris Koch
Chris KochChris Koch
Instructional designer, Instructor and Sr. Software Engineer
Skill objectives