Who is this course for?
This course is for those who plan to take a step into the field of data science and beginner to intermediate level data analyst, data scientist, and data engineers.
Most of the exercises are based on my experience of working as a data scientist with real-life datasets so you can benefit from this course even if you are already using Pandas at your job. If you have never used Pandas before or have little experience, you can learn a lot because the exercises are created in a way that is simple and easy-to-understand. All you need is a basic level of Python knowledge.
What is needed to take this course?
Lectures are structured as me going over Jupyter notebooks explaining exercises. Notebooks can be found in the description of each lecture. If you want to download the notebooks and follow along, make sure you also download the relevant datasets available in the data folder in the course repository.
You also need to have Jupyter notebook installed on your computer. You can also Google Colab, which allows for running Jupyter notebooks in your browser for free.
The course is divided into 6 chapters:
- Data exploration and manipulation
- Data filtering
- Combining DataFrames
- Data analysis and visualization
- Use cases
- More learnings
Each chapter contains multiple lectures with each one focusing on a particular task such as how to filter a DataFrame, how to create pipelines with multiple steps, and how to use Python dictionaries to enhance the power of Pandas functions.
By the time you finish this course, you’ll have solved at least 500 exercises and you’ll be able to solve most of the tasks related to tabular data.
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