Python for Data Analysis: step-by-step with projects

This course offers 8+ hours of HD video lectures, detailed code notebooks, 3 guided practice projects, based on multiple real-world datasets.

This course will guide you to learn from scratch how to analyze data efficiently in Python.

By following this course, you'll gain practical experience analyzing real-world datasets. So that by the end, you'll be able to conduct your own analysis with Python, and extract valuable insights that can transform your business!

What are the design principles of the course

Instead of dumping all the available Python libraries or functions to you, we picked only the most useful ones based on our industry experience to cover in the course. This allows you to focus and master the foundations.

The course is arranged in different sections based on the step-by-step process of REAL data analysis. Please check out the course overview lecture for details.

Besides Python programming, you'll also get exposed to basic statistical knowledge necessary for data analysis.

Combined with the detailed video lectures, you'll be given a few projects to work on to reinforce the knowledge.

In the end, you'll have a solid foundation of data analysis, and be able to use Python for the whole process.

Why data analysis in Python

Data analysis is a critical skill and is getting more popular.

Nowadays, almost every organization has some data. Data could be very useful, but not without appropriate analysis. Data analysis enables us to transform data into insights for businesses, to make informative decisions.

You can find data analysis being used in almost every industry, be it health care, finance, or technology.

While Python is one of the most in-demand skills for data science by employers. It is not only easy to learn, but also very powerful.

Who is this course for

This course is helpful for anyone interested in analyzing data effectively. Perhaps you want to become a data analyst or a data scientist, or maybe you just want the skills to work on your projects.

This course is bner-friendly. However, we recommend you to have some basic knowledge of Python or at least another programming language.

With that said, there is a Python crash course included, so you can pick up or review the skills needed.

What are the main Python libraries covered

Pandas

Scikit-learn

Seaborn

All you need to start this course is the desire to learn, and a computer!

Looking forward to seeing you inside the course!

Cheers,

Lianne and Justin

Preview image designed by freepik

Basic Python ONLY

If you have experience with other similar programming languages, take the Python Crash Course included

How to use Python for data analysis

Reach an intermediate level of Python

Experience analyzing real-world datasets in lectures and guided projects

Use Python data analysis libraries (Pandas, Scikit-learn, Seaborn)

Import, examine, export data in Python

Manipulate data

Clean data

Transform data

Calculate summary statistics

Create data visualizations in Python

Use JupyterLab/Jupyter Notebook




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