Analytics in Stock Markets Zoomcamp

[Started 15th April, you can still join] Spring 2024 cohort
In collaboration with DataTalks.Club

A gentle introduction to stock market trading, Python programming in Colab, analytics, and data visualisation

Course Syllabus

Why join?

Zero barriers to start

We use Google Colab notebooks. It is a copy-past solution, one-click init environment, and easy to extend.

No need to have a finance background

We explain our ideas with a plain language without complicated models and assumptions from theoretic finance.

Build a semi-automatic trading system

You'll create a set of notebooks with your ideas that can be applied for any broker account.

Make unbiased decisions

You'll try to validate the hypothesis at scale: from one example to hundreds of stocks observed.


Working in pairs and talking to like-minded people interested in investing, analysis, and programming.

Confident trading

You'll get a minimum working knowledge to generate your ideas and test them on the market.

Who should attend?

We believe the most common use cases are:
Home traders and individual investors seeking a macro view of the market and the development of statistical intuition.
Students looking to enhance their coding skills and tackle mathematical challenges in financial markets.
Analysts or developers interested in trading, offering a brief introduction to the financial markets.
Analysts aiming to acquire coding skills, with opportunities to practice web scraping, time-series analysis, and predictive modelling on extensive datasets.


For this course, it's recommended to have:
An analytical mindset and the ability to make decisions based on data.
Basic coding skills, ideally in a modern programming language (especially Python).
Some background in trading or a keen interest in financial markets to apply the knowledge immediately.
Previous experience in any of DataTalks.Club courses is beneficial.

Format of a course

Weekly live YouTube sessions will cover new material.
Lecture materials will include sample code examples for your reference.
Home exercises will be given to reinforce the covered content.
Each section concludes with a questionnaire for submitting your homework.
Final weeks will be devoted to a Capstone project and peer review


By the end of the course, you will
Discover new ways to check facts automatically on a large scale.
Use Python to gather, test, and visualise the data, as well as create personalised trading algorithms.
Connect with peers who share similar interests and can help you achieve better, long-lasting results.

[Course Registration] Spring 2024 cohort - start in April

Please fill in your details in the form (~5 minutes)

Analytical Blog articles

Explore the recent research algorithms, including interactive charts and code on the Github

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