# Stock Market Analytics Zoomcamp: a five-module course that runs in cohorts > This repository holds the course materials for the Analytics in Stock Markets Zoomcamp, organised as one folder per module plus a projects folder and per-cohort homeworks, and it is meant to be worked through alongside livestreamed videos. It is a teaching repository rather than a library, it declares no licence, and its maintenance cycle follows the course calendar rather than a release schedule. **DataTalksClub/stock-markets-analytics-zoomcamp** — Course Materials for Analytics in Stock Markets Zoomcamp - Repository: https://github.com/DataTalksClub/stock-markets-analytics-zoomcamp - Website: https://pythoninvest.com/course - Stars: 1,029 · Forks: 293 - Language: Jupyter Notebook - License: not declared - Published: 2026-10-19 · Updated: 2026-10-19 · Language: en - Canonical page: https://hysenlabs.com/projects/datatalksclub-stock-markets-analytics-zoomcamp ## The repository is the syllabus, one directory per module This is a course, not a library, and the top-level layout is the table of contents. The primary language recorded for the repository is Jupyter Notebook, which is the artefact a student actually works in, and the entries at the root are the numbered module folders. ```console 01-intro-and-data-sources/ 02-dataframe-analysis/ 03-modeling/ 04-trading-strategy-and-simulation/ 05-deployment-and-automation/ ``` The short syllabus published on the course site follows the same order. Module 1 is Introduction and Data Sources, module 2 is Working with the Data in Pandas, module 3 is Analytical Modeling, module 4 is Trading Strategy and Simulation, and module 5 is Deployment and Automation, followed by a project. The detail behind each module heading is what tells you what a week looks like. Module 1 covers understanding data-driven decisions, initiating data extraction, where to focus attention and the considerations of risk and reward, then a practical setup with Colab and an initial data download using Finance APIs, and then the essential principles for API selection, including when it becomes necessary to consider payment options in the API selection process. Module 2 goes through the core libraries for data analysis in Python, naming Numpy, Pandas and Matplotlib. Modules 3, 4 and 5 are named in the syllabus rather than expanded in the visible README, so their contents have to be read from the folders themselves. There is also a projects/ directory, a course.yaml file, a certificates.md file and a cohorts/ directory. ## Cohorts carry the homeworks and the deadlines The cohorts/ directory is what makes this a course with a schedule rather than a pile of notebooks. The README lists a cohort folder per year, and each one holds the homeworks and deadlines for that run. The 2026 folder is cohorts/2026/, the 2025 folder is cohorts/2025/ and the 2024 folder is cohorts/2024/. The dates make the cadence concrete. The 2026 cohort started on 24 August 2026, a Monday, at 4pm GMT+1 in Dublin time. The 2025 cohort started on 19 May 2025 at 6:30pm GMT+1, and the 2024 cohort started on 15 April 2024 at 17:00 GMT. That is a rhythm of a course that runs a few times a year, and the folders accumulate rather than being replaced, so the repository doubles as an archive of past cohorts. The registration form changes with the cohort. For 2026 the form is on the course page, while the 2025 and 2024 forms are both marked as not accepting responses anymore, which tells you plainly that a cohort's enrolment is a closed thing once it has started. The projects/ directory and the certificates.md file are the other end of that structure. A final project sits at the end of the syllabus, and the certificates file suggests that completing the work produces something you can point at, which is the usual currency of a course like this. ## Self-paced mode is the way to use this without a cohort The README is explicit that all the materials of the course are freely available so that you can take the course at your own pace, and it spells out what that means. Follow the suggested syllabus week by week. You do not need to fill in the registration form, just start watching the videos and join Slack. If you have problems, check the FAQ, and if the FAQ does not cover your problem, ask for help in Slack. That is the whole self-service story, and it is a sensible design for a repository that is publicly readable. The one requirement is community participation rather than payment, since the help path runs through the DataTalks.Club Slack and the course channel #course-stocks-analytics-zoomcamp. The Slack requirement is worth thinking about before you start. The FAQ page at datatalks.club is where the recurring technical questions live, and the Slack channel is where the one-off problems go. If you are not willing to join a chat, the FAQ is the only support surface, and the repository will not answer questions on its own. A second self-paced support surface exists in the form of preparation workshops. The README links three: an Economics and Automation Workshop on building a data pipeline for economic insights, a session on predicting financial time-series, and one on building an AI stock research agent. These are linked from the 2026 section as preparation, so they are meant to be watched before the modules rather than during them. ## Module 1 makes you choose a data source and a payment plan The first module is where the course makes its most consequential decision for you, because everything downstream depends on where the data comes from. Its outline starts with the philosophy behind making decisions based on data, the range of potential personal investments, and where to focus attention given the considerations of risk and reward. Then the practical part, which is the setup. The module guides you through setting up Colab for practical data analysis and downloading your initial financial data using Finance APIs, and it covers the essential principles for API selection, with considerations for choosing the right API for your data needs and an explicit note on when it becomes necessary to consider payment options in the API selection process. That last point is the one people underestimate. Free market data APIs are usually rate limited, delayed, or restricted in what you can request historically, and the module treats the move from a free tier to a paid feed as a decision with its own criteria rather than an afterthought. A student who picks the wrong feed in week one discovers the limits in module 2 or 3, when the notebook needs more history than the free tier allows. Using Colab as the default environment is a deliberate choice with its own consequences. It removes local setup friction and gives every student the same environment, which makes the cohort experience coherent. It also means the work lives in a hosted notebook runtime rather than on your machine, which is a constraint when module 5 gets to deployment. ## The video half of the course lives on YouTube and PythonInvest The repository is the written material, and the teaching happens in video. All modules are streamed on the PythonInvest YouTube channel and added to a playlist per cohort, with the 2026 cohort having its own playlist, alongside 2025. The videos are published on PythonInvest's YouTube channel, and there is a pre-launch stream with a course overview and Q&A for each cohort, streamed before the start date, on 17 August 2026 for the 2026 run, 14 April 2025 for 2025 and 25 March 2024 for 2024. The course has its own site, pythoninvest.com/course, and the homepage of the repository points at it. That site is where the short syllabus is published, where the current registration form lives, and where the supplementary pre-read material is listed, including a financial news feed, a blog with analytical long-reads, and a set of market insight and podcast sources. The pre-reading list also includes books, and the README names several titles explicitly. There is a small commercial dimension too. The TODO list before the course starts asks students to subscribe to PythonInvest to receive analytics and future course announcements, alongside registering in the DataTalks.Club Slack, joining the course Slack channel and the course Telegram channel with announcements. The materials themselves are free, and the pre-course checklist asks students to subscribe to PythonInvest for analytics and future course announcements. ## The last module is about deployment, which is where the notebooks end The syllabus ends with Module 5, Deployment and Automation, and a project. That ordering is the pedagogical argument of the whole course: the early modules build a data pipeline and a model, module 4 turns it into a trading strategy with simulation, and module 5 deals with the part that decides whether any of it runs unattended. For a data course, that is the honest ending, because most notebooks never get further than a colleague's laptop. The value of having the deployment module in the same syllabus as the modelling one is that the constraints are known to the people who wrote the modelling code. The project is the capstone, and the README gives it an unusual preparation instruction. Before selecting a project, students are asked to read recent financial news or analytics from the listed feeds, and the works list includes a financial news feed, a blog of analytical long-reads, market insights, investing coverage on CNBC, the Unhedged podcast and articles, and Yahoo Finance, plus several books with titles like The Trading Game, Unknown Market Wizards, The Man Who Solved the Market, The Tao of Trading and a guide to options trading. The reasoning is that a project proposal should start from a real market question rather than from a technique you want to practise. What the visible README does not give is the content of modules 3, 4 and 5, the grading criteria, or the deadlines themselves. Those live in the cohort folders and in the module directories, so a self-paced student discovers them by opening the folders. ## No licence is declared, and the maintenance signal is a calendar Two structural facts are worth knowing before you build anything on top of this repository. The first is that no licence is declared. The repository metadata reports the licence as unknown, and there is no LICENSE file among the top-level entries, which are the module folders, projects/, cohorts/, certificates.md, course.yaml and the README. That is a licensing fact rather than legal advice, and the practical reading is that the material is published for learners to use, but the terms for reuse in a commercial product, in a course of your own, or in a derived dataset are not stated. If you plan to publish something built on these notebooks, ask the authors. The second is that the maintenance signal is a course calendar, not a release history. There are no GitHub releases, the default branch is main, and the last push was on 2026-10-16, which is during the 2026 cohort that started in August. The commits land while the course runs, when a notebook needs a fix for the current platform or a link breaks, and they stop when the cohort ends. For a learner, that is fine. For an organisation that wants to run its own internal course on this material, it means the repository is a snapshot of the 2026 run rather than a versioned curriculum, and the cohort folders from 2024 and 2025 are the closest thing to a changelog you have. ## Conclusion The Stock Market Analytics Zoomcamp repository fits a self-learner who wants a structured path through market analytics, from data sources and Pandas through modelling, backtested trading strategies and deployment, with notebooks and cohort homeworks to work from. It does not fit someone looking for a maintained library or a reusable pipeline, because it is course material that assumes a course and a calendar, and the notebooks are the artefact rather than a package. Before starting, read the FAQ page for the technical questions, clone the repository and work the module folders in order, and register for a cohort only if you want the deadlines and the live sessions. ## FAQ ### Can I take the Stock Market Analytics Zoomcamp without joining a cohort? Yes. The README says all the materials of the course are freely available so that you can take the course at your own pace, following the suggested syllabus week by week. You do not need to fill in the registration form, you just start watching the videos and join Slack, and the FAQ page at datatalks.club covers the recurring technical questions. ### What does the Stock Market Analytics Zoomcamp cover? Five modules and a project: Introduction and Data Sources, Working with the Data in Pandas, Analytical Modeling, Trading Strategy and Simulation, and Deployment and Automation. Module 1 sets up Colab, downloads data with Finance APIs and covers API selection, and module 2 covers Numpy, Pandas and Matplotlib. ### Where are the homeworks and deadlines kept? In the cohort folders, with one folder per run: cohorts/2026/, cohorts/2025/ and cohorts/2024/. Each cohort folder holds the homeworks and deadlines for that run, and the 2026 cohort started on 24 August 2026 at 4pm GMT+1. ### Where do I watch the course videos? All modules are streamed on the PythonInvest YouTube channel and added to a playlist per cohort, and the course site at pythoninvest.com/course hosts the syllabus and the registration form. Each cohort also has a pre-launch overview and Q&A stream before it starts. ### Can I use the notebooks and materials commercially? The repository does not declare a licence and there is no LICENSE file among the top-level entries, so the terms for reuse beyond studying the material are not stated. The README links the course site, the FAQ and the Slack channel for the questions the authors answer directly. ## Sources - [DataTalksClub/stock-markets-analytics-zoomcamp on GitHub](https://github.com/DataTalksClub/stock-markets-analytics-zoomcamp) - [Issues](https://github.com/DataTalksClub/stock-markets-analytics-zoomcamp/issues) - [Project website](https://pythoninvest.com/course) - [README](https://github.com/DataTalksClub/stock-markets-analytics-zoomcamp/blob/main/README.md) --- Hysen Labs editorial analysis, written from the project's own repository and release notes. Cite the canonical page: https://hysenlabs.com/projects/datatalksclub-stock-markets-analytics-zoomcamp