Prove Your Data Science Chops

Master Python for Real Projects

Python is the primary language of data science. You need fluency before any library makes sense.

1. 1Write Pythonic code, not just Python code
2. 2Work with APIs and external libraries
3. 3Write tests for your own code
4. 4Build a data pipeline from files to output
5. 5Publish a Python package on PyPI

Master SQL for Data Analysis

Every real dataset lives in a database. SQL is the tool you use to understand data before you model it.

1. 1Query a real database and answer a question
2. 2Write JOIN queries across multiple tables
3. 3Aggregate, group, and transform data with CTEs and window functions
4. 4Answer a real business question end-to-end

Master Statistics for Data Analysis

Statistics is the foundation that makes model output interpretable. Without it, you cannot tell a real signal from noise.

1. 1Describe a real dataset with statistics
2. 2Run and interpret a hypothesis test
3. 3Build and evaluate a regression model
4. 4Design and analyse an A/B test
5. 5Communicate findings to a non-technical audience

Build an End-to-End ML Pipeline

A real ML pipeline — data ingestion, feature engineering, model training, evaluation, deployment — is what separates a data scientist from someone who has taken ML courses.

1. 1Define the prediction problem, select the dataset, and document architecture decisions
2. 2Build, train, and evaluate the pipeline with proper methodology
3. 3Package as a reproducible artifact and present for ML practitioner Q&A

Complete a Kaggle Competition (Top 25%)

Kaggle competitions force you to work with messy real data, iterate fast, and read other people's solutions. No course replicates this.

1. 1Complete a Getting Started competition end to end
2. 2Build a competitive pipeline for a Featured competition
3. 3Achieve top-25% placement in a Featured or Research competition

Earn Google Data Analytics Certificate

Optional. Audit the course for free — the certificate is paid. The value is in completing the curriculum and the capstone project.

1. 1Complete Certificate Foundations and Demonstrate Spreadsheet Competence
2. 2Complete SQL and R Modules with Independent Analyses
3. 3Complete the Certificate and Produce a Portfolio Case Study

Build and Ship a Data Science Project

The capstone. One project that touches every skill in this path — from raw data to a deployed model to a report a stakeholder can act on.

1. 1Define the question, source the data, and complete exploratory analysis
2. 2Build, evaluate, and iterate on an analysis or model pipeline
3. 3Publish findings with reproducible code and present to a technical reviewer

The open-source university data science curriculum. This path follows its structure. Use it as your master checklist and reading list throughout.

Free, complete statistics curriculum. Work through this before starting any ML course — the concepts you skip here will come back to bite you in model evaluation.

Free micro-courses on Python, SQL, ML, and more. The courses use industry-standard datasets and are written by practitioners, not academics.

Audit for free. Do not pay for the certificate — the value is in completing the curriculum and the capstone project, not in the badge.

The best free statistics and ML explanations on YouTube. Watch a StatQuest video before every new concept you encounter — it will save you hours of confusion.