Data Science Credential

Master Python for Real Projects

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

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.

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

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.

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

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

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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.

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.