Data · For career-switchers and analysts levelling up

Learn data science — stats, Python, and a project arc that holds together.

Strive builds a data-science course around where you are: career-switcher, analyst going deeper, or engineer crossing over. Stats foundations, pandas and scikit-learn, and a portfolio-shaped project arc. Lessons stream with runnable code, and a daily recall queue keeps the formulas and APIs in your head.

Build my Data science course — free

Free tier included. No credit card.

Typically 25–40 hoursintermediateEnglish

What you’ll learn

Wrangle real-world data in pandas — joins, group-bys, time series, missing values.
Reason about distributions, sampling, and the bits of probability you’ll actually use.
Train a regression and a classifier in scikit-learn and explain what they’re doing.
Evaluate a model honestly — train/test splits, cross-validation, the right metric for the job.
Diagnose under- and overfitting and pick a fix that isn’t "more data".
Communicate a model the way an analyst should — what it does, what it misses.
Ship a portfolio-shaped project from raw data to a written write-up.

A typical Strive course on Data science

The stats you can’t skip — distributions, sampling, inference4 lessons
pandas as your daily driver5 lessons
Exploratory analysis and feature thinking4 lessons
Regression — fit, interpret, and don’t lie with R²4 lessons
Classification — logistic regression, trees, and the right metric4 lessons
Evaluation, leakage, and honest validation3 lessons
A portfolio-shaped project from raw data to write-up3 lessons

Demonstration outline — your course is generated around your answers, so module count, depth, and difficulty will differ from this. Across the 7 modules above there are 27 lessons.

Frequently asked

Do I need a stats background?

No, but you’ll spend the first module building one. The course teaches the stats you’ll actually use — distributions, inference, evaluation — not a full degree’s worth. If you already have it, the wizard lets you skip ahead.

Is this Python-only or does it touch R?

Python-only by design — pandas and scikit-learn. R is a fine choice for some teams, but the data-science job market overwhelmingly hires Python, and the course is honest about that.

Does it cover deep learning?

Lightly. Classical ML is the spine of the course. The wizard offers a short PyTorch primer at the end if you ask for it; for serious deep-learning work, run the machine-learning course next.

Will Strive find me a job?

No, and we’ll never claim that. What you’ll have at the end is a working toolkit and one finished project — that’s the part you can actually point at in an interview.

Ready to learn Data science?

Tell us where you are today. AI builds your course in minutes — and the daily recall queue makes sure you keep what you learn.

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Learn data science — stats, Python, and a project arc that holds together.

Typically 25–40 hoursintermediateEnglish

What you’ll learn

Wrangle real-world data in pandas — joins, group-bys, time series, missing values.

Reason about distributions, sampling, and the bits of probability you’ll actually use.

Train a regression and a classifier in scikit-learn and explain what they’re doing.

Evaluate a model honestly — train/test splits, cross-validation, the right metric for the job.

Diagnose under- and overfitting and pick a fix that isn’t "more data".

Communicate a model the way an analyst should — what it does, what it misses.

Ship a portfolio-shaped project from raw data to a written write-up.

A typical Strive course on Data science

The stats you can’t skip — distributions, sampling, inference4 lessons

pandas as your daily driver5 lessons

Exploratory analysis and feature thinking4 lessons

Regression — fit, interpret, and don’t lie with R²4 lessons

Classification — logistic regression, trees, and the right metric4 lessons

Evaluation, leakage, and honest validation3 lessons

A portfolio-shaped project from raw data to write-up3 lessons