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DATAVERSE
2 months

Data Science, the fast start

Two focused months of Python, statistics and machine learning. Enough to build a real model and know whether you want to go deeper.

See modules
Length
2 months
Starts from
Zero

Is this for you?

It's for you if

  • You want to know what machine learning really involves before going all in.
  • You're fine with a fast pace for a short stretch.
  • You like the idea of predicting things, not just reporting them.

Pick something else if

Where this can take you.

Roles this program prepares you for. No salary promises, just what the work is.

  • Junior Data Scientist

    You test ideas with data and build models that predict what happens next.

    • Python
    • scikit-learn
    • Statistics

  • Data Analyst

    You do analyst work, with models in your toolkit when a chart isn't enough.

    • Pandas
    • Visualisation
    • SQL

  • Research Analyst

    You dig into a question, run the numbers and write up what you found.

    • Statistics
    • Python
    • Writing

Swipe for all three

Titles vary by company. In the conversation we'll tell you honestly which of these fit your background.

Six modules. One at a time.

This is the map. The week-by-week plan, tools and projects come when you ask for them.

  1. 01

    Python for data, from zero

  2. 02

    Statistics and probability you'll actually use

  3. 03

    Exploring data with Pandas and charts

  4. 04

    Your first machine learning models

  5. 05

    Judging a model honestly: evaluation and tuning

  6. 06

    Capstone: a prediction project, start to finish

Want the full week-by-week plan?
Tools, projects, schedule and fees. We'll send it on WhatsApp.

Tools you'll use

  • Python
  • NumPy
  • Pandas
  • Matplotlib
  • scikit-learn
  • Jupyter

What you'll make.

A notebook that predicts who is about to cancel, with the score and what it means.

churn_model.ipynb
# who is about to cancel?
from sklearn.ensemble import RandomForestClassifier
model = RandomForestClassifier(n_estimators=200)
model.fit(X_train, y_train)
print(model.score(X_test, y_test))
Accuracy on unseen viewers0.87

Illustrations of capstone work, drawn with practice data. Not screenshots of client or company systems.

Problems from brands you already use.

Three of the cases you'll work through in this program.

  • NetflixData Science

    Who is about to cancel?

    Build a model that spots viewers drifting away, and explain what it found.

    • Python
    • scikit-learn
    • Evaluation
  • SpotifyData Science

    What makes a song a hit?

    Explore track features and test how well they predict popularity.

    • Pandas
    • Regression
    • Visualisation
  • PhonePeData Science

    Is this payment a fraud?

    Train a classifier on lopsided transaction data and decide what a false alarm really costs.

    • Classification
    • Metrics
    • Python

Swipe for all three

Learning case studies built on public information and practice datasets. DATAVERSE is not affiliated with these companies.

What happens before your first interview.

Here is the work we do with you before you apply anywhere.

  1. From month 1

    A LinkedIn worth opening

    Profile rebuilt with a mentor, one section at a time.

  2. Every module

    Projects, reviewed

    Each one checked and sent back until it's portfolio ready.

  3. Final stretch

    Mock interviews

    Practice rounds with feedback you can act on the same day.

  4. When you're ready

    Introductions

    We share openings and refer you where your work fits.

Straight answers.

Is two months enough to become a data scientist?
No, and we won't pretend it is. Two months is enough to build a real foundation and one solid project. Most people then go deeper with another program or keep building on their own. We'll tell you honestly which makes sense for you.
How much maths do I need?
School-level maths is enough to begin. We teach the statistics you need as we go, with examples instead of proofs.
Do I need to know Python already?
No. Python is taught from the first line. If you already know some, the first weeks will simply feel easier.
What will I have at the end?
A working prediction project, built and explained by you, and a clear idea of whether data science is your thing.

Ready when you are.

Ask for the full plan for Data Science. If it's not the right fit, we'll say so.