Yiga AI

Career track

Data Scientist with Python

16 courses, 120 hours, ending in Data Scientist Interview Prep.

Yiga AI

Intermediate → Advanced en

Intermediate → Advanced · Career Track · 5–6 months at 10 hrs/week

Duration
5–6 months
Effort
10 hrs/week
Courses
16
Hands-on exercises
289
Projects
16
Certificate
Included
Your pace≈ 5–6 months · 246 hours total

What you'll learn

16 courses in this track

  1. 01Introduction to Deep LearningIntermediate · 16.7 hours · 20 lessonsNeural networks · Keras · Training · +1 more
  2. 02Project: Crop Disease Detection from ImagesAdvanced · 19.3 hours · 24 lessonsData collection · CNN · Evaluation · +1 more
  3. 03Statistical ThinkingIntermediate · 14 hours · 16 lessonsDistributions · Sampling · Bootstrapping · +1 more
  4. 04Data Scientist Interview PrepIntermediate · 14.1 hours · 17 lessonsCase studies · Coding rounds · Stats questions · +1 more
  5. 05Unsupervised LearningIntermediate · 14 hours · 16 lessonsK-means · Hierarchical · PCA · +1 more
  6. 06Project: Loan Default Prediction (Fintech)Advanced · 19.3 hours · 24 lessonsBusiness framing · Modelling · Fairness check · +1 more
  7. 07Python for Data Science RefresherBeginner · 14 hours · 16 lessonspandas · NumPy · Plotting · +1 more
  8. 08Natural Language ProcessingIntermediate · 16.7 hours · 20 lessonsTokenisation · TF-IDF · Sentiment · +1 more
  9. 09Deploying Models with FastAPIIntermediate · 14 hours · 16 lessonsREST APIs · Serialisation · Docker · +1 more
  10. 10Tree-Based Models & Gradient BoostingIntermediate · 14 hours · 16 lessonsDecision trees · Random forest · XGBoost · +1 more
  11. 11Experiment Design & A/B TestingIntermediate · 14 hours · 16 lessonsDesign · Power · Analysis · +1 more
  12. 12Supervised Learning with scikit-learnIntermediate · 16.7 hours · 20 lessonsRegression · Classification · Train/test · +1 more
  13. 13Model Evaluation & TuningIntermediate · 14 hours · 16 lessonsCross-validation · Grid search · ROC/AUC · +1 more
  14. 14Time Series AnalysisIntermediate · 16.7 hours · 20 lessonsTrends & seasonality · ARIMA · Prophet · +1 more
  15. 15Feature EngineeringIntermediate · 14 hours · 16 lessonsEncoding · Scaling · Selection · +1 more
  16. 16Machine Learning ExplainabilityIntermediate · 14 hours · 16 lessonsSHAP · Feature importance · Fairness · +1 more

About this track

What you’ll learn.

1

Python for Data Science Refresher

2

Statistical Thinking

3

Supervised Learning with scikit-learn

4

Unsupervised Learning

Skills you’ll gain

Data

How this compares

Comparable to DataCamp Data Scientist with Python · Coursera IBM Data Science. This track is 246 hours of study — about 5–6 months at 10 hrs/week.

Career outcomes

Turn learning into forward motion.

Build job-ready evidence for roles that use these skills every day.

Data Analyst

Turn raw information into decisions people can act on.

Local salary guide · UGX 2M–6M per month

Curriculum

A clear path, course by course.

Complete the courses in order so each new skill builds on the last.

  1. 1

    Course 1 of 16

    Introduction to Deep Learning

    Neural networks, Keras, Training, and more.

    Intermediate · Course · 4 weeks at 5 hrs/week

    17 hours · 20 lessons · 20 exercises · 4 quizzes · 1 project

    View course
  2. 2
  3. 3

    Course 3 of 16

    Statistical Thinking

    Distributions, Sampling, Bootstrapping, and more.

    Intermediate · Course · 3 weeks at 5 hrs/week

    14 hours · 16 lessons · 16 exercises · 4 quizzes · 1 project

    View course
  4. 4

    Course 4 of 16

    Data Scientist Interview Prep

    Case studies, Coding rounds, Stats questions, and more.

    Intermediate · Course · 3 weeks at 5 hrs/week

    14 hours · 17 lessons · 17 exercises · 4 quizzes · 1 project

    View course
  5. 5

    Course 5 of 16

    Unsupervised Learning

    K-means, Hierarchical, PCA, and more.

    Intermediate · Course · 3 weeks at 5 hrs/week

    14 hours · 16 lessons · 16 exercises · 4 quizzes · 1 project

    View course
  6. 6
  7. 7
  8. 8

    Course 8 of 16

    Natural Language Processing

    Tokenisation, TF-IDF, Sentiment, and more.

    Intermediate · Course · 4 weeks at 5 hrs/week

    17 hours · 20 lessons · 20 exercises · 4 quizzes · 1 project

    View course
  9. 9

    Course 9 of 16

    Deploying Models with FastAPI

    REST APIs, Serialisation, Docker, and more.

    Intermediate · Course · 3 weeks at 5 hrs/week

    14 hours · 16 lessons · 16 exercises · 4 quizzes · 1 project

    View course
  10. 10
  11. 11
  12. 12
  13. 13

    Course 13 of 16

    Model Evaluation & Tuning

    Cross-validation, Grid search, ROC/AUC, and more.

    Intermediate · Course · 3 weeks at 5 hrs/week

    14 hours · 16 lessons · 16 exercises · 4 quizzes · 1 project

    View course
  14. 14

    Course 14 of 16

    Time Series Analysis

    Trends & seasonality, ARIMA, Prophet, and more.

    Intermediate · Course · 4 weeks at 5 hrs/week

    17 hours · 20 lessons · 20 exercises · 4 quizzes · 1 project

    View course
  15. 15

    Course 15 of 16

    Feature Engineering

    Encoding, Scaling, Selection, and more.

    Intermediate · Course · 3 weeks at 5 hrs/week

    14 hours · 16 lessons · 16 exercises · 4 quizzes · 1 project

    View course
  16. 16

Applied learning project

Finish with work you can show.

Bring the track together in Machine Learning Explainability. Apply what you have learned to a practical challenge and explain the decisions behind your solution.

  • Python for Data Science Refresher
  • Statistical Thinking
  • Supervised Learning with scikit-learn
Joel N., Career readiness

Learn from experts

Joel N.

Career readiness

Connects technical learning to portfolios, interviews and confident career moves.

Amina Hassan, Yiga learner

“The track removed the uncertainty. I knew what to learn next and what to show employers.”

Amina Hassan

Career switcher, Dar es Salaam

Frequently asked questions

Before you begin.

Do I need prior experience?

This track is designed for intermediate → advanced learners. Start with the first course and follow the numbered path so each skill builds naturally.

How long will the track take?

The track contains 16 courses and about 246 hours of learning. Most learners finish in around 5–6 months at 10 hrs/week.

Can I learn on my phone?

Yes. Yiga lessons are designed to work across phones and computers, so you can keep learning on the device available to you.

Will I earn a credential?

You can earn a shareable Yiga credential after completing the required learning and assessment activities in the track.

Is financial support available?

Scholarship and financial-aid options are available for eligible learners. Contact the Yiga support team before enrolling for guidance.

Ready when you are

Start this track today.

Begin with course 1 of 16, Introduction to Deep Learning, and work through the path at your own pace.

Start this track View first course

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