Courses

Data Science

Statistics, Python, SQL, visualization, and business intelligence

  • 8 lessons
  • 4.5 hours
  • Beginner
  • Graded labs: 2
Start course Course map & resources

What you will learn

  • Summarize a dataset with mean, median and spread, and flag outliers with the IQR rule.
  • Interpret confidence intervals and p-values correctly, and say what an A/B test can and cannot prove.
  • Clean and reshape tabular data with pandas: filter, group, join and handle missing values.
  • Query a database with SQL: joins, GROUP BY, subqueries and window functions.
  • Choose the right chart for a question and avoid misleading scales.
  • Build and validate a predictive model with a train/test split and the right metric.
  • Describe how data pipelines (ETL/ELT) move and clean data, and where they break.
  • Publish a portfolio project that turns one clear question into an insight a stakeholder can act on.

How you are assessed

A data scientist is judged by answers, not code length: your quizzes show sound statistical reasoning, your labs show you can wrangle and query data correctly, and a challenge tests it on a fresh dataset.

To pass this track
  • Complete every Foundations lesson
  • Average on first-try quizzes: at least 70%
  • Graded lab exercises to pass: 2
  • Challenges to solve: 1
  • Lessons

    Own the daily toolkit

    Complete statistics, Python for data, SQL and visualization.

  • Quizzes

    Reason statistically

    Average at least 70% on first-try quizzes about distributions, sampling and chart choice.

  • Lab exercises

    Wrangle and query

    Pass the group-by aggregation lab in Python and the SQL lab (SQLite) against their tests.

  • Challenges

    Face new data

    Solve one data-flavoured challenge without reusing a lesson's code verbatim.

Curriculum

Module 1 · 4 lessons

Analyst fundamentals

Statistics, Python and SQL: the daily toolkit.

0/4
  1. 1.Statistics — Making Sense of Data

    Mean, median, standard deviation, probability distributions, hypothesis testing, and p-values. You can't interpret data without statistics — it's the language data scientists speak.

    40 min 2 exercises 1 quiz
    Challenges: 2 Try in Code Lab
  2. 2.Python for Data — Meet pandas

    Pandas for data manipulation, NumPy for numerical computing, and Jupyter notebooks for interactive analysis. These three tools are in every data scientist's daily workflow.

    40 min 2 exercises 1 quiz Graded labs: 1
    Lab Try in Code Lab
  3. 3.SQL for Data — Ask Your Data Questions

    SELECT, JOIN, GROUP BY, window functions, CTEs, and query optimization. SQL is how data scientists extract insights from databases — it's used daily in every data role.

    35 min 1 exercise 1 quiz Graded labs: 1
    LabChallenge Try in Code Lab
  4. 4.Data Visualization — Charts That Tell Truth

    Matplotlib, Seaborn, and Plotly for Python charts. Tableau and Power BI for business dashboards. Clear visualization is how you turn analysis into decisions others can act on.

    30 min 1 exercise 1 quiz
    Try in Code Lab
Module 2 · 2 lessons

Prediction and pipelines

Machine learning for analysts and how data reaches you.

0/2
  1. 5.Machine Learning for Data Science

    Scikit-learn, regression, classification, clustering, model evaluation, and feature engineering. Data scientists use ML to build predictive models that drive business decisions.

    40 min 2 exercises 1 quiz
    Challenge Try in Code Lab
  2. 6.Data Engineering — The Plumbing of Data

    ETL pipelines, Apache Spark, data warehouses (BigQuery, Snowflake, Redshift), and Airflow for orchestration. Data engineers build the pipelines that data scientists depend on.

    25 min 1 quiz
    Try in Code Lab
Module 3 · 2 lessons

Dashboards and career

BI tools and a portfolio that gets interviews.

0/2
  1. 7.Business Intelligence — Dashboards That Decide

    Power BI, Tableau, and Looker for building executive dashboards. Understanding business KPIs and how to communicate insights to non-technical stakeholders.

    25 min 1 quiz
    Try in Code Lab
  2. 8.Portfolio & Kaggle — Get Hired

    Build a portfolio of real projects on GitHub. Compete on Kaggle to sharpen your skills and benchmark yourself against the world. A strong portfolio beats a degree in data science hiring.

    25 min 1 quiz
    Try in Code Lab

Run a real example

This is the kind of code you will write. Run it, then change it and run it again.

Python

One extreme salary drags the mean far above the median: the first thing a statistician checks. Remove the 140 and compare.

Open in Code Lab

Recommended challenges

Put the skills to work against a puzzle with a flag or a test suite.

See all challenges

Badges

Earned automatically from your progress on this device.

  • First Dataset

    Locked

    Finish your first lesson.

    0 / 1

  • Analyst Toolkit

    Locked

    Complete every fundamentals lesson.

    0 / 4

  • Query Wrangler

    Locked

    Pass 2 graded lab exercises.

    0 / 2

  • Statistical Mind

    Locked

    Score 80% or more on the first try in 4 lesson quizzes.

    0 / 4

  • Data Scientist

    Locked

    Complete every lesson in the track.

    0 / 8

Certificate of completion

Lessons completed: 0 / 8

Complete every lesson to unlock your certificate.