Course 2 of 7 · Beginner

Data ToolsNumPy, pandas, plots and scikit-learn.

Arrays, tables and charts drawn as what they are, so shapes, masks and joins stop being guesswork.

Start with 0.1 Why These ToolsSee the syllabus
5modules
23lessons planned
23ready to study now
100quiz questions so far

Who Data Tools is for and what you will learn

This track is for you if

  • You can write Python functions and loops, and want to work with real tables.
  • Array shapes, masks and joins still feel like guesswork.
  • You have called scikit-learn's fit and predict without knowing what they do.

By the end you can

  • Reason about an array's shape and dtype before an operation runs.
  • Select, filter, group and join tables with pandas.
  • Make charts that answer one question clearly.
  • Use scikit-learn's fit, transform and predict with confidence.

Before you start

Basic Python: variables, loops, functions and lists. The Python track covers all of it.

Every open lesson is free to read. The opening modules are a free sample: a free account opens all of their material. Pro adds it for the rest of the course.

The syllabus5 modules, each ending in its own finish line.

Every lesson is listed, including the ones still in production, so you can see where the track goes. Open lessons have their parts and key terms free to read, and their quiz free with an account.

  1. Module 0 · 1 lesson · videos in production

    Why These Tools

    1. 0.1Why These ToolsSay what this track is for, what the four libraries are, and in what order we take them.
    Finish lineModule overview: start here
  2. Module 1 · 7 lessons · videos in production

    NumPy

    Builds to: The chain's year, in NumPy
    1. 1.1Why NumPySay what an array holds that a list does not, and why that makes it faster and smaller.
    2. 1.2Shape, dtype and ReshapingRead any array's shape and dtype, and reshape without copying.
    3. 1.3Indexing, Slicing and MasksSelect exactly the elements you want, including by condition.
    4. 1.4Vectorised Operations and BroadcastingCompute on whole arrays at once, and predict when shapes combine.
    5. 1.5Aggregations Along AxesSum, average and find extremes over rows or columns.
    6. 1.6Linear Algebra in NumPyWrite the maths of the ML videos directly as code.
    7. 1.7Random Numbers and ReproducibilityGenerate random data you can reproduce.
    Finish lineModule quiz · 15Project: The chain's year, in NumPyInterview questionsModule overview: notebooks, common mistakes, cheat sheet
  3. Module 2 · 7 lessons · videos in production

    pandas

    1. 2.1Meet pandasSay what a DataFrame is and why tabular data gets its own library, by loading one real file and asking it a question.
    2. 2.2Series and DataFramesHold a table with named columns and an index.
    3. 2.3Selecting and FilteringGet the rows and columns you need.
    4. 2.4Cleaning DataFix types, missing values and duplicates.
    5. 2.5groupby and AggregationSplit, apply, combine: statistics per group.
    6. 2.6Merging, Joining and ReshapingCombine tables and change their shape.
    7. 2.7Dates and Time Series in pandasParse dates and resample time series.
    Finish lineModule quiz · 11PracticeInterview questionsModule overview: notebooks, common mistakes, cheat sheet
  4. Module 3 · 5 lessons · videos in production

    Visualisation

    1. 3.1Why Matplotlib and seabornSay why we draw data, what matplotlib and seaborn are and how one sits on the other, what they are used for, and what this module covers and leaves to other tracks.
    2. 3.2Meet MatplotlibDraw the first plot, label it, and choose between a line and a scatter.
    3. 3.3Matplotlib FundamentalsUnderstand the figure–axes model and draw the four basic plots.
    4. 3.4Several Plots and Readable ChartsPut several charts on one figure, and make one a reader can not misread.
    5. 3.5The Same Plots with seabornDraw the basic plots in one call each from a DataFrame, and see what seaborn does by default.
    Finish lineModule quiz · 10PracticeInterview questionsModule overview: notebooks, common mistakes, cheat sheet
  5. Module 4 · 3 lessons · videos in production

    The scikit-learn API

    1. 4.1Meet scikit-learnSay what scikit-learn is for, and fit and score a first model against a baseline.
    2. 4.2Estimators: fit, predict, transformUse any scikit-learn estimator with the same verbs, and tell what you set before `fit` from what it learned during it.
    3. 4.3Pipelines, and Where scikit-learn Goes NextChain a transformer and a model into one object, score it with one line of cross-validation, and know which ML video takes each topic further.
    Finish lineModule quiz · 11PracticeInterview questionsModule overview: notebooks, common mistakes, cheat sheet

How Data Tools is taughtone lesson, opened up.

Lessons in this track are built the same way. Here is Tools 1.6 as its outline lists it: what it builds on, its parts in order, and everything around it in its module.

Tools 1.6

Linear Algebra in NumPy

Write the maths of the ML videos directly as code.

The lesson, part by part
  1. The feature matrix, and one prediction by hand
  2. The same prediction in a Python loop over every row
  3. X at w: the matrix product, every row at once
  4. Star is element wise, and at is the matrix product
  5. Transpose with dot T, and the two pieces the solve needs
  6. A one dimensional array has no orientation, and the None index
  7. The normal equations, and np dot linalg dot solve
  8. Why not np dot linalg dot inv: the condition number, and lstsq
  9. Checking the weights against scikit-learn
  10. Recap and your challenge

Key term · Matrix productRow-column combination written @

After the lesson
  • A 5-question quiz, marked the moment you answer
  • Practice at levels A to C, with values to check against
  • Where the idea is used in real work
In its module · 1. NumPy
  • 4 notebooks to run the examples yourself
  • 22 interview questions with model answers
  • The module project: The chain's year, in NumPy, set with no template
Open the lesson See the module

What's inside a lessonin Data Tools, published so far.

Counted from this track's published lessons. A part still being written is left out until it is published.

  1. Learn

    The lesson

    Short parts, each with its key terms, and a free video where one is recorded.

    23 lessonsNo account
  2. Check yourself

    Quiz

    Multiple-choice questions on the lesson, marked the moment you answer.

    23 lessonsFree account
  3. Practise

    Assignments

    Exercises to do yourself, starting from the lesson's own challenge.

    22 lessonsPro
  4. Apply it

    Real-world applications

    Where each idea is used in real work, from data analysis to production models.

    7 lessonsPro
  5. Prepare

    Interview questions

    Questions an interviewer asks about each module, with model answers.

    48 questionsPro
  6. Build

    Projects and notebooks

    Module projects, set as a problem with no template and checked by a notebook, and notebooks to run the examples yourself.

    1 project, 15 notebooksPro

What you will builda module project, set with no template.

Each project is a problem to solve on your own, then checked against test values or a notebook.

Start free, go further with Pro

Compare the plans

Free

In Data Tools:

  • All 23 lessons' parts and key terms, no account
  • Quizzes and saved progress with a free account
  • Every layer of modules 0 and 1, the free sample
Start with 0.1 Why These Tools
Pro

Pricing announced soon.

  • Every course, from Python to deep learning and generative AI
  • Practice problems for every lesson, levels A to C, with values to check
  • Self-checking notebooks that mark your answers as you go
  • Module projects, cheat sheets, common mistakes and interview answers
  • A certificate for every course you complete
  • Weekly live sessions with Gaurav
  • Everything in Free

Worked articles on Data Tools

All Data Tools resources

Where Data Tools is used

All AI applications

Questions about Data Tools

Why learn these four libraries?

Almost every machine learning notebook loads data with pandas, computes with NumPy, plots with Matplotlib and models with scikit-learn.

Which versions does the course use?

The versions Google Colab ships, checked when each lesson is recorded, including NumPy 2 and pandas 3.

Other courses

See the machine learning path