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Intermediate · module I2

Working with Data: NumPy, pandas, SQL and Visualisation

By the end: Clean and explore a real dataset with pandas and SQL, compute summaries with NumPy, and present findings with charts

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Covers
Loading, cleaning, querying, exploring and charting real data with Python
Contents
5 chapters, 21 topics
Time
About 4 hours
Level
Intermediate
Your progress
0 / 21

This module builds on Python Programming for AI. We suggest looking at it first, but the choice is yours.

Chapter 1 NumPy

  1. 1.1

    Introduction to NumPy

    Arrays, and why they are faster than lists

    Code: create arrays and compare speed

    10 minBeing written
  2. 1.2

    Array Shape, Indexing and Slicing

    Dimensions, shapes, reshaping and selecting values

    Code: work with a 2D table of marks

    12 minBeing written
  3. 1.3

    Array Operations and Broadcasting

    Maths on whole arrays at once

    Code: scale and normalise marks

    10 minBeing written
  4. 1.4

    Statistics with NumPy

    Mean, median, standard deviation, minimum, maximum and percentiles

    Code: summarise sensor readings

    10 minBeing written

Chapter 2 pandas

  1. 2.1

    Series and DataFrames

    pandas' two main structures

    Code: build a DataFrame from a dictionary

    10 minBeing written
  2. 2.2

    Reading Data from Files

    read_csv, read_excel and read_json

    Code: load a real dataset

    8 minBeing written
  3. 2.3

    Exploring a Dataset

    head, info, describe, value_counts and data types

    Code: first look at a dataset

    10 minBeing written
  4. 2.4

    Selecting and Filtering Data

    loc, iloc and boolean filters

    Code: answer five questions about the data

    12 minBeing written
  5. 2.5

    Sorting, Grouping and Aggregating

    sort_values, groupby and pivot tables

    Code: sales by city and month

    12 minBeing written
  6. 2.6

    Merging and Reshaping Data

    merge, concat, melt and pivot

    Code: combine two tables

    10 minBeing written

Chapter 3 Data Cleaning

  1. 3.1

    Missing Values

    Finding, dropping and filling missing data

    Code: clean a survey dataset

    10 min Activity
  2. 3.2

    Duplicates, Wrong Types and Outliers

    Removing duplicates, fixing types and spotting extreme values

    Code: clean a messy file

    12 minBeing written
  3. 3.3

    Preparing Text and Dates

    String columns and date-time columns

    Code: extract month and weekday

    8 minBeing written

Chapter 4 Databases and SQL

  1. 4.1

    Introduction to Databases

    Tables, rows, keys and why data lives in databases

    Interactive: design a school database

    8 minBeing written
  2. 4.2

    SQL Essentials

    SELECT, WHERE, ORDER BY, GROUP BY and JOIN

    Code: query a database from Python with SQLite

    15 min Read

Chapter 5 Visualisation

  1. 5.1

    Matplotlib Basics

    Line, bar, scatter, histogram and pie charts, with labels

    Code: chart a dataset five ways

    12 minBeing written
  2. 5.2

    Seaborn for Statistical Charts

    Distribution plots, box plots, heatmaps and pair plots

    Code: explore relationships

    12 minBeing written
  3. 5.3

    Exploratory Data Analysis Workflow

    Univariate, bivariate and multivariate analysis

    Project: a complete EDA notebook

    20 min Activity
  4. 5.4

    Data Storytelling with Charts

    Choosing a message and designing charts that support it

    Project: a one-page data story

    12 minBeing written
  5. 5.5

    Building a Data Dashboard

    Several charts that answer one question

    Project: an interactive dashboard

    15 minBeing written
  6. 5.6

    Module Review and Assessment

    Coding exercises and a scored quiz

    Graded data tasks

    15 minBeing written