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Cover of AIML 111 Data Science with Python

AIML 111

Data Science with Python

Programming, Analysis and Visual Storytelling

Every organisation now collects data: sales and stock records, applications and admissions, sensor readings, prices, clicks and messages. Very few use that data well. The gap is not a shortage of software. It is a shortage of people who can turn a vague question into a precise one, get the right data, clean it honestly, analyse it with sound statistics, and present the answer so that a decision-maker understands it and acts on it. This book trains you to be one of those people.

17 chapters in 5 parts, 277 pages. Editor-in-Chief: Olusola Sayeed Ayoola. Published by RAIN, Ibadan, 2026.

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Contents

Open a chapter to see its sections. Each chapter ends with a QR code for its free assessment.

1The Data Science Process
  • 1.1 What data science is
  • 1.2 The CRISP-DM process
  • 1.3 Kinds of data
  • 1.4 Your working environment
  • 1.5 Reproducibility

Take the Chapter 1 assessment

2Python in Depth for Data
  • 2.1 Iterators, generators and lazy evaluation
  • 2.2 Single-pass statistics
  • 2.3 Functional tools and comprehensions
  • 2.4 Defensive data code
  • 2.5 Reusable modules
  • 2.6 Performance

Take the Chapter 2 assessment

3Numerical Computing with NumPy
  • 3.1 The ndarray
  • 3.2 Vectorisation and broadcasting
  • 3.3 Indexing and selection
  • 3.4 Linear algebra
  • 3.5 Random numbers and simulation

Take the Chapter 3 assessment

4Data Frames with pandas
  • 4.1 Series, DataFrames and the index
  • 4.2 Reading data
  • 4.3 Selecting, filtering and sorting
  • 4.4 Group-by: split, apply, combine
  • 4.5 Merging and joining
  • 4.6 Reshaping

Take the Chapter 4 assessment

5Data Cleaning and Preparation
  • 5.1 Missing data
  • 5.2 Duplicates, inconsistent categories and outliers
  • 5.3 Cleaning text
  • 5.4 Dates, times and time zones
  • 5.5 Constructing and encoding features
  • 5.6 Case study: cleaning a Nigerian market-price dataset

Take the Chapter 5 assessment

6Data from Databases and the Web
  • 6.1 Databases from Python
  • 6.2 Web APIs
  • 6.3 Web scraping
  • 6.4 Storing results

Take the Chapter 6 assessment

7Descriptive Statistics
  • 7.1 Measures of centre
  • 7.2 Measures of spread
  • 7.3 Shape
  • 7.4 Relationships between two variables

Take the Chapter 7 assessment

8Probability and Distributions
  • 8.1 Probability
  • 8.2 Random variables
  • 8.3 Common distributions
  • 8.4 The law of large numbers and the central limit theorem

Take the Chapter 8 assessment

9Inferential Statistics
  • 9.1 Sampling
  • 9.2 Confidence intervals
  • 9.3 Hypothesis testing
  • 9.4 Errors and power
  • 9.5 Case study: an A/B test of the application page

Take the Chapter 9 assessment

10Visualisation Principles
  • 10.1 Perception
  • 10.2 The grammar of graphics
  • 10.3 Choosing a chart for the question
  • 10.4 Honest charts
  • 10.5 Dashboards versus reports

Take the Chapter 10 assessment

11Plotting with Matplotlib, Seaborn and Plotly
  • 11.1 Matplotlib
  • 11.2 Seaborn
  • 11.3 Interactive charts with Plotly
  • 11.4 Exporting figures

Take the Chapter 11 assessment

12Spreadsheets for Analysts: Microsoft Excel
  • 12.1 Formulas and references
  • 12.2 Functions for analysis
  • 12.3 Lookups
  • 12.4 PivotTables and PivotCharts
  • 12.5 Power Query
  • 12.6 From Excel to Python and back

Take the Chapter 12 assessment

13Power BI
  • 13.1 Getting and transforming data
  • 13.2 Data modelling
  • 13.3 DAX
  • 13.4 Building and publishing reports
  • 13.5 Case study: an admissions dashboard

Take the Chapter 13 assessment

14Tableau
  • 14.1 Connecting to data
  • 14.2 Dimensions, measures and the shelves
  • 14.3 Calculations
  • 14.4 Dashboards and stories
  • 14.5 Power BI and Tableau compared

Take the Chapter 14 assessment

15Exploratory Data Analysis
  • 15.1 A systematic EDA checklist
  • 15.2 Univariate, bivariate and multivariate analysis
  • 15.3 Detecting data leakage early
  • 15.4 Communicating findings

Take the Chapter 15 assessment

16Introduction to Regression and Forecasting
  • 16.1 Simple linear regression
  • 16.2 Multiple regression
  • 16.3 Trends, moving averages and forecasting

Take the Chapter 16 assessment

17Capstone Analytics Project
  • 17.1 Defining the question with a stakeholder
  • 17.2 From raw data to dashboard
  • 17.3 Writing the analytical report
  • 17.4 Presenting to a non-technical audience
  • 17.5 The capstone project

Take the Chapter 17 assessment

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