Data Analysis with Python

From Raw Data to Real-World Insights

Prof. Philippe Tamla

Begin with a real question, acquire and validate evidence, compute with arrays and labelled tables, investigate patterns, and communicate a reproducible answer. The practical lessons develop the numerical reasoning and data preparation needed for visualization, machine learning and deep learning.

Work through your Python prerequisites in Book 1 when needed. AI support is optional; explaining and checking the analysis yourself is the goal.

Introduction

1. Understanding Data Analysis

• Start here: run, investigate and explain
• Introducing Data Analysis
• The Python Data Stack
• The Data Science Lifecycle
• Data Types and Analytical Workflows
• Example Datasets in the Dynamic Humanities
• From an interesting question to a reproducible investigation

NumPy

2. NumPy Arrays and Selection

• Data Analysis with NumPy
• NumPy Arrays
• Indexing, Slicing, Boolean Selection
• Array shapes, axes, broadcasting and safe copies

3. Computing with NumPy

• Universal Functions
• Compute with arrays and explain numerical choices

Mathematics & Statistics

4. Linear Algebra and Matrix Operations

• Linear Algebra and Matrix Operations

5. Statistical Reasoning and Randomness

• Random Number Generation
• Mathematical and statistical judgment with NumPy
• Probability, sampling and uncertainty before modelling
• Coding Challenge: Exploring Cultural Production

Pandas

6. Pandas: Series and DataFrames

• Data Analysis with Pandas
• Series
• DataFrames
• Selecting, aligning and indexing tables safely

7. Data Input and Output

• Data Input and Output
• Acquire documented data from files, SQL and APIs

8. Cleaning and Validating Data

• Missing Data
• Clean, validate and document a table

9. Combining and Reshaping Data

• Concatenation, Merging, and Joining
• Validate joins and reshape without changing the question

10. Operations, GroupBy and Exploratory Analysis

• GroupBy
• Operations with Pandas
• Explore distributions, groups and defensible comparisons

Applied Analysis

11. Dates, Time and Change

• Work with dates, time, gaps and repeated observations

12. Diagnostic Visualization for Analysis

• Use diagnostic plots to investigate data

13. Reproducible Analysis and Reporting

• Build a reproducible analysis and communicate its limits

14. Preparing for Machine Learning and Deep Learning

• Understand a machine-learning question before choosing a model
• Prepare a trustworthy handoff for machine learning and deep learning

15. Preparing Text and Multimodal Data

• Prepare documents, text and multimodal records

Projects

16. Guided Real-World Investigations

• Guided project: audit a cultural collection
• Guided project: measured data, missingness and subgroup comparisons
• Guided project: build a traceable document table

17. Independent Data Analysis Capstone

• Independent capstone: a documented analysis from question to handoff

Begin reading · Companion data