Fundamental concepts and techniques for data analysis with Python

INTRODUCTION

Introduction to data analysis with Python

• What is data analysis
• Data analysis with Python
• The Python data analysis toolbox
  • NumPy
  • Pandas
  • SciPy
  • Statsmodels
  • PyMC3
  • Scikit-Learn
  • Matplolib
  • Seaborn
  • Altair
  • Bokeh

Prerequisites

• Basic Python programming
• Basic algebra ideas

PART I: Data wrangling and exploratory data analysis

Basic concepts in data analysis:

• Tabular data
• Non tabular data
• Relational database
• Time series data
• Matrices, vectors, and tensors
• Anatomy of a table
• Types of variables
• Missing data
• Data cleaning
• Data exploration
• Data visualization
• Hypothesis testing
• Causal inference

Data analysis workflow

• Questions
• Data loading
• Data cleaning
• Data formating
• Data exploration and visualization
• Data modeling
• Insight and communication

Case study 1

Data analysis example with real messy data following the standard data analsis workflow

Introduction to NumPy

• What is NumPy
• NumPy set up
• NumPy Arrays
• Array creation
• Vectorization
• Array data type and conversions
• Array mathematics and element-wise operations
• Array manipulation
• Logic functions and array evaluation
• Array indexing
• Array copies
• String operations with NumPy

Introduction to pandas

• What is pandas
• Pandas set up
• Pandas data structures
• Data loading with Pandas
• Data transformation and lambda functions
• Indexing and selecting data
• Merging, joining, and concatenating data
• Reshaping and pivot tables
• Data cleaning and missing values
• Working with text data
• Multi-index and advance indexing

Case study 2

Advance data analysis example with real messy data following the standard data analsis workflow

PART II

Introduction to data visualization with Python

Introduction to matplotlib

Introduction to altair

Case study 3