1.1. Data Value
1.2. Data and Big Data
1.3. Big Data Management
Chapter laboratory exercises: Python, Installation, Introduction, linear programming, etc.

2.1. What is data analysis
2.2. Use of big data
2.3. Data Acquisition and Preparation
2.4. Ethics of Big Data
Chapter Lab Exercises: Python Libraries: datetime, csv, subprocess, pandas, numpy, etc. Excel (.csv), SQLite.

3.1. Data Analysis
Chapter Lab Exercises: Python, Python Libraries: matplotlib, seaborn, date and time, csv, thread, etc.

4.1. Predictive analytics
4.2. Model evaluation
Chapter 4 Lab Exercises: Python, Python Libraries: scipy, sklearn and IPython.display, etc. and an additional application called Graphviz.

5.1. Creating a data story
5.2. The power of visualization
Chapter 5 Lab Exercises: Microsoft Excel, Python, Python Libraries.

6.1. Scale of data analysis
6.2. Introduction to data engineering
6.3. The Big Data Plan
6.4. Image processing laboratories
Chapter 6 Lab Exercises: Python, Python Libraries: picamera, scipy, sklearn, etc.