Chapter 1: Introduction to Python [25 pages]
Description: Python is one of the most popular open-source programming languages and is easy to learn as well.
Topics to be covered:
1. Installation - how to install Python and Anaconda
2. Using Jupyter notebooks
3. Data types in Python
4. Loops and conditional statements
5. Functions
6. Strings and their methods
7. PEP(Python Enhancement Proposal) guidelines
8. Sympy library and solving mathematical problems with Python
Chapter 2: Exploring Containers, Classes & Objects, and Working with Files [25 pages]
Description: Understanding data structures, classes and objects and file handling in Python.
Topics to be covered:
1. Lists
2. Tuples
3. Dictionaries
4. Sets
5. Classes and Objects
6. Working with files
Chapter 3: Regular Expressions [20 pages]
Description: Regular expressions are important to understand as they have a wide range of applications, from natural language processing to working with files to manipulating strings
Topics to be covered:
1. Introduction to regular expressions
2. Meta-characters
3. Python functions for regular expressions
4. Matching characters and sub-expressions
5. Using conditions for matching
Chapter 4: Data Analysis Basics [10 pages]
Description: In this chapter, we will cover the basic terminology in data analysis and the data analysis workflow
Topics to be covered:
1. Basic concepts and definitions
2. Levels of data
3. Data analysis workflow
Chapter 5: Numpy Library [10 pages]
Description: The Numpy library will be explained in detail.
Topics to be covered:
1. Introduction
2. Creating arrays
3. Slicing and dicing
4. Array manipulations
Chapter 6: Data Wrangling with Pandas [50 pages]
Description: Everything related to Pandas, a widely used library used for manipulating and analyzing data
Topics to be covered:
1. Introduction
2. Series and their methods/functions
3. DataFrames and their methods/functions
4. Grouping and Aggregation
5. Merging objects
6. Tidying data
Chapter 7: Data Visualization [20 pages]
Description: An introduction to data visualization, which is crucial for and conveying insights to a new audience
Topics to be covered:
1. Introduction
2. Types of plots
3. Creating plots with the Matplotlib library
4. Using the Pandas library for drawing graphs
5. Visualization with the Seaborn library
Chapter 8: Case Studies [60 pages]
Each case study will start with an overview of the dataset, followed by an in-depth analysis of data that includes data tidying, wrangling, and visualization. The case study would conclude with cr
Gayathri Rajagopalan works for a leading Indian multi-national organization, with ten years of experience in the software and information technology industry. A computer engineer and a certified Project Management Professional (PMP), some of her key focus areas include Python, data analytics, machine learning, and deep learning. She is proficient in Python, Java, and C/C++ programming. Her hobbies include reading, music, and teaching data science to beginners.