Become a Python Data Analyst

Table of contents

1. Chapter 1 : The Anaconda Distribution and the Jupyter Notebook
   1. The Course Overview
   2. The Anaconda Distribution
   3. Introduction to the Jupyter Notebook
   4. Using the Jupyter Notebook
2. Chapter 2 : Vectorizing Operations with NumPy
   1. NumPy: Python’s Vectorization Solution
   2. NumPy Arrays: Creation, Methods and Attributes
   3. Using NumPy for Simulations
3. Chapter 3 : Pandas: Everyone’s Favorite Data Analysis Library
   1. The Pandas Library
   2. Main Properties, Operations and Manipulations
   3. Answering Simple Questions about a Dataset – Part 1
   4. Answering Simple Questions about a Dataset – Part 2
4. Chapter 4 : Visualization and Exploratory Data Analysis
   1. Basics of Matplotlib
   2. Pyplot
   3. The Object Oriented Interface
   4. Common Customizations
   5. EDA with Seaborn and Pandas
   6. Analysing Variables Individually
   7. Relationships between Variables
5. Chapter 5 : Statistical Computing with Python
   1. SciPy and the Statistics Sub-Package
   2. Alcohol Consumption – Confidence Intervals and Probability Calculations
   3. Hypothesis Testing – Does Alcohol Consumption Affect Academic Performance?
   4. Hypothesis Testing – Do Male Teenagers Drink More Than Females?
6. Chapter 6 : Introduction to Predictive Analytics Models
   1. Introduction to Predictive Analytics Models
   2. The Scikit-Learn Library – Building a Simple Predictive Model
   3. Classification – Predicting the Drinking Habits of Teenagers
   4. Regression – Predicting House Prices