Learn Python for Data Science from Scratch -with 10 Projects

Course Curriculum

Chapter 1: Introduction to Python and the Jupyter Notebook

Lecture 1: Introduction

Lecture 2: What is Python?

Lecture 3: Overview of the Jupyter Notebook

Lecture 4: The Print Function

Lecture 5: Basic Arithmetic Functions

Lecture 6: Variables

Lecture 7: Project 1

Lecture 8: Project 1 (Solution)

Chapter 2: Data Types and Structures in Python

Lecture 1: Strings

Lecture 2: Strings Numerical Data Types

Lecture 3: Lists

Lecture 4: Tuples

Lecture 5: Dictionaries

Lecture 6: Project 2

Lecture 7: Project 2 Solution

Chapter 3: Control Flow in Python

Lecture 1: Overview of Control Flow

Lecture 2: Conditional Statements

Lecture 3: For Loops

Lecture 4: While loops

Lecture 5: Project 3

Lecture 6: Project 3 Solution

Chapter 4: Functions and Modules in Python

Lecture 1: Functions

Lecture 2: Lambda Functions

Lecture 3: Modules

Lecture 4: Project 4

Lecture 5: Project 4 Solution

Chapter 5: Introduction to Numpy

Lecture 1: Introduction to Numpy

Lecture 2: Creating arrays in Numpy

Lecture 3: Indexing and Slicing Arrays

Lecture 4: Copy and View in Numpy

Lecture 5: Shape and reshaping arrays

Lecture 6: Basic Operations in Numpy Arrays

Lecture 7: Data Analytics operations in Numpy

Lecture 8: Project 5

Lecture 9: Project 5 Solution

Chapter 6: Introduction to Pandas

Lecture 1: Introduction to Pandas

Lecture 2: Reading in Files in Pandas

Lecture 3: Looking at data in the dataframe

Lecture 4: Accessing, filtering and Sorting data

Lecture 5: Indexing, loc and iloc in Pandas

Lecture 6: Groupby and aggregate functions

Lecture 7: Merge, Join and Concatenate

Lecture 8: Data Cleaning in Pandas 1

Lecture 9: Data Cleaning in Pandas 2

Lecture 10: Data Visualization in Pandas

Lecture 11: Project 6

Lecture 12: Project 6 Solution

Chapter 7: Introduction to Matplotlib

Lecture 1: Introduction to Matplotli

Lecture 2: Basic Plots in Matplotlib

Lecture 3: Project 7

Lecture 4: Project 7 Solution

Chapter 8: Basic Machine Learning with Scikit-Learn

Lecture 1: Introduction to Machine Learning

Lecture 2: Supervised & Unsupervised Learning

Lecture 3: Machine Learning Techniques

Lecture 4: Introduction to Scikit-Learn

Chapter 9: Regression Models with Scikit-Learn

Lecture 1: Introduction to Regression Models

Lecture 2: Building your First Linear Regression Model 1

Lecture 3: Building your First Linear Regression Model 2

Lecture 4: Building your First Linear Regression Model 3

Lecture 5: Building your First Linear Regression Model 4

Lecture 6: Project 8

Lecture 7: Project 8 Solution

Chapter 10: Classification Models with Scikit-Learn

Lecture 1: Introduction to Classification Models

Lecture 2: Building your First Classification Model 1

Lecture 3: Building your First Classification Model 2

Lecture 4: Building your First Classification Model 3

Lecture 5: Building your First Classification Model 4

Lecture 6: Project 9

Lecture 7: Project 9 Solution

Chapter 11: Clustering Models with Scikit-Learn

Lecture 1: Introduction to Clustering Models

Lecture 2: Building your First Clustering Model 1

Lecture 3: Building your First Clustering Model 2

Lecture 4: Project 10

Lecture 5: Project 10 Solution

Chapter 12: Wrap up

Lecture 1: Wrap – Up