Python + Data Science : Practical Guide [13 Hours]

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction

Chapter 2: Environment

Lecture 1: Introduction to data science

Lecture 2: Installing dependencies

Lecture 3: Real world examples

Lecture 4: Using Anaconda and Jupyter notebooks

Chapter 3: Integers and Strings

Lecture 1: Introduction to variables

Lecture 2: Integers, floats, and math operators

Lecture 3: Strings and indexing part 1

Lecture 4: Strings and indexing part 2

Lecture 5: Using Modulo with Strings

Lecture 6: Problem

Lecture 7: Answer

Chapter 4: If Statements and Basic Programming Logic

Lecture 1: Booleans and Comparison Operators

Lecture 2: If / Else Statements

Lecture 3: Elif and Logic Operators

Lecture 4: Try / Except Statements

Lecture 5: Finally Statements and Advance Try/Except Logic

Lecture 6: Exception Types

Lecture 7: Problem

Lecture 8: Answer

Chapter 5: Lists, Tuples, Dictionaries and For/While Loops

Lecture 1: Lists and tuples

Lecture 2: Dictionaries

Lecture 3: For loops

Lecture 4: While loops

Lecture 5: Loop logic

Lecture 6: List/Dictionary comprehensions

Lecture 7: Problem

Lecture 8: Answer

Chapter 6: Functions and Packages

Lecture 1: Scripting in Python

Lecture 2: Functions

Lecture 3: Function parameters and scope

Lecture 4: Packages and pip

Lecture 5: Reading Files and the with statement

Lecture 6: Writing Out to Files

Lecture 7: Problem

Lecture 8: Answer

Chapter 7: Pandas and Data Frames

Lecture 1: Pandas and data Frames

Lecture 2: Pandas part 2

Lecture 3: Introduction to statistics

Lecture 4: Statistics part 2

Lecture 5: Practical analysis

Lecture 6: Problem

Lecture 7: Answer

Chapter 8: Visualization – Scatter Plots, Bar Plots

Lecture 1: Introduction to visualization

Lecture 2: Plotting and styling

Lecture 3: Scatter plots

Lecture 4: Bar Plots and standard deviation

Lecture 5: Problem

Lecture 6: Answer

Chapter 9: Scraping the Web with Python

Lecture 1: Scraping and HTML

Lecture 2: Building a simple crawler

Lecture 3: Scraping data from HTML

Lecture 4: Problem

Lecture 5: Answer

Chapter 10: Basics of Natural Language Processing (NLP)

Lecture 1: Introduction to NLP

Lecture 2: Basic NLP

Lecture 3: Introduction to sentiment analysis

Lecture 4: Problem

Lecture 5: Answer

Chapter 11: Introduction to Machine Learning

Lecture 1: Introduction to machine learning

Lecture 2: Introduction to reinforcement learning

Lecture 3: Introduction to random forest modeling

Lecture 4: Introduction to deep learning

Lecture 5: Problem

Lecture 6: Answer

Chapter 12: Optional Classes

Lecture 1: Python Style Guidelines

Lecture 2: Navigating Directories and os Package

Lecture 3: Creating a Class Type

Chapter 13: Project #1 – Analyze and visualize data on Kaggle

Lecture 1: Project 1 – Analyze and visualize data on Kaggle

Lecture 2: Project 1 Answer

Chapter 14: Project #2 – Natural language processing

Lecture 1: Project 2 – Natural language processing

Lecture 2: Project 2 Answer

Chapter 15: Project #3 – Create a simple support vector machine

Lecture 1: Project 3 – Create a simple support vector machine

Lecture 2: Project 3 Answer

Chapter 16: BONUS LECTURE

Lecture 1: Bonus Lecture: More Interesting Stuff, Offers and Discounts