Python for Data Science Bootcamp 2023: From Zero to Hero

Course Curriculum

Chapter 1: Installation and Setup

Lecture 1: Installing Python and Jupyter Notebook through Anaconda

Lecture 2: Jupyter Notebook Interface

Lecture 3: Cell Types and Modes in Jupyter Notebook

Lecture 4: Most Common Keyboard Shortcuts in Jupyter Notebook

Lecture 5: Read This Before You Start (+Cheat Sheet for The Course)

Chapter 2: Python Crash Course (Optional)

Lecture 1: Optional: Python Crash Course

Lecture 2: Hello World

Lecture 3: Data Types

Lecture 4: Variables

Lecture 5: Lists

Lecture 6: Dictionary

Lecture 7: If Statement

Lecture 8: For loop

Lecture 9: Functions

Lecture 10: Modules

Chapter 3: Introduction to Pandas and Numpy

Lecture 1: Section Overview

Lecture 2: Introduction to Pandas

Lecture 3: How to Create a Dataframe

Lecture 4: Different Ways to Display a Dataframe

Lecture 5: Basic Attributes, Functions and Methods

Lecture 6: Selecting One Column from a Dataframe

Lecture 7: Selecting Two or More Columns from a Dataframe

Lecture 8: Add New Column to a Dataframe (Simple Assignment)

Lecture 9: Add New Column to a Dataframe with assign() and insert()

Lecture 10: Operations on Dataframes (columns and rows)

Lecture 11: The value_counts() method

Lecture 12: Important Note

Lecture 13: Sort a Dataframe with sort_values()

Lecture 14: The set_index() and sort_index() methods

Lecture 15: Rename Columns and Index with rename()

Lecture 16: Exercise for this Section

Chapter 4: Project #1 – Web Scraping with Pandas

Lecture 1: Part 1

Lecture 2: Part 2

Lecture 3: Part 3

Chapter 5: Filtering Data

Lecture 1: Filter a Dataframe Based on 1 Condition

Lecture 2: Creating a Conditional Column from 2 Choices: np.where()

Lecture 3: Filter a Dataframe Based on 2 or More Conditions: &, |

Lecture 4: Creating a Conditional Column from More Than 2 Choices: np.select()

Lecture 5: The isin() Method

Lecture 6: Find Duplicate Rows with the duplicated() method (keep first, last, and false)

Lecture 7: Drop Duplicate Elements with the .drop_duplicates() Method

Lecture 8: Get and Count Unique Values with the unique() and nunique() Methods

Chapter 6: Data Extraction

Lecture 1: Differences between the loc() and iloc() methods

Lecture 2: First Look at The Dataset: Setting Index and Selecting Columns

Lecture 3: Selecting elements by index label with loc()

Lecture 4: Selecting elements by index position with iloc()

Lecture 5: Set New Value for a Cell In a Dataframe

Lecture 6: Drop Rows or Columns from a DataFrame

Lecture 7: Create Random Sample with the sample() Method

Lecture 8: Filter a dataFrame with the query() method

Lecture 9: The apply() method

Lecture 10: Lambda function + apply() method

Lecture 11: Make a Copy of a Dataframe with copy() (Deep Copy vs Shallow Copy)

Chapter 7: Reshaping and Pivoting Dataframes

Lecture 1: Introduction to Pivot Tables

Lecture 2: The .pivot() method

Lecture 3: The pivot_table() method

Chapter 8: Project #2: Making Static and Interactive Data Visualization

Lecture 1: Project Overview (+ Exercise)

Lecture 2: Dataset Overview and Making Pivot Table

Lecture 3: Lineplot

Lecture 4: Barplot

Lecture 5: Piechart

Lecture 6: Boxplot

Lecture 7: Histogram

Lecture 8: Scatterplot

Lecture 9: Save Plot and Export Pivot Table

Lecture 10: Interactive Visualization with Pandas

Chapter 9: GroupBy and Aggregate Function

Lecture 1: Dataset Overview

Lecture 2: The agg() method

Lecture 3: The Split-Apply-Combine Strategy

Lecture 4: The groupby() method

Lecture 5: The groupby() and agg() method

Lecture 6: The groupby() and lambda function

Lecture 7: The filter() method

Chapter 10: Merging and Concatenating Dataframes

Lecture 1: Exploring The Dataset

Lecture 2: Concatenate Vertically

Lecture 3: Concatenate Horizontally

Lecture 4: Inner Joins

Lecture 5: Full Join and Exclusive Full Join

Lecture 6: Left Join and Exclusive Left Join

Lecture 7: Right Join and and Exclusive Right Join

Chapter 11: Regular Expressions

Lecture 1: Section Overview

Lecture 2: Regex Metacharacters and Flags

Lecture 3: Quantifiers (+Greedy and Lazy Matches)

Lecture 4: More Metacharacters

Lecture 5: search() and findall()

Lecture 6: Exercises

Chapter 12: Project #3: Data Cleaning with Pandas

Lecture 1: Dataset Overview

Lecture 2: Identify Missing Data with the isnull() Method