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Python for Data Science Bootcamp 2022: From Zero to Hero
Installation and Setup
Installing Python and Jupyter Notebook through Anaconda (3:43)
Jupyter Notebook Interface (10:00)
Cell Types and Cell Modes (7:35)
Most Common Keyboard Shortcuts in Jupyter Notebook (5:03)
Read This Before You Start (+Cheat Sheet for The Course)
Python Crash Course
Hello World (3:48)
Data Types (8:37)
Variables (7:40)
Lists (24:18)
Dictionary (10:34)
If Statement (6:12)
For loop (5:42)
Function (7:08)
Modules (3:40)
Intro to Pandas and Numpy
Section Overview + All The Files for This Section
Introduction to Pandas (6:24)
How to Create a Dataframe (16:23)
Different Ways to Display a Dataframe (6:48)
Basic Attributes, Methods and Functions (11:50)
Selecting One Column from a Dataframe (5:53)
Selecting Two or More Columns from a Dataframe (5:35)
Add New Column to a Dataframe (Simple Assignment) (9:59)
Add New Column to a Dataframe with assign() and insert() (6:41)
Operations on Dataframes (columns and rows) (8:10)
The value_counts() method (4:10)
Important Note
Sort a Dataframe with sort_values() (9:37)
The set_index() and sort_index() methods (6:56)
Rename Columns and Index with rename() (5:58)
Exercise for this Section
Project 1 - Web Scraping with Pandas
Part 1 (7:31)
Part 2 (19:44)
Part 3 (11:58)
Filtering Data
Download All The Files Used In this Section
Filter a Dataframe Based on 1 Condition (14:14)
Creating a Conditional Column from 2 Choices np.where() (10:29)
Filter a Dataframe Based on 2 or More Conditions (12:07)
Creating a Conditional Column from More Than 2 Choices np.select() (12:51)
The .isin() Method (7:17)
Find Duplicate Rows with the .duplicated() Method (17:55)
Drop Duplicate Elements with the .drop_duplicates() Method (10:17)
Get and Count Unique Values The unique() and nunique() methods (3:54)
Data Extraction
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Differences between the loc() and iloc() method (7:15)
First Look at The Dataset Setting Index and Selecting Columns (5:31)
Selecting elements by index label with .loc() (20:20)
Selecting elements by index position with .iloc() (13:02)
Set New Value for a Cell in a Dataframe (9:47)
Drop Rows Or Columns from a Dataframe (9:36)
Create Random Sample with the sample() Method (7:44)
Filter a DataFrame with the query() method (12:29)
The apply() Method (6:09)
Lambda function + apply() method (17:52)
Make a Copy of a Dataframe with copy() (Deep Copy vs Shallow Copy) (7:18)
Reshaping and Pivoting Dataframes
Introduction to Pivot Tables in Pandas (4:43)
The pivot() method (6:05)
The pivot_table() method (8:08)
Project 2: Making Static and Interactive Data Visualization
Project Overview (+Exercise)
Dataset Overview and Making Pivot Table (9:35)
Lineplot (4:24)
Barplot (7:44)
Piechart (3:41)
Boxplot (3:34)
Histogram (1:29)
Scatterplot (4:44)
Save Plot and Export Pivot Table (3:06)
Interactive Visualization with Pandas (27:12)
GroupBy and Aggregate Function
Dataset Overview (+Files for this Section) (6:50)
The agg() method (14:50)
The Split-Apply-Combine Strategy (7:09)
The groupby() method (13:43)
The groupby() and agg() method (10:58)
The groupby() and lambda function (9:46)
The filter() method (7:37)
Merging and Concatenating DataFrames
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Exploring The Dataset (5:38)
Concatenate Vertically (12:04)
Cconcatenate Horizontally (11:58)
Inner Joins (9:21)
Full Join and Exclusive Full Join (18:31)
Left Join and Exclusive Left Join (24:11)
Right Join and Exclusive Right Join (19:40)
Regular Expressions
Section Overview
Regex Metacharacters and Flags (13:20)
Quantifiers (+Greedy and Lazy Matches) (9:33)
More Metacharacters (16:43)
search() and findall() (7:20)
Exercises (9:30)
Project 3 - Data Cleaning with Pandas
Dataset Overview (3:46)
Identify Missing Data with the isnull() Method (10:28)
Dealing with Missing Data: Remove a column or row with .drop, .dropna or .isnull (12:35)
Dealing with Missing Data: Replace NaN by the mean, median, mode with .fillna() (14:06)
Extracting data with split() and extract() methods (15:44)
How to Identify and Deal with Outliers (17:26)
Dealing with inconsistent capitalization lower(), upper(), title() (4:52)
Remove blank spaces with strip(), lstrip(), and rstrip() (4:41)
Replace strings with replace() or sub() (8:14)
Machine Learning with Python
Section Overview (+Files For This Section)
Dataset Overview (2:23)
Linear regression (3:31)
Linear Regression with Statsmodels - Simple Linear Regression (11:14)
Linear Regression with Statsmodels - Multiple Linear Regression (3:46)
Linear Regression with sklearn (4:56)
Project 4: Text Classification with scikit-learn
The Dataset, Selecting a Sample Datafame and The Problem to Solve (8:35)
Dealing with Imbalanced Classes: Undersampling and Oversampling (17:19)
Splitting data into train and test set (6:54)
Text Representation with Bag of Words: CountVectorizer and TF-IDF (17:59)
Turning our text data into numerical vectors (7:00)
Model Selection: Types of Machine Learning Algorithms for out Project (2:05)
Support Vector Machines (SVM) (6:23)
Decision Tree (4:34)
Naive Bayes (5:11)
Logistic Regression (2:22)
Model Evaluation - Confusion Matrix (6:32)
Mean Accuracy (3:31)
F1 Score (4:55)
Classification Report (1:44)
Tuning the Model - GridSearchCV (3:38)
ChatGPT for Data Science
Section Overview
Doing Data Science Tasks with ChatGPT (9:58)
Hello World
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