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Data Manipulation in Python: Master Python, Numpy & Pandas
Python Quick Refresher (Optional)
Welcome to the course! (0:37)
Introduction to Python (0:52)
Setting up Python (2:24)
What is Jupyter? (0:59)
Anaconda Installation: Windows, Mac & Ubuntu (4:15)
How to implement Python in Jupyter? (0:44)
Managing Directories in Jupyter Notebook (2:48)
Input/Output (1:44)
Working with different datatypes (1:06)
Variables (1:50)
Arithmetic Operators (1:48)
Comparison Operators (0:43)
Logical Operators (3:05)
Conditional statements (2:20)
Loops (4:30)
Sequences: Lists (3:18)
Sequences: Dictionaries (2:48)
Sequences: Tuples (1:07)
Functions: Built-in Functions (0:26)
Functions: User-defined Functions (3:14)
Essential Python Libraries for Data Science
Installing Libraries (0:36)
Importing Libraries (1:47)
Pandas Library for Data Science (0:48)
NumPy Library for Data Science (0:51)
Pandas vs NumPy (0:33)
Matplotlib Library for Data Science (0:37)
Seaborn Library for Data Science (0:20)
Fundamental NumPy Properties
Introduction to NumPy arrays (0:45)
Creating NumPy arrays (6:13)
Indexing NumPy arrays (5:45)
Array shape (0:35)
Iterating Over NumPy Arrays (4:57)
Mathematics for Data Science
Basic NumPy arrays: zeros() (1:33)
Basic NumPy arrays: ones() (1:09)
Basic NumPy arrays: full() (1:16)
Adding a scalar (1:41)
Subtracting a scalar (1:04)
Multiplying by a scalar (1:20)
Dividing by a scalar (1:25)
Raise to a power (0:48)
Transpose (0:48)
Element wise addition (1:59)
Element wise subtraction (0:56)
Element wise multiplication (0:58)
Element wise division (1:04)
Matrix multiplication (1:34)
Statistics (2:54)
Python Pandas DataFrames & Series
What is a Python Pandas DataFrame? (0:57)
What is a Python Pandas Series? (0:42)
DataFrame vs Series (0:28)
Creating a DataFrame using lists (3:17)
Creating a DataFrame using a dictionary (1:06)
Loading CSV data into python (1:52)
Changing the Index Column (1:06)
Inplace (1:20)
Examining the DataFrame: Head & Tail (0:36)
Statistical summary of the DataFrame (0:37)
Slicing rows using bracket operators (1:26)
Indexing columns using bracket operators (0:51)
Boolean list (1:15)
Filtering Rows (1:22)
Filtering rows using & and | operators (1:51)
Filtering data using loc() (3:35)
Filtering data using iloc() (2:23)
Adding and deleting rows and columns (2:41)
Sorting Values (1:39)
Exporting and saving pandas DataFrames (1:30)
Concatenating DataFrames (0:59)
groupby() (2:39)
Data Cleaning
Introduction to Data Cleaning (0:37)
Quality of Data (0:47)
Examples of Anomalies (1:04)
Median-based Anomaly Detection (2:41)
Mean-based anomaly detection (2:50)
Z-score-based Anomaly Detection (2:50)
Interquartile Range for Anomaly Detection (4:34)
Dealing with missing values (6:01)
Regular Expressions (6:57)
Feature Scaling (3:17)
Data Visualization using Python
Introduction (0:29)
Setting Up Matplotlib (0:33)
Plotting Line Plots using Matplotlib (1:45)
Title, Labels & Legend (6:46)
Plotting Histograms (1:22)
Plotting Bar Charts (2:04)
Plotting Pie Charts (2:49)
Plotting Scatter Plots (5:43)
Plotting Log Plots (0:41)
Plotting Polar Plots (2:06)
Handling Dates (0:43)
Creating multiple subplots in one figure (3:28)
Exploratory Data Analysis
Introduction (0:19)
What is Exploratory Data Analysis? (0:30)
Univariate Analysis (1:41)
Univariate Analysis: Continuous Data (6:00)
Univariate Analysis: Categorical Data (2:16)
Bivariate analysis: Categorical & Categorical (4:32)
Bivariate analysis: Continuous & Categorical (3:07)
Detecting Outliers (5:34)
Categorical Variable Transformation (4:22)
Time Series in Python
Introduction to Time Series (2:15)
Getting Stock Data using Yfinance (3:14)
Converting a Dataset into Time Series (4:23)
Working with Time Series (3:49)
Time Series Data Visualization with Python (3:03)
Univariate Analysis: Continuous Data
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