Learn to transform raw datasets into actionable insights through this comprehensive 3-hour and 16-minute tutorial covering the complete data analysis workflow in Python. Master essential libraries including pandas for data manipulation, NumPy for numerical operations, and seaborn for data visualization while working in JupyterLab, a powerful web-based development environment. Begin with fundamental concepts by establishing datasets, navigating JupyterLab's interface, and utilizing magic commands for enhanced productivity. Progress through creating and manipulating pandas DataFrames, reading CSV files, validating data with .info() and .describe() methods, and performing data selection using .iloc[], .loc[], and boolean indexing techniques. Explore data visualization fundamentals including histograms, box plots, scatter plots with regression lines, bar plots, violin plots, and relational plots using both pandas and seaborn libraries. Advance to sophisticated data operations including importing data from multiple sources such as CSV, JSON, Stata files, and SQLite databases, applying lambda functions for data transformation, merging datasets, reshaping data from wide to long format, and creating pivot tables. Master time series analysis techniques including DateTime indexing, date range operations, resampling, and calculating rolling averages and running totals. Conclude with building and evaluating predictive models using correlation analysis and scikit-learn's linear and multiple regression capabilities, including proper handling of categorical variables through encoding methods. Gain practical experience with real-world data cleaning workflows, feature engineering, outlier detection, and the complete data analysis lifecycle from raw data import to actionable business insights.

Syllabus

Start
Introduction
Establishing Datasets
Starting Jupyterlab and Using It
Using Split Windows and Magic Commands
Magic Commands and Guide
End Magic Commands
Pandas Dataframe Examples and Table
Reading a CSV and creating a Dataframe
Creating a Dataframe and Printing the Dataframe Head
.info and Data Validation
Select dtypes and Describe
Categories and Count
.agg Function and Dates
.iloc[] and Indexing
Booleans and .loc With Categoricals
Create a Mask 1:
Using a Mask 1:
Beginner Conclusion 1:
Intermediate Introduction 1:
Read CSV and .sort_index
WS-001 Plot and How to Read a Plot 1:
Histogram and Box Plot
Seaborn Introduction and Scatter Plots in Seaborn and Pandas
Scatter Plot and Regression Line in Seaborn
Mean and Bar Plot
Seaborn Barplot and Using Mean as an Estimator
Seaborn Relational Plot
Frequency of Categoricals and ViolinPlot
ViolinPlot and Histogram with Trend Line
Intermediate Conclusion
Advanced Introduction and Importing Data
Pulling Data from a Database Part 1
Pulling Data From a Database Part 2
Simplified Dataframes and SettingWithCopyWarning Part 1
Simplified Dataframes and SettingWithCopyWarning Part 2
Apply
Lambda Functions
Merge Two Databases
Long Tables
Data Visualization for Long Tables
Pivot Table and DateTime Indexing
Date Range and Mean Daily CPU Usage