Data Analysis with Python Full Course Tutorial
Data Analysis with Python Full Course Tutorial
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Jupyterlab Install: https://jupyter.org/install
Seaborn: https://seaborn.pydata.org/
Pandas: https://pandas.pydata.org/
Exercise Files: https://tinyurl.com/53yy6xfj
Who it's for: Who it is for: This training is for learners who want to turn raw datasets into actionable insights using Python—especially those who already have a basic Python foundation (variables, data types, control flow, plus lists/dictionaries/functions) and are ready to apply common data-analysis libraries like NumPy, pandas, and seaborn to real-world data cleaning, exploration, and visualization workflows. It’s particularly relevant for people who want to use data analysis to diagnose issues, spot trends, and make better operational or business decisions (e.g., improving performance over time, increasing efficiency, and supporting profitability).
What it is: Python is the programming language you use to do data analysis efficiently—especially when you combine it with libraries like pandas (for working with datasets) and visualization tools like seaborn to manipulate, explore, and interpret data to discover real-world insights. In this course, you run that Python work inside JupyterLab, a web-based environment (the next iteration of Jupyter Notebook) that lets you work with notebooks alongside terminals and text editors, keep multiple projects organized in one place, and manage your analysis in a “notebook with multiple pages” style workflow. It runs through a Python kernel on a local server you launch (often after installing via pip install jupyterlab), and it includes productivity features like IPython “magic commands” (single-% for line magics and double-%% for cell magics) to help you navigate your workspace and measure or capture code
What you'll learn: You will learn a practical, end-to-end data analysis lifecycle in Python: importing data from CSV, JSON, Stata files, and SQLite databases; cleaning data by selecting relevant fields, validating missing values, and handling outliers; engineering features with apply and lambda functions; combining datasets with merges/joins; reshaping data from wide to long format; summarizing data with grouping/aggregation and pivot tables; working with time series data through resampling, reindexing, rolling averages, and running totals; and building and evaluating basic predictive models using correlation and scikit-learn linear and multiple regression, including handling categorical variables with encoding.
Start 0:00
Introduction 0:08
Establishing Datasets 4:47
Starting Jupyterlab and Using It 7:20
Using Split Windows and Magic Commands 13:10
Magic Commands and Guide 13:38
End Magic Commands 16:00
Pandas Dataframe Examples and Table 17:38
Reading a CSV and creating a Dataframe 20:17
Creating a Dataframe and Printing the Dataframe Head 25:01
.info and Data Validation 29:25
Select dtypes and Describe 34:12
Categories and Count 39:36
.agg Function and Dates 43:40
.iloc[] and Indexing 45:39
Booleans and .loc With Categoricals 48:32
Create a Mask 1:01:50:20
Using a Mask 1:05:58:29
Beginner Conclusion 1:11:05:16
Intermediate Introduction 1:11:56:02
Read CSV and .sort_index() 1:14:22
WS-001 Plot and How to Read a Plot 1:18:56:04
Histogram and Box Plot 1:22:53
Seaborn Introduction and Scatter Plots in Seaborn and Pandas 1:28:48
Scatter Plot and Regression Line in Seaborn 1:32:34
Mean and Bar Plot 1:35:59
Seaborn Barplot and Using Mean as an Estimator 1:41:24
Seaborn Relational Plot 1:41:58
Frequency of Categoricals and ViolinPlot 1:44:39
ViolinPlot and Histogram with Trend Line 1:51:19
Intermediate Conclusion 1:57:11
Advanced Introduction and Importing Data 1:59:31
Pulling Data from a Database Part 1 2:03:49
Pulling Data From a Database Part 2 2:08:16
Simplified Dataframes and SettingWithCopyWarning Part 1 2:11:15
Simplified Dataframes and SettingWithCopyWarning Part 2 2:14:32
Apply 2:20:42
Lambda Functions 2:23:22
Merge Two Databases 2:26:04
Long Tables 2:32:51
Data Visualization for Long Tables 2:35:49
Pivot Table and DateTime Indexing 2:40:14
Date Range and Mean Daily CPU Usage 2:52:31
#pythontutorial #dataanalysis #pythontutorial
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