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Python Data Analyst Bootcamp: Process, Analyze & Visualize

Name: Python Data Analyst Bootcamp: Process, Analyze & Visualize
Rating: 4.4 (9 reviews)

The most in-demand Python libraries for data analysis, visualization & dashboards with practice and a portfolio project

Created byOlha Piliaieva

Last updated 5/2025

English

What you'll learn

Learn Python syntax, data types, PEP, functions, and modules from zero by building a small app as a hands-on homework project.
Understand OOP: classes, types, and project code structure to master object-oriented programming concepts and apply them confidently.
Explore key data analysis libraries: NumPy, Pandas, and the newer Polars for efficient data processing and real-world analysis tasks.
Master data visualization with Matplotlib and Seaborn, completing a real stock market analysis project to apply your skills practically.
Gain Git and GitHub skills from basics to advanced, learning version control and project management vital for real-world software development.
Get hands-on with Streamlit, build a price analysis app, and deploy it free on Streamlit Cloud to create a portfolio-worthy project.

Coding Exercises

This course includes our updated coding exercises so you can practice your skills as you learn.

Course content

12 sections • 100 lectures • 12h 5m total length

Introduction3:53

Python Installation6:04
Setting Up for Work: Installing Code Editors3:54
Work with Different Python Versions: Virtual Environments, Pyenv11:55
PEP Guidelines, Understanding Python Syntax6:41
Numeric Types in Python: int, float, and complex6:39
Working with Floating-Point Numbers and Rounding
Working with Integer Variables and Basic Operations
Working with Data Type Conversion
Atomic and Reference Object Types in Python4:59
String Data Type in Python10:30
Working with Strings in Python
List Data Type in Python8:47
Tuple Data Type in Python9:09
Working with Tuples in Python
Boolean Data Type in Python4:54
Using Boolean Values, Logical Expressions, and Conditional Logic
Dictionary Data Type in Python7:22
Working with Dictionaries in Python
Set and Frozenset Data Types in Python8:15
Working with Sets in Python
Binary Sequence Types in Python14:08
Loops and How to Work with Them8:01
Break and Continue: Control Flow Statements in Python7:17
Utilizing Break and Continue for Control Flow in Python
Nested Loops and Conditional Statements8:24
Working with Loops and Nested Data Structures9:30
Implementing a For Loop
List Comprehensions in Python13:55
Efficient Data Manipulation using List Comprehensions in Python
Input() Function in Python7:03

What is a Function? Creating Functions in Python10:08
Positional and Keyword Arguments in Python Functions8:36
Argument Packing and Unpacking Operators in Python Functions8:34
Lambda Functions in Python7:14
What is Scope in Python?8:24
Using try-except in Python Functions10:00
What are Modules in Python: Working with Them. Import Statement16:48
A Decorator as a Design Pattern in Python: How to Use and Implement Them6:19
Creating a QR Code Generator for Your Social Media with Python7:19
Creating a Custom Function
Implementing a Function with Error Handling using try-except
Creating a Lambda Function
Creating a Custom Decorator
Passing Positional and Named Arguments into a Function
Understanding Packing Function Arguments
Understanding and Using Unpacking in Function Arguments
Understanding how Decorators Modify the Behavior of Functions in Python

Object-Oriented Programming (OOP) in Python: Classes and Instances11:02
Writing a Constructor for a Class and Creating an Instance
Creating a Custom Class
Object-Oriented Programming in Python: Class, Static, and Instance Methods9:06
Working with Static Method
Understanding Class Methods
Object-Oriented Programming in Python: Single and Multiple Inheritance10:07
Object-Oriented Programming in Python: Multilevel and Hierarchical Inheritance7:25
Class Inheritance
Object-Oriented Programming in Python: Composition11:46
Class Composition
Object-Oriented Programming in Python: Polymorphism5:10
Understanding Polymorphism
Working with super(), Inheritance, and Polymorphism
Object-Oriented Programming in Python: Encapsulation13:32
Understanding Encapsulation in Python
Object-Oriented Programming in Python: Getters, Setters, and Properties6:33
Object-Oriented Programming in Python: Aggregation6:25
Object-Oriented Programming in Python: Abstraction10:39
Using Getter and Setter Methods
Working with Properties

Numpy Intro2:43
NumPy for Beginners: What It Is and Why It’s Essential3:44
What Is an Array? Different Ways to Create Arrays in NumPy8:12
Create a NumPy Array
NumPy Array: Slicing and Indexing4:20
Array Properties and Advanced Indexing Techniques9:41
Access Array Elements
Array Indexing and Slicing
Extract a subarray from a 2D array using slicing
Essential Techniques for NumPy Array Creation and Transformation4:34
NumPy Array Manipulation View, Copy, Reshape, and Flattening Techniques5:45
Reshape an Array
Flatten a 2D NumPy Array
Math functions in NumPy6:46
Calculate the Mean
Find the Maximum Value
Randomization and Combining Arrays in NumPy7:54
Advanced Array Operations and Splitting Techniques9:03
Loading, Saving, and Searching in NumPy5:17
Array Operations
Array Broadcasting
Unique Values in an Array
Array Concatenation

Pandas Intro1:26
Getting Started with Pandas: Installation, Anaconda Setup, Jupyter Notebook6:46
Pandas Series Explained: Creating, Manipulating, and Comparing with NumPy Arrays5:40
Pandas DataFrames: Access, Modification, Filtering, Indexing5:39
MultiIndex in Pandas: Hierarchical Indexing Explained7:08
Pandas DataFrame Analysis: Grouping, Aggregation, and Math Functions7:32
Real Datasets: Data Downloading, Analysis, and SQL Integration in Pandas8:09
Pivot Tables in Pandas: Data Cleaning and Real-World Data Analysis7:19
Pandas Data Visualization: Charts, Graphs, and Insights8:44
Creating a Pandas DataFrame from a Dictionary
Add a New Column to an Existing DataFrame
Filter Rows Based on a Condition
Sort Data by a Column
Rename Columns
Drop a Column
Use .loc to Access Specific Rows and Columns
Group Data and Calculate Summary Statistics
Count Non-Null Values in a Column
Find the Number of Null Values in Each Column
Remove Rows with Null Values
Fill Null Values with a Default Value
Count the Number of Unique Values in a Column
Check for Duplicates in a Column
Drop Duplicates from a DataFrame

Polars Intro1:34
Polars: Key Differences from Pandas and Why It’s Faster6:07
Explore Polars, a fast data frame library for large datasets, using lazy evaluation, multithreading, memory mapping, and Apache Arrow support with a Rust backend and pandas-like API.
Installing Polars, Loading DataFrames, and Accessing Columns Efficiently7:12
Polars: Arithmetic Operations, Column Management, Data Manipulation8:05
DataFrames in Polars: Slicing, Descriptive Statistics, and Advanced Data Process9:52
Polars DataFrame Methods: Flags, Schema, Column Operations, and Conversions7:38
Advanced Data Manipulation in Polars: Grouping, Aggregation, Sorting5:25
Advanced Data Operations in Polars: write_csv, Pivot Tables, and Join Strategies8:20
Understanding Eager and Lazy Execution in Polars: A Speed Comparison with Pandas16:53
Data Visualization in Polars. Advantages, Limitations, and comparison4:23

Seaborn Intro1:11
What Seaborn Is, How to Install It. How It Compares to Matplotlib7:05
Exploring Seaborn Built-in Datasets: histplot and scatterplot for Data Visualize8:50
Advanced Seaborn: Exploring Boxplot, Catplot, and Extended Parameters6:39
Seaborn Visualizations: Working with Violin Plot, Strip Plot, and Jointplot5:11
Advanced Seaborn: Working with PairGrid Plot and Heatmap from Pivot Table6:20

Git intro1:12
Git Installation & Setup: Global and Local Settings7:27
Git Workflow Explained: Working Area, Staging, Repository & .gitignore7:06
Renaming and Deleting Files in Git: Git Commands6:05
.gitignore: Types of Settings & Managing User Info in Git Config4:48
GitHub Essentials: Cloning Repositories, Pushing & Pulling Changes, SSH Connect5:09
Pushing Local Changes to GitHub, Deleting Repo, and CLI Connection Setup8:11
Installing Git Graph Extension in VS Code & Basic Git Branch Commands5:06
Merging Branches in Git: Step-by-Step Guide6:52
Resolving Merge Conflicts in Git7:12
Advanced Techniques Git Reflog and Reset: Hard vs Soft6:26
Advanced Git Techniques: Amend, Squash, and Deleting Branches Merged & Unmerged6:08
Working with Git Logs: Customizing Log Output and Using Aliases6:51
Working with Git Rebase: What It Is?7:00

Requirements

No prior experience required! This course is beginner-friendly and designed to help you learn Python and data analysis from scratch.
Basic understanding of programming concepts (variables, loops, conditionals) is helpful, but not mandatory.
A computer with an internet connection to access course materials and run code.
A passion for learning and a desire to build hands-on projects.
We will be using Python, Jupyter Notebooks, and VSCode. Don’t worry - we’ll show you how to install Python and set up your development environment step-by-step in the course.

Description

This course is designed to teach you the most in-demand Python libraries and tools used by data professionals, making it ideal for aspiring data scientists, analysts, and developers.

What you’ll learn:

Python Basics to Advanced: Starting from the fundamentals, we'll build a solid foundation in Python, guiding you through key programming concepts and progressing to advanced topics like Object-Oriented Programming (OOP).
NumPy & Pandas: Master the core libraries for fast and efficient data wrangling, manipulation, and analysis. Learn how to work with large datasets, handle missing values, and perform advanced calculations.
Polars: Explore Polars, a fast, scalable DataFrame library. We'll compare it with Pandas to show how Polars offers faster performance for data manipulation, especially with large datasets.
Data Visualization with Seaborn & Matplotlib: Learn how to visualize data like a pro. From creating simple plots to designing beautiful, interactive charts, you’ll understand how to tell compelling stories with data. We’ll cover everything from basic visualizations to advanced techniques like heatmaps, histograms, and scatter plots.
Git: Master version control with Git. Learn how to track and manage changes in your code, collaborate on projects, and keep your work organized.
Building Dashboards with Streamlit: Learn how to create interactive dashboards using Streamlit and Matplotlib. You’ll build a live project from scratch using real-world data and deploy it for free, showcasing your work to others.

As part of this course, you’ll complete numerous hands-on assignments to practice and reinforce your learning. By the end, you’ll have a complete, deployable dashboard project that you can demonstrate to potential employers or clients.

Who this course is for:

Beginners to Python If you're new to programming, this course will teach you Python from scratch, starting with the fundamentals and progressing to more advanced topics like object-oriented programming.
Aspiring Data Analysts If you're interested in starting a career in data analysis, this course will give you the foundational skills in Python, data manipulation, and visualization that are in high demand by employers.
Students and Professionals Looking to Transition to Data If you're looking to switch careers and break into the data field, this course will help you develop the technical skills needed to analyze and visualize real-world data effectively.
Portfolio Builders - Create real projects to showcase in your data portfolio.
Self-Learners - Follow a structured path to master Python and data analysis.

Python Data Analyst Bootcamp: Process, Analyze & Visualize

What you'll learn

Explore related topics

Coding Exercises

Course content

Introduction1 lecture • 4min

Python: Syntax and Data Structures19 lectures • 2hr 37min

Python: Core Structure9 lectures • 1hr 23min

Python: Object-oriented programming10 lectures • 1hr 32min

NumPy: Array Operations, Data Manipulation and Advanced Technique11 lectures • 1hr 8min

Pandas: Master Data Handling in Python library9 lectures • 58min

Polars: Python library for Speed in Data Processing10 lectures • 1hr 15min

Data Visualization with Matplotlib: From Simple Charts to Advanced Techniques6 lectures • 34min

Seaborn Essentials: Python library for Visualization6 lectures • 35min

Git: From Basics to Advanced Techniques for Efficient Version Control14 lectures • 1hr 26min

Requirements

Description

Who this course is for: