What You Will Learn!
- Foundations of Programming , Setting Up the Environment , Python Basics , Functions and Modules , Data Structures in Python , Introduction to NumPy and Pandas
- Data Visualization Basics , Advanced Data Visualization with Seaborn , Data Cleaning and Preprocessing , Data Analysis with Pandas , Capstone Project
- Capstone Project: Applying Python programming and data analysis skills to a real-world project. Presenting findings and insights derived from the analyzed data.
- Practical Application: The capstone project provides an opportunity for students to apply their skills to a real-world data analysis problem
Description
In the "Python for Data Analysis" course, students will acquire a comprehensive set of skills and knowledge that empowers them to effectively use Python for analyzing and manipulating data. The course covers a wide range of topics to ensure a well-rounded understanding, from foundational programming concepts to advanced data analysis techniques. Here's an overview of what students will learn:
Module 1: Introduction to Python Programming
Foundations of Programming:
Understanding basic programming concepts.
Exploring the Python programming language.
Setting Up the Environment:
Installing and configuring Python and an integrated development environment (IDE).
Python Basics:
Mastering Python syntax, variables, and data types.
Implementing control flow through if statements and loops.
Functions and Modules:
Writing functions for code modularity.
Exploring Python modules and libraries.
Module 2: Data Handling in Python
Data Structures in Python:
Working with lists, dictionaries, and tuples.
Understanding their use in data handling.
Introduction to NumPy and Pandas:
Using NumPy arrays for numerical operations.
Exploring Pandas for data manipulation with DataFrames.
Module 3: Data Visualization with Matplotlib and Seaborn
Data Visualization Basics:
Understanding the importance of visualizing data.
Creating basic plots using Matplotlib.
Advanced Data Visualization with Seaborn:
Enhancing visualizations with Seaborn.
Creating informative and aesthetic charts and graphs.
Module 4: Introduction to Data Analysis with Pandas and NumPy
Data Cleaning and Preprocessing:
Handling missing data and outliers.
Transforming and cleaning data for analysis.
Data Analysis with Pandas:
Performing basic data analysis tasks using Pandas DataFrames.
Aggregating, grouping, and filtering data.
Module 5: Capstone Project: Data Analysis with Python
Capstone Project:
Applying Python programming and data analysis skills to a real-world project.
Presenting findings and insights derived from the analyzed data.
Who Should Attend!
- This Course for any one want to learn data analysis field there is no need to any background for this course ,, in this course you will learn in details many things