Python for Data Analysis
Learn Python for data analysis through hands-on, real-world projects. Master data cleaning, visualization, statistics, and insights using industry-standard libraries.
Data is one of the world's most valuable assets, and organizations rely on skilled data analysts to transform raw data into meaningful insights. This course is designed to help you build practical Python data analysis skills through hands-on, real-world projects.
Whether you're a beginner, a student, a business professional, or an aspiring data analyst, you'll learn how to work with real datasets and solve business problems using Python.
Throughout the course, you'll gain practical experience with the most popular Python libraries used by data professionals, including:
Python Fundamentals for Data Analysis
NumPy for numerical computing
Pandas for data manipulation and analysis
Matplotlib for data visualization
Seaborn for statistical graphics
You'll learn how to:
Import and organize datasets
Clean messy and missing data
Filter, sort, and transform data
Perform exploratory data analysis (EDA)
Create professional charts and dashboards
Calculate descriptive statistics
Identify trends, patterns, and outliers
Generate business insights from data
Build complete end-to-end data analysis projects
By the end of this course, you'll have completed multiple real-world projects that demonstrate your ability to analyze, visualize, and communicate data effectively. These projects can also be included in your professional portfolio to showcase your skills to employers.
This course emphasizes practical learning over theory, making it ideal for anyone who wants to become job-ready in data analysis using Python.
Who this course is for:
Beginners with little or no Python experience
Students interested in data science or analytics
Business professionals who work with data
Excel users transitioning to Python
Aspiring Data Analysts and Business Analysts
Anyone interested in learning data analysis through practical projects
Whether you're a beginner, a student, a business professional, or an aspiring data analyst, you'll learn how to work with real datasets and solve business problems using Python.
Throughout the course, you'll gain practical experience with the most popular Python libraries used by data professionals, including:
Python Fundamentals for Data Analysis
NumPy for numerical computing
Pandas for data manipulation and analysis
Matplotlib for data visualization
Seaborn for statistical graphics
You'll learn how to:
Import and organize datasets
Clean messy and missing data
Filter, sort, and transform data
Perform exploratory data analysis (EDA)
Create professional charts and dashboards
Calculate descriptive statistics
Identify trends, patterns, and outliers
Generate business insights from data
Build complete end-to-end data analysis projects
By the end of this course, you'll have completed multiple real-world projects that demonstrate your ability to analyze, visualize, and communicate data effectively. These projects can also be included in your professional portfolio to showcase your skills to employers.
This course emphasizes practical learning over theory, making it ideal for anyone who wants to become job-ready in data analysis using Python.
Who this course is for:
Beginners with little or no Python experience
Students interested in data science or analytics
Business professionals who work with data
Excel users transitioning to Python
Aspiring Data Analysts and Business Analysts
Anyone interested in learning data analysis through practical projects
What You'll Learn
Learners will be able to set up and verify a Python data-analysis environment (local JupyterLab or Colab) and keep notebooks reproducible with Restart and Run All.
Learners will be able to write core Python using variables, types, collections, conditions, loops and functions, and debug common errors by reading tracebacks.
Learners will be able to use NumPy arrays and pandas DataFrames to load, inspect, select, filter, sort and group data from CSV, Excel and JSON files.
Learners will be able to detect and handle missing, invalid and duplicate values, and report how much data each result is based on.
Learners will be able to calculate descriptive statistics and create Matplotlib and Seaborn charts to explore patterns and outliers.
Learners will be able to explain the limitations of their results and describe what the data can and cannot support.
Prerequisites
No prior programming experience is required.
Basic computer skills are sufficient.
A Windows, macOS, or Linux computer.
Willingness to practice by completing hands-on exercises and projects.
Internet connection for downloading datasets and Python packages.
Course Content
6 lessonsWeek 1. Introduction to Python and the Data-Analysis Environment
Start your data analysis journey by learning the fundamentals of Python and setting up a professional data analysis environment. You'll install the essential tools, write your first Python programs, and gain a solid understanding of the core concepts needed for the rest of the course. By the end of the week, you'll have a fully configured workspace and be ready to begin analyzing real-world data.Session 1: Introduction, Installation, and Environment Setup
60 minSession 2: Python Fundamentals for Data Analysis
120 minWeek 2: Python Data Structures and Programming Logic
Strengthen your Python programming skills by mastering data structures and programming logic. You'll learn how to organize data efficiently, automate repetitive tasks, and write reusable, error-resistant code for data analysis.Session 3: Collections and Data Structures
180 minSession 4: Conditions, Loops, Functions, and Error Handling
180 minWeek 3: NumPy and pandas Fundamentals
Learn the core libraries that power Python data analysis. This week introduces NumPy for efficient numerical computing and pandas for working with structured data, giving you the essential tools to analyze real-world datasets.Session 5: Numerical Analysis with NumPy
180 minSession 6: Introduction to pandas and DataFrames
180 minWeek 6
Added from an AI course reviewWeek 7
Added from an AI course reviewWeek 8
Added from an AI course reviewWeek 9
Added from an AI course review
Tochukwu Udeh
Lead Instructor
About the Instructor
Improving lives, unlocking potentials and fulfilling destinies
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