Data Science

Transform data into decisions — master the skills to analyze, visualize, and predict using data

Created by Andrei Neagoie, Daniel Bourke

Course Overview

In today’s information-driven world, data is one of the most valuable resources — but only if you know how to use it. The Data Science course is designed to teach you how to collect, process, analyze, and extract valuable insights from data. You’ll learn the core tools of modern data science, from Python programming and data visualization to statistical analysis and machine learning basics.

Whether you're preparing for a career in tech, business, or research, this course gives you a strong foundation in one of the most in-demand skills in the global job market.

What You Will Learn

This course combines theory with practical application across key data science concepts:

1. Introduction to Data Science
2. Understand what data science is and how it's used in business, healthcare, marketing, and more.
3. Python for Data Analysis
4. Learn Python programming with a focus on data manipulation using libraries like Pandas and NumPy.
5. Data Cleaning & Preprocessing
6. Handle missing values, remove duplicates, and prepare data for analysis.
7. Data Visualization
8. Create clear and insightful visualizations using Matplotlib and Seaborn.
9. Statistics & Probability
10. Learn the math behind data science — distributions, regression, sampling, and hypothesis testing.
11. Exploratory Data Analysis (EDA)
12. Discover patterns, trends, and relationships in data.
13. Introduction to Machine Learning
14. Get started with algorithms like Linear Regression, Decision Trees, and Clustering.
15. Project-Based Learning
16. Apply your skills on real datasets to solve practical problems.

Who This Course is For

This course is perfect for:

1. Beginners with a strong interest in analytics or tech
2. Business professionals wanting to leverage data for better decisions
3. Aspiring data scientists, analysts, or AI specialists
4. Researchers or students working with large datasets
5. Career switchers moving into tech or analytics

Hands-On Learning Experience

You’ll build knowledge through structured tasks and real-life examples:

1. Python Coding Challenges: Practice syntax, functions, and data manipulation
2. Mini Data Projects: Analyze public datasets (e.g., sales, social media, health)
3. Data Cleaning Exercises: Prepare raw data for analysis
4. Visual Dashboard Creation: Design charts and graphs to tell a data story
5. Basic Model Building: Predict outcomes and evaluate model performance

Career Outcomes

After completing this course, you will be able to:

1. Analyze large datasets and derive insights
2. Use Python and data science libraries confidently
3. Build visualizations and reports for business intelligence
4. Begin working on machine learning projects
5. Pursue roles like Data Analyst, Junior Data Scientist, Business Intelligence Analyst

Tools & Concepts Covered

You’ll gain hands-on experience with:

1. Programming Tools: Python, Jupyter Notebooks
2. Libraries: Pandas, NumPy, Matplotlib, Seaborn, Scikit-learn
3. Concepts: Descriptive stats, data wrangling, modeling, classification
4. Project Platforms: Kaggle datasets, Google Colab (optional)
5. 
   

Discover the Power Behind the Numbers

Enroll in Data Science and gain the skills to turn raw information into actionable insights.