Data science bootcamp — From Data to Insights

What you’ll learn

• Understand the data science lifecycle
• Learn Python for data science
• Work with datasets and different data formats
• Clean and prepare messy data
• Perform exploratory data analysis
• Use statistics to understand data
• Create meaningful data visualizations
• Work with NumPy and Pandas
• Query and analyze data with SQL
• Discover patterns and relationships in datasets
• Build and evaluate machine learning models
• Understand supervised and unsupervised learning
• Apply regression and classification techniques
• Work with clustering and dimensionality reduction
• Evaluate model performance
• Communicate insights through data storytelling
• Build a complete data science project

1. 01Module 1 — Enter the Data Science World

   Discover what data science is, how data scientists work, and how data moves from a raw dataset to actionable insights.

2. 02Module 2 — Python for Data Science

   Build the Python foundations needed for data analysis, including variables, data structures, functions, loops, modules, and working with files.

3. 03Module 3 — Working with Data

   Learn how to load, inspect, organize, and manipulate datasets using tools such as NumPy and Pandas.

4. 04Module 4 — SQL & Databases

   Learn how to retrieve and analyze data stored in databases using SQL. Explore queries, filtering, aggregation, joins, and more.

5. 05Module 5 — Data Cleaning & Preparation

   Learn how to deal with missing values, duplicates, inconsistent formats, outliers, and other common problems found in real-world datasets.

6. 06Module 6 — Exploratory Data Analysis

   Learn how to investigate datasets, identify patterns and relationships, ask meaningful questions, and turn raw data into useful observations.

7. 07Module 7 — Data Visualization

   Transform data into clear and compelling visualizations. Learn how to choose the right charts and communicate information effectively.

8. 08Module 8 — Statistics for Data Science

   Build a practical understanding of statistics, including distributions, probability, correlation, sampling, hypothesis testing, and statistical significance.

9. 09Module 9 — Machine Learning Fundamentals

   Discover how machines learn from data and explore the foundations of supervised and unsupervised learning.

10. 10Module 10 — Predictive Modeling

    Build your first machine learning models using techniques such as linear regression, logistic regression, decision trees, and classification.

11. 11Module 11 — Model Evaluation & Improvement

    Learn how to measure model performance, avoid overfitting, split datasets correctly, select useful features, and improve your models.

12. 12Module 12 — Unsupervised Learning

    Explore techniques that allow models to discover hidden structures in data, including clustering and dimensionality reduction.

13. 13Module 13 — Data Storytelling

    Learn how to transform technical analysis into a clear story that can be understood by managers, clients, and non-technical audiences.

14. 14Module 14 — From Notebook to Real Project

    Learn how to structure a complete data science workflow, document your analysis, make your work reproducible, and present your results professionally.

15. 15Module 15 — Capstone: Solve a Real Data Problem

    Work on an end-to-end project using a real-world dataset. Define the problem, collect and clean the data, explore it, build models, evaluate your results, and present your findings.