Data Science — Home

Learn Data Science

Master data science from fundamentals to advanced machine learning.

Course contents

1. Introduction - What is Data Science
2. Get Started - Setting up environment
3. Statistics - Statistical foundations
4. Probability - Probability theory
5. Hypothesis Testing - Statistical tests
6. Regression - Predictive modeling
7. Classification - Categorizing data
8. Clustering - Grouping data
9. Dimensionality Reduction - Feature reduction
10. Feature Engineering - Data preparation
11. Data Visualization - Charts and graphs
12. EDA - Exploratory Data Analysis
13. Data Cleaning - Data preprocessing
14. Time Series - Temporal data
15. NLP - Natural Language Processing
16. Deep Learning - Neural networks
17. Computer Vision - Image analysis
18. Recommender Systems - Recommendations
19. A/B Testing - Experimentation
20. Big Data - Large-scale data
21. Spark - Distributed computing
22. SQL - Data queries
23. Python - Programming
24. R - Statistical computing
25. Tableau - Visualization tool
26. Power BI - Business intelligence
27. MLOps - ML operations
28. Model Deployment - Production ML
29. AutoML - Automated ML
30. XAI - Explainable AI
31. Ethics - Data ethics
32. Bias - Algorithmic bias
33. Privacy - Data privacy
34. Governance - Data governance
35. Data Lakes - Storage architecture
36. Data Warehouses - Analytics storage
37. ETL - Data pipelines
38. Data Quality - Data validation
39. Data Modeling - Data design
40. DAGs - Workflow orchestration
41. Airflow - Pipeline management
42. dbt - Data transformation
43. Data Culture - Organization
44. Interview Prep - Job preparation
45. Best Practices - Tips and patterns

Who is this course for?

• Aspiring data scientists
• Analysts
• Engineers
• Researchers

Prerequisites

• Basic programming
• Statistics fundamentals
• Linear algebra basics

Resources

• Kaggle
• DataCamp
• Coursera