Course Briefing
Introduction to Data Science
Master data pipelines, statistical analysis, and machine learning models. This track bridges the gap between raw data and actionable intelligence.
This crash course is designed for rapid architectural understanding. You will not find endless video tutorials here. Instead, you will read the core theory, analyze real-world engineering patterns, and immediately execute your knowledge in the terminal.
CLEARANCE REQUIRED: PYTHON CORE
Data Science Module 2 jumps directly into NumPy and Pandas code. Without the foundation below, you will struggle with vectorization, DataFrame operations, and data visualization concepts that are assumed knowledge.
Required Database
| Skill | Modules | Topics |
|---|---|---|
| Python Fundamentals | CORE_1-8 | Variables, data types, loops, functions, lists, dictionaries |
| NumPy Fundamentals | CORE_31 | Arrays, vectorization, broadcasting, operations |
| Pandas Fundamentals | CORE_32 | Series, DataFrames, filtering, groupby, merging |
| Data Visualization | CORE_33 | Matplotlib and Seaborn basics |
Do not bypass this requirement. The Data Science labs assume fluency in Python.
If you've completed these Python modules, you are cleared to proceed.