Learning Paths / Data Scientist Track
Data Scientist Track
A career track for data scientists: Python, SQL and data wrangling, databases, core DSA, and machine learning foundations.
Start learning →Python
- 1
Functions
Define reusable blocks of code with parameters, returns, and defaults
- 2
Data Types
Core built-in types: numbers, strings, booleans, and type checking
- 3
List Comprehension
Concise syntax for building lists from iterables with optional filtering
- 4
File I/O
Read and write text and binary files with open, paths, and JSON
- 5
Type Hints
Annotate code with static types for tooling, clarity, and safer refactors
Data Wrangling with SQL
- 1
SELECT
Query rows and columns from tables with filtering and sorting
- 2
WHERE Clause
Filter rows with conditions, operators, and NULL handling
- 3
Joins
Combine rows from related tables with INNER, LEFT, and other joins
- 4
GROUP BY
Aggregate rows with COUNT, SUM, AVG, and HAVING filters
- 5
Window Functions
Compute running totals, rankings, and comparisons across row windows
- 6
Common Table Expressions
Name intermediate result sets with WITH for readable, reusable queries
Databases
Foundations of DSA
Machine Learning
- 1
Machine Learning Basics
Supervised and unsupervised learning, training, and inference for developers
- 2
Neural Networks
Layers, weights, forward passes, and activation functions explained
- 3
AI Fundamentals
Core concepts, types of AI, and essential terminology for developers
- 4
Embeddings
Vector representations, similarity search, and practical use cases
- 5
Evaluation
Building eval sets, metrics, and LLM-as-judge for reliable systems