Data Science

The full pipeline from raw data to decisions: collection, cleaning, exploration, analysis and communication.

intermediate~45 hours14 topics~31h of lessonsRoadmap Level 5

Wrangling and reading data well is the common core of analytics, ML and research.

RECOMMENDED BEFORE THIS
○ Python Programming○ Statistics○ Probability

These are suggested first. You can still open any topic — this only helps you pick an order.

Prerequisites:Python ProgrammingStatisticsProbability
Learning Path
07 stages
14 topics · 7 stages0/14 completed
01

Fundamentals

Start from zero. Build the vocabulary.

The Data Science Pipeline~45 minbeginnerPandas and DataFrames~120 minbeginnerNumPy for Numerical Data~75 minbeginner
02

Core Concepts

The ideas you cannot skip.

Data Cleaning~90 minintermediateExploratory Data Analysis~90 minintermediateData Visualization with Matplotlib/Seaborn~90 minintermediate
03

Intermediate

Connect ideas and go deeper.

Feature Engineering~90 minintermediateIntro to Modeling~90 minintermediatescikit-learn Workflow~120 minintermediate
04

Advanced

Beyond the basics.

Interpretability and Ethics~75 minadvancedTime Series and Specialized Data~90 minadvanced
05

Practice

Solve problems until it sticks.

Practice: Full Analyses~300 minintermediate
06

Projects

Apply everything in real work.

Project: End-to-End Data Analysis~480 minadvanced
07

Interview Prep

Convert knowledge into offers.

Data Science Interview Questions~90 minadvanced