01Suggested
Arithmetic
Whole numbers, operations, fractions, decimals, percentages, ratios, powers, and estimation.
Browse subjects
130 subjects · 371 topics
02
Algebra
Expressions, equations, functions, graphs, and patterns.
03
Calculus
Limits, derivatives, integrals, rates of change, and accumulation.
04
Linear Algebra
Vectors, matrices, linear systems, and transformations.
05
Probability and Statistics
Uncertainty, data summaries, inference, and statistical models.
06
Mathematics for Machine Learning and NLP
Data representations, losses, optimization, and language-model probabilities.
07
Discrete Math & Algorithms
Math for Data Science · Study order 4. Count possibilities and reason about efficiency. Big-O of common operations; basic counting; what entropy measures.
08Suggested
Linear Algebra
Math for Data Science · Study order 2. Data is tables of numbers (matrices); models transform them. Why PCA works (eigenvectors of the covariance matrix); similarity as a dot product / cosine; what rank means.
09
Calculus & Optimization
Math for Data Science · Study order 3. Models learn by reducing error; the gradient tells which direction. Explain gradient descent in plain words; what the learning rate does; why the chain rule matters for neural networks.
10
Statistics & Probability
Math for Data Science · Study order 1. Measure uncertainty and decide whether a pattern is real or luck. What a p-value is and is not; Bayes by hand; CLT in one sentence; overfitting vs underfitting.
11
Statistics
What a number is allowed to claim: samples, averages, spread, and charts that mislead.
12
Geometry
Shapes, measurement, congruence, similarity, and coordinates on a plane.
13
Trigonometry
Angles, right triangles, sine and cosine, and periodic change.
14
Discrete mathematics
Counting, logic, and finite structures used to describe programs and networks.
15Suggested
Python Scripting
Scripts for automation: files, APIs, devices
16
Data Structures & Algorithms
Coding interview fundamentals
17
SQL & Data
Queries, joins, and database design
18Suggested
Linux & Shell
Command line, permissions, services, cron
19
Networking
IP, switching, routing, protocols, troubleshooting
20
HTML, CSS & JavaScript
Core front-end skills and browser basics
21
System Design
Scaling, caching, databases, trade-offs
22
Cloud Basics
Core services, IAM, scaling
23
Cybersecurity
Fundamentals, threats and response
24
QA & Test Automation
Testing, automation and bug reports
25
Troubleshooting
Fault-finding drills across industrial, network and Linux
26
AI fundamentals
How these systems actually work: data, models, prediction, and the usual limits.
27
AI
What artificial intelligence is, what it can do, and what it still is not.
28
Machine learning
How a system learns from examples — and how that learning goes wrong.
29
Language models
How large language models produce text — and how to use that without mistaking it for a mind.
30
Embedded Systems
Microcontrollers, interfaces and firmware
31
Working with data
Tables, summaries, and preparing numbers so a later claim can be trusted.
32
Operating systems
Processes, memory, files, and how software shares one machine.