PracHub Learning Academy
Your next round, mapped.
Start with the skill you need now. Each path moves from a clear mental model to worked decisions, interview drills, and realistic failure cases.
12 focused tracks 794 guided lessons progress saved as you study
Start learning nowSoftware Engineering
Build coding fluency, then move from reliable services to senior architecture trade-offs.
Practical Python: Foundations, Patterns, and Problem Solving
Learn Python by tracing programs, choosing data structures, and solving exercises in an editable console. Build from values and control flow to functions, collections, classes, packages, and two checkpoints that combine these ideas.
Foundations of System Design
Develop a system design from requirements and runtime fundamentals through networks, coordination, storage, security, and scaling. Worked examples and practice questions connect each design choice to its assumptions, costs, and failure behavior.
System Design Interview Casebook
Learn system design through detailed building blocks, worked interview cases, and a separate crash review path. Practice capacity estimates, data correctness, failure recovery, and clear explanations of design trade-offs.
Machine Learning & AI
Move from model judgment to production ML, foundation models, GenAI platforms, and agents.
Designing Generative AI Systems: Architecture, Scale, and Deployment
Design generative AI services around explicit requirements, authorized retrieval, capacity estimates, evaluation, safety checks, and reversible releases. Text, image, speech, video, and captioning cases show how the tradeoffs change by workload.
Designing Reliable AI Agents: Architecture, Control, and Evaluation
Design agents that use narrow tools, preserve durable state, stop on clear conditions, and act only within granted authority. The course covers retries, approvals, sandboxing, evaluation, incident stops, and the cases where a fixed workflow is the better design.
How Large Language Models Work: Foundations, Training, and Applications
Follow text through a language model, then study how training, retrieval, tools, and evaluation shape an application. Numerical examples and text, image, audio, and video cases connect model mechanics with system design decisions.
Machine Learning Interview Playbook: Practical Systems and Case Studies
Practice the decisions that come up in machine learning interviews: framing a task, choosing a baseline, evaluating models, debugging failures, and explaining project work with evidence.
Machine Learning System Design Interview: Frameworks and Casebook
Design machine learning systems that connect product decisions to labels, features, training, serving, experiments, monitoring, and recovery, then apply the method to realistic interview cases.
Data Science
Practice product decisions, experiments, and complete take-home analyses.
Product Data Science
Practice product data science through metrics, experiments, model interpretation, and SQL. Worked examples show how to check assumptions, explain uncertainty, and connect an analysis to a product decision.
Data Science Projects
Work through data science projects with examples of data cleaning, exploration, modeling, and written recommendations. Use the solutions to compare assumptions and reasoning, then adapt the approach to the data and evaluation criteria in your own assignment.
A/B Testing for Data Science Interviews: From Framing to Final Decision
This course is designed to help Data Scientists systematically master A/B testing interview questions, from high-level product reasoning to rigorous statistical analysis. A/B testing questions are among the most common and most discriminating interview questions for product, growth, marketplace, and ML-adjacent Data Science roles. Interviewers are not only evaluating statistical knowledge, but also: Whether you can frame ambiguous problems Whether you understand causality and experimental design Whether you can reason about bias, dilution, power, and trade-offs Whether you can translate results into clear product decisions This course teaches a single, reusable mental framework that allows you to answer any A/B testing question with structure, confidence, and depth. What This Course Emphasizes Interview-ready answer structures Both math/statistics and product reasoning Real-world experimentation pitfalls interviewers care about Common follow-up questions and how to handle them Concrete examples from real interview questions Clear verbal phrasing you can use in live interviews
Interview Skills
Turn real experience into specific, credible answers without sounding rehearsed.
