Learning Tracks

Choose your path to mastery. Each track is designed for specific goals and experience levels, with structured modules, hands-on exercises, and step-by-step problem solving at every difficulty level.

🎯 Quick Track Finder

Your Situation Recommended Track Difficulty Time
Brand new to data science Data Science Foundations Beginner 4-6 weeks
Interview in 3 weeks The 21-Day Sprint Intermediate 21 days
Product/Growth analyst role Product Analytics Mastery Intermediate 6-8 weeks
Preparing for interviews Interview Success Blueprint Intermediate 4-6 weeks
Data engineering career Data Engineering Deep Dive Advanced 8-12 weeks
Senior/Staff level prep Advanced Analytics & ML Prep Advanced 8-10 weeks

Beginner Tracks

4-6 weeks Beginner

🌱 Data Science Foundations

Start your data science journey with core concepts, gentle introductions, and hands-on practice.

Perfect for: New learners, career changers, students starting in data science

📚 Learning Objectives

  • Understand core statistical concepts and when to apply them
  • Write basic SQL queries to extract and analyze data
  • Perform exploratory data analysis with Python
  • Interpret data visualizations and distributions
  • Build confidence with real-world data problems

📋 Module Breakdown

Week 1-2: Statistics Fundamentals — Core statistical concepts with intuitive explanations
🎯 Exercises
  • Conceptual Mean vs Median Decision Tree — Practice choosing the right measure of center
  • Calculation Standard Deviation by Hand — Calculate variance and std dev step-by-step
  • Interpretation Distribution Shape Analysis — Identify skewness and outliers from histograms
📖 Resources
Week 3-4: SQL Basics — Query fundamentals with progressive complexity
🎯 Exercises
  • Query SELECT, WHERE, ORDER BY — Filter and sort data from single tables
  • Query GROUP BY and Aggregations — Calculate counts, sums, averages by category
  • Query Simple JOINs — Combine data from two related tables
📖 Resources
Week 5-6: Python for Analysis — Pandas fundamentals for data manipulation
🎯 Exercises
  • Code DataFrame Basics — Load, inspect, and select data
  • Code Data Cleaning Workflow — Handle missing values and data types
  • Project Mini EDA Project — Complete analysis of a simple dataset
📖 Resources
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Intermediate Tracks

21 days Intermediate

🚀 The 21-Day Sprint

Accelerated interview preparation program with daily structured learning.

Perfect for: Time-constrained professionals preparing for data science interviews

📚 Prerequisites

  • Basic statistics knowledge
  • Familiarity with SQL fundamentals
  • Python/pandas basics

📋 Module Breakdown

Days 1-7: Technical Foundations Refresh — Reinforce core skills with interview-focused practice
🎯 Daily Exercises
  • Timed Daily SQL Challenge — One medium-difficulty SQL problem per day
  • Review Statistics Quick Hits — Rapid-fire concept review (15 min/day)
  • Code Python Pattern Practice — Common interview code snippets
📖 Resources
Days 8-14: Analytical Deep Dive — Case studies, metrics, and structured thinking
🎯 Daily Exercises
  • Case Study Metric Design Exercise — Define success metrics for a product feature
  • Case Study Root Cause Analysis — Investigate a drop in a key metric
  • Presentation Findings Presentation — Practice explaining insights clearly
📖 Resources
Days 15-21: Mock Interviews & Polish — Full interview simulations and refinement
🎯 Daily Exercises
  • Mock Technical Screen Simulation — 45-minute SQL + analytics session
  • Mock Behavioral Interview Practice — STAR stories with feedback
  • Review Weak Spot Remediation — Targeted practice on identified gaps
📖 Resources
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6-8 weeks Intermediate

📊 Product Analytics Mastery

End-to-end product analytics from metrics design to experimentation.

Perfect for: Product managers, product analysts, growth analysts

📚 Prerequisites

  • Basic statistics knowledge
  • SQL fundamentals
  • Understanding of product development lifecycle

📋 Module Breakdown

Module 1: Metrics Framework — Designing metrics that drive decisions
🎯 Exercises
  • Design North Star Metric Workshop — Define a North Star for a sample product
  • Analysis Metric Decomposition — Break down DAU into component drivers
  • Case Study Counter-Metrics Exercise — Identify guardrail metrics for optimization
Module 2: A/B Testing Deep Dive — Rigorous experimentation methodology
🎯 Exercises
  • Calculation Sample Size Calculation — Determine required sample for an experiment
  • Analysis Results Interpretation — Analyze experiment outcomes with nuance
  • Case Study Novelty Effect Analysis — Identify and adjust for novelty effects
📖 Resources
Module 3: Cohort & Retention Analysis — Understanding user lifecycle patterns
🎯 Exercises
  • Query Cohort Retention Query — Build a retention matrix with SQL
  • Visualization Retention Curve Interpretation — Identify inflection points and opportunities
  • Project Full Cohort Analysis Project — End-to-end analysis with recommendations
Module 4: Growth Analytics — Funnel optimization and growth modeling
🎯 Exercises
  • Analysis Funnel Drop-off Analysis — Identify and prioritize conversion gaps
  • Modeling Growth Modeling Exercise — Build a simple growth model
  • Presentation Growth Opportunity Pitch — Present findings to stakeholders
📖 Resources
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4-6 weeks Intermediate

🏆 Interview Success Blueprint

Complete preparation for data science and analytical interviews.

Perfect for: Interview candidates for data science, analytics, and related roles

📚 Prerequisites

  • Completed Data Science Foundations track OR equivalent experience
  • Basic familiarity with business metrics

📋 Module Breakdown

Module 1: Technical Preparation — SQL, Python, and statistics for interviews
🎯 Exercises
  • Timed SQL Under Pressure — Solve problems with time constraints
  • Code Python Interview Patterns — Common pandas operations and algorithms
  • Quiz Statistics Rapid Fire — Quick conceptual questions
📖 Resources
Module 2: Analytical Skills — Case studies and structured problem-solving
🎯 Exercises
  • Case Study Product Sense Cases — Practice with Meta/Google style cases
  • Case Study Metric Investigation — Diagnose metric movements
  • Presentation Insight Communication — Practice clear, concise presentations
📖 Resources
Module 3: Behavioral Mastery — STAR stories and leadership principles
🎯 Exercises
  • Writing Story Bank Development — Document 10+ polished STAR stories
  • Mock Behavioral Mock Interview — Practice with feedback
  • Reflection Weakness Reframing — Prepare authentic weakness answers
📖 Resources
Module 4: Interview Day Execution — Logistics, mindset, and follow-up
🎯 Exercises
  • Checklist Pre-Interview Checklist — Complete preparation walkthrough
  • Simulation Full Interview Day Simulation — Practice entire interview flow
  • Writing Thank You Note Templates — Prepare personalized follow-ups
📖 Resources
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Advanced Tracks

8-12 weeks Advanced

⚙️ Data Engineering Deep Dive

Comprehensive guide to data engineering best practices and architecture.

Perfect for: Data engineers, analytics engineers, and architects

📚 Prerequisites

  • Strong SQL proficiency
  • Experience with data pipelines
  • Understanding of cloud infrastructure basics

📋 Module Breakdown

Module 1: Strategy & Architecture — High-level data platform design
🎯 Exercises
  • Design Architecture Diagram Exercise — Design a data platform for a given use case
  • Analysis Trade-off Analysis — Compare batch vs streaming architectures
  • Case Study Migration Planning — Plan a legacy system migration
📖 Resources
Module 2: Data Modeling — Dimensional modeling and schema design
🎯 Exercises
  • Modeling Star Schema Design — Design a star schema for e-commerce
  • Modeling Slowly Changing Dimensions — Implement SCD Type 2
  • Query Complex Aggregations — Write efficient analytical queries
📖 Resources
Module 3: Data Quality & Governance — Building trust in your data
🎯 Exercises
  • Implementation Data Quality Checks — Implement validation rules
  • Design Data Contract Design — Define producer-consumer contracts
  • Analysis Lineage Documentation — Map data flow through pipelines
Module 4: Pipeline Development — ETL/ELT best practices and orchestration
🎯 Exercises
  • Implementation Incremental Load Pattern — Implement efficient incremental loading
  • Implementation Idempotent Pipeline — Design rerunnable pipelines
  • Project End-to-End Pipeline Project — Build a complete data pipeline
📖 Resources
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8-10 weeks Advanced

🧠 Advanced Analytics & ML Prep

Advanced statistical methods and machine learning foundations for senior roles.

Perfect for: Senior data scientists, ML engineers, researchers

📚 Prerequisites

  • Strong statistics foundation
  • Python proficiency including NumPy and pandas
  • Experience with A/B testing

📋 Module Breakdown

Module 1: Advanced Statistics — Beyond the basics: causal inference and advanced methods
🎯 Exercises
  • Analysis Difference-in-Differences — Apply DiD to a quasi-experiment
  • Analysis Propensity Score Matching — Control for confounders with PSM
  • Calculation Bayesian A/B Testing — Calculate posterior probabilities
📖 Resources
Module 2: Experimentation Edge Cases — Handling complex experimentation scenarios
🎯 Exercises
  • Case Study Network Effects in Experiments — Address interference between units
  • Analysis Multi-Armed Bandits — When to use bandits vs A/B tests
  • Case Study Long-Term Experiment Effects — Measure effects beyond the experiment window
📖 Resources
Module 3: ML for Analytics — Practical ML for analytical applications
🎯 Exercises
  • Implementation Feature Engineering Pipeline — Build robust feature transformations
  • Analysis Model Interpretation — Explain model predictions to stakeholders
  • Project Prediction Model Project — Build and evaluate a prediction model
Module 4: Senior Interview Prep — Staff/Principal level interview preparation
🎯 Exercises
  • Case Study System Design for Analytics — Design an analytics system end-to-end
  • Presentation Executive Communication — Present complex findings simply
  • Mock Technical Leadership Interview — Demonstrate technical leadership
📖 Resources
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📖 How Learning Tracks Work

Understanding Learning Tracks

  • Structured Learning: Follow a curated sequence of content designed for your goals
  • Modules with Exercises: Each track is divided into modules with hands-on exercises at various difficulty levels
  • Progressive Difficulty: Exercises range from conceptual to implementation, building skills incrementally
  • Flexible Pace: Learn at your own speed with clear time estimates
  • Real Resources: Each module links to relevant handbook pages for deep learning

Tips for Success

  • Complete modules in order for the best learning experience
  • Don't skip exercises - they're designed to reinforce learning
  • Take notes and revisit challenging concepts
  • Practice explaining concepts out loud (interview simulation)
  • Use the linked resources for deeper understanding

Choosing Your Path

  • New to data science? → Start with Data Science Foundations
  • Short timeline? → Choose The 21-Day Sprint
  • Product/Growth role? → Pick Product Analytics Mastery
  • Engineering focus? → Go for Data Engineering Deep Dive
  • Targeting senior roles? → Complete Advanced Analytics & ML Prep

🎯 Exercise Type Legend

Type Icon Description
Conceptual 📚 Understanding and explaining concepts
Calculation 🔢 Step-by-step mathematical work
Query 💻 Writing SQL queries
Code 🐍 Python/programming exercises
Analysis 📊 Data analysis and interpretation
Case Study 📋 Realistic business scenarios
Design 🎨 Creating frameworks and architectures
Project 🏗️ End-to-end implementation
Mock 🎤 Interview simulation
Writing ✍️ Documentation and communication
Timed ⏱️ Practice under time pressure
10 mins Beginner