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60-day challenge

Data Science

Turn raw data into real insight, one task a day for 60 days.

Start this challengeSee the 60 days
  • Python
  • Pandas
  • SQL
  • Statistics
  • ML
Cover art for the Data Science challenge

How it works

Start any day. Go at your own pace.

  1. 01

    A task a day

    Each day opens once the one before is submitted: a scoped task that takes under an hour.

  2. 02

    Build in public

    Push the work to your GitHub repository and post about it on LinkedIn. Both links are your submission.

  3. 03

    A quiz each week

    A short multiple-choice quiz at the end of the week, marked straight away with the reasons.

  4. 04

    Badges to share

    First day, seven days, halfway and the finish, each a badge you can post.

By the end, you will

  • Clean, explore and visualise real datasets
  • Build and validate predictive models
  • Communicate findings with clear stories

The 60 days

Every day, planned.

Week 1 · Days 1–7Python Foundations + Data Science Basics
  1. Day 1Python Fundamentals Bootcamp (Core Syntax)
  2. Day 2Python Control Flow & Functions
  3. Day 3Python Data Structures & File Handling
  4. Day 4Project Setup + Dataset Selection
  5. Day 5Data Loading & Understanding
  6. Day 6Data Cleaning & Preprocessing
  7. Day 7EDA + Sprint 1 Review
Week 2 · Days 8–14Week 2
  1. Day 8Finding Hidden Patterns Through EDA
  2. Day 9Cleaning Messy Real-World Data
  3. Day 10Transforming Raw Data into Better Signals
  4. Day 11Building Your First ML Pipeline
  5. Day 12Teaching Machines to Predict Numbers
  6. Day 13Preventing Models from Memorizing Data
  7. Day 14Adapting Models to Changing Constraints
Week 3 · Days 15–21Week 3
  1. Day 15Predicting Customer Churn with Logistic Regression
  2. Day 16Movie Recommendation Using K-Nearest Neighbors
  3. Day 17Loan Approval Prediction with Decision Trees
  4. Day 18Fraud Detection Using Random Forest
  5. Day 19Boosting Model Performance with XGBoost
  6. Day 20Why Accuracy Alone Can Mislead You
  7. Day 21Choosing the Best Model Like a Real Data Scientist
Week 4 · Days 22–28Week 4
  1. Day 22Turning Raw Categories into Machine Learning Signals
  2. Day 23Finding the Most Important Signals in Data
  3. Day 24Compressing Complex Data with PCA
  4. Day 25Building Reliable Models with Cross-Validation
  5. Day 26Optimizing ML Systems with Hyperparameter Tuning
  6. Day 27Understanding Bias vs Variance in ML Systems
  7. Day 28Building Your Most Optimized ML System Yet
Week 5 · Days 29–35Customer Segmentation, Recommendations & Dashboards
  1. Day 29Understanding Customer Types with K-Means Clustering
  2. Day 30Finding the Ideal Number of Customer Segments
  3. Day 31Building Customer Personas from Behavioral Data
  4. Day 32Building a Personalized Product Recommendation Engine
  5. Day 33Detecting Unusual Customer Behavior
  6. Day 34Designing Executive-Level Customer Dashboards
  7. Day 35Combining Customer Intelligence Modules into One Analytics System
Week 6 · Days 36–42Forecasting, KPI Analytics & Predictive Business Intelligence
  1. Day 36Forecasting Customer Growth Trends
  2. Day 37Predicting Future Revenue with Time Series Models
  3. Day 38Analyzing Customer Retention and Lifetime Value
  4. Day 39Designing a KPI Monitoring System for Executives
  5. Day 40Evaluating Business Decisions with A/B Testing
  6. Day 41Predicting High-Risk Customers Before They Churn
  7. Day 42Building a Unified Customer Intelligence Decision System
Week 7 · Days 43–49Deployment, Monitoring & Capstone Engineering
  1. Day 43Turning Your Customer Intelligence Model into an API
  2. Day 44Building an Interactive Customer Intelligence Dashboard
  3. Day 45Deploying Your Customer Intelligence Platform to the Cloud
  4. Day 46Monitoring Customer Intelligence Systems in Production
  5. Day 47Defining Your Customer Intelligence Capstone System
  6. Day 48Building the Data Pipeline for Your Customer Intelligence Platform
  7. Day 49Building the First Working Version of Your Customer Intelligence Platform
Week 8 · Days 50–56Optimization, Explainability & Production Analytics
  1. Day 50Improving Your Customer Intelligence System with Model Optimization
  2. Day 51Explaining Customer Predictions with Explainable AI
  3. Day 52Designing a Professional User Interface for Your Analytics Platform
  4. Day 53Adding Real-Time Customer Prediction Workflows
  5. Day 54Turning Analytics into Executive Business Insights
  6. Day 55Improving the Scalability and Reliability of Your Analytics Platform
  7. Day 56Presenting the Mid-Stage Version of Your Customer Intelligence Platform
Week 9 · Days 57–60Documentation, Presentation & Production Launch
  1. Day 57Documenting Your Customer Intelligence Platform Like a Professional Engineer
  2. Day 58Creating a Professional Demo Presentation for Your Analytics Platform
  3. Day 59Polishing and Finalizing Your Customer Intelligence Platform
  4. Day 60Launching Your Customer Intelligence Platform to the World
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