AI and Data Scientist Roadmap¶

• Roadmap: https://roadmap.sh/ai-data-scientist

1. Mathematics¶

1.1 Linear Algebra, Calculus, Mathematical Analysis¶

• 1.1.1 Mathematics for Machine Learning (Courses)

1.2 Differential Calculus¶

• 1.2.1 Coursera: Algebra and Differential Calculus (Course)

2. Statistics¶

2.1 Statistics, CLT¶

• 2.1.1 Coursera: Introduction to Statistics (Course)

2.2 Hypothesis Testing¶

• 2.2.1 Coursera: Hypothesis Testing (Course)

2.3 Probability and Sampling¶

• 2.3.1 Coursera: Probability and Statistics (Course)

2.4 AB Testing¶

• 2.4.1 Practitioner's Guide to Statistical Tests (Article)
• 2.4.2 Experiment Design Article (Article)

2.5 Increasing Test Sensitivity¶

• 2.5.1 Minimum Detectable Effect (Article)
• 2.5.2 Paper: Improving Test Sensitivity (Paper)
• 2.5.3 Paper: Improving Sensitivity (CUPED) (Paper)
• 2.5.4 CUPED at Booking.com (Article)
• 2.5.5 Doordash: CUPAC (Article)
• 2.5.6 Netflix: Stratification (Paper)

2.6 Ratio Metrics¶

• 2.6.1 Microsoft: Delta Method in Metric Analytics (Paper)
• 2.6.2 Paper: Ratio Metrics (Paper)

3. Econometrics¶

3.1 Pre-requisites of Econometrics¶

• 3.1.1 Fundamentals of Econometrics (Book)

3.2 Regression, Timeseries, Fitting Distributions¶

• 3.2.1 Intro to Econometrics (Book)
• 3.2.2 Coursera: Econometrics (Course)
• 3.2.3 Kaggle: Learn Time Series (Course)
• 3.2.4 Kaggle: Time Series Basics (Tutorial)
• 3.2.5 ARIMA model for Time Series (Tutorial)
• 3.2.6 Time Series Models (Tutorial)
• 3.2.7 Forecasting Task with Solution (OpenSource)
• 3.2.8 Coursera: Linear Regression (Course)

4. Coding¶

4.1 Learn Python Programming Language¶

• 4.1.1 Learn Python: Kaggle (Course)
• 4.1.2 Google's Python Class (Course)

4.2 Data Structures and Algorithms (Python)¶

• 4.2.1 Algorithmic Exercises (Tutorial + Challenges)
• 4.2.2 Study Plans - Leetcode (Challenges)
• 4.2.3 Algorithms Specialization (Course)

4.3 Learn SQL¶

• 4.3.1 SQL Tutorial (Course)

5. Exploratory Data Analysis¶

5.1 Data understanding, Data Analysis and Visualization¶

• 5.1.1 Exploratory Data Analysis with Python and Pandas (Course)
• 5.1.2 Exploratory Data Analysis for Machine Learning (Course)
• 5.1.3 Exploratory Data Analysis with Seaborn (Course)

6. Machine Learning¶

6.1 Classic ML (Sup., Unsup.), Advanced ML (Ensembles, NNs)¶

• 6.1.1 Open Machine Learning Course - Open Data Science (Course)
• 6.1.2 Machine Learning Specialization (Course)
• 6.1.3 Pattern Recognition & ML by Christopher m. Bishop (eBook)
• 6.1.4 GitHub repository with notes & code from the eBook above

7. Deep Learning¶

7.1 Fully Connected, CNN, RNN, LSTM, Transformers, TL¶

• 7.1.1 Deep Learning Specialization (Courses)
• 7.1.2 Deep Learning Book (eBook)
• 7.1.3 Attention is all you need (Paper)
• 7.1.4 The Illustrated Transformer (Article)

8. MLOps¶

8.1 Deployment Models, CI/CD¶

• 8.1.1 MLOps Specialization (Courses)