Study Notes Index (knowledge base structure)#

:construction: ONGOING CONSTRUCTION :construction:

1 Statistics#

1.1 General topics#

1.2 Survival analysis#

1.3 Bayesian Statistics#

1.4 Missing data and imputation methods#

1.5 Causal inference (Propensity score methods)#

1.5.1 Use of external control data in RCT#

1.6 Meta-analysis methods#

1.7 Regression methods#

1.9 Correlated/longitudinal data analysis#

1.10 Ordinal data#

1.xx Other topics#

2 Data Science & Machine Learning#

2.1 Data preprocessing pipeline#

2.1.1 Data manipulation#

  • Numpy

  • Pandas

  • SQL

  • Spreadsheets

2.2 ML methods#

  • Fundamental ML concepts

    • Data importation

    • Data manipulation (above)

    • Frame ML problem

    • EDA (including data visualization)

    • Implement ML models (below)

    • Optimize ML model

      • Feature selection/engineering

      • Improve model generalization (hyperparameter tuning, model validation, etc.)

    • ML model interpretation

  • ML General

  • Stochastic gradient descent

2.2.1 Surpervised learning#

2.2.2 Unsupervised learning#

2.2.3 Deep learning#

2.2.4 Reinforcement learning#

2.2.5 NLP#

2.2.6 Recommendation systems#

2.3 Data visualization#

2.4 Analytical tools#

2.4.1 R#

2.4.2 Python#

2.4.3 Use notebook#

2.4.4 Cloud service#

2.5 Other topics#

2.5.1 ML real-world application#

2.5.2 Bussiness sense#

2.5.3 Data ethics#

3 Math#

3.1 Essence of Linear Algebra#

3.2 Essence of Calculus#

4 Computer Science & Programming#

4.1 Python#

4.2 SQL#

4.3 Algorithm & Data Structure#

4.4 Git & Github#

4.5 Use Linux#

4.6 Rust#

4.7 Containers#

4.8 Web Development#

4.9 D3#

4.x Learning resources#

5 Soft skills#

5.1 Communication & collaboration#

5.2 Interview skills#

5.3 Learning method/philosophy#