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Data Science Demystified
Data_Science_Demystified
1. Mathematics Fundamentals
1.1. Linear Algebra Fundamentals
1.1.1. MindMap
1.1.2. Linear Algebra Transformations
1.1.3. Eigen Values and Vectors
1.1.4. Decomposition Techniques
1.1.5. Advanced Topics
1.1.6. Applications
1.2. Calculus
1.2.1. Calculus Fundamentals
1.2.2. Functions and Derivatives
1.2.3. Maxima and Minima
1.2.4. Function Chaining
1.2.5. Applications in Data Science
1.3. Optimization
2. ML Problem Approach
2.1. Hypothesis Generation
2.1.1. Hypothesis Introduction
2.1.2. Characteristics of a Good Hypothesis
2.1.3. Significance of Hypothesis Generation
2.1.4. Hypothesis Generation Frameworks
2.1.4.1. Issue Tree
2.1.4.2. Mind Map
2.1.4.3. Fishbone Diagram
2.1.4.4. Pyramid Principle
2.2. Data Understanding
2.2.1. Exploratory Analysis
2.2.1.1. Comparitive Analysis
2.2.1.2. Distribution Analysis
2.2.1.3. Correlation Analysis
2.2.1.4. Trend Analysis
2.2.1.5. Variable Importance
2.2.1.6. Principal Component Analysis
2.2.2. Domain Driven Analysis
2.3. Feature Engineering
2.3.1. Feature Encoding
2.3.2. Feature Transformation
2.3.3. Feature Scaling
2.3.4. Feature Discretization
2.4. Feature Selection
2.4.1. Filter Approach
2.4.2. Wrapper Approach
2.4.3. Embedded Approach
2.4.4. Hybrid Approach
2.5. Handling Missing Data
2.5.1. Missing Data Pattern Analysis
2.5.2. Missing Data Deletion
2.5.3. Missing Data Imputation
2.5.3.1. Simple Imputation
2.5.3.2. Multiple Imputation
2.5.3.3. Model Based Imputation
2.6. Handling Outliers
2.6.1. Detecting Outliers
2.6.2. Outlier Treatment
2.7. Hyper Parameter Optimization
2.7.1. Grid Search
2.7.2. Random Search
2.7.3. Bayesian Search
2.8. Model Explainability
2.8.1. Local Explainability
2.8.2. Global Explainability
2.9. Model Validation
2.9.1. Cross Validation
2.9.2. Evaluation Metrics
2.9.3. Learning Curves
2.9.4. Model Validation
3. Statistics
3.1. Probability
3.1.1. Key Ideas
3.1.2. Probability Axioms
3.1.3. Types of Probability
3.1.4. Frequentist vs Bayesian Approach
3.2. Sampling
3.2.1. Types of Sampling
3.2.2. Sampling
3.3. Descriptive Statistics
3.3.1. Concepts
3.3.2. Types of Variables
3.3.3. Data Distribution
3.3.4. Measures of Central Tendency
3.4. Inferential Statistics
3.4.1. Core Ideas
3.4.2. Hypothesis Testing
3.4.3. Confidence Interval
3.4.4. Central Limit Theorem
3.4.5. Correlation vs Causation
3.5. Hypothesis Testing
3.5.1. Core Ideas
3.5.2. Parametric Tests
3.5.3. Non Parametric Tests
4. Regression
4.1. Core Ideas
4.2. Model Metrics
4.3. Model Selection
4.4. Widely Used Techniques
4.4.1. Linear Models
4.4.2. Non Linear Models
5. Classification Technique
5.1. Classification Concept
5.2. Classification Metrics
5.3. Multi Class Classification
5.4. Multi Label Classification
5.5. Classification Model Selection
5.6. Widely Used Methods
5.6.1. Logistic Regression
5.6.2. Naive Bayes
5.6.3. SVM (Support Vector Machine)
5.6.4. KNN (K-Nearest Neighbors Algorithm)
5.7. DECISION TREE
5.7.1. ID3
5.7.2. CART
5.7.3. CHAID
5.8. Ensemble
5.8.1. Bagging (Bootstraped Aggregation)
5.8.2. Boosting
6. Recommendation System
6.1. Collaborative Based Filtering
6.2. Content Based Filtering
6.3. Evalation of Recommender Algorithms
6.4. Singular Value Decomposition
7. Unsupervised Learning
7.1. Concepts
7.2. Gaussian-Mix Model (EM)
7.3. PCA (Principle Component Analysis)
7.4. Self Organizing Maps (SOM)
7.5. UL Evaluation Metrics
7.6. Clustering
7.6.1. Fuzzy Clustering
7.6.2. Hierarchical Clusterr
7.6.3. K-Means
7.6.4. K-Medians
8. Time Series Analysis
8.1. Time Series Concepts
8.2. AutoCorrelation And Partial AutoCorrelation
8.3. Classical Time Series Models
8.3.1. Univariate Time Series
8.3.1.1. ARIMA
8.3.1.2. SARIMA
8.3.1.3. ETS
8.3.1.4. Prophet
8.3.1.5. UCM
8.3.1.6. Croston
8.3.2. Multivariate Time Series
8.3.2.1. ARIMAX
8.3.2.2. VAR
8.3.2.3. VARMA & VARMAX
8.3.2.4. VECM
8.4. Stationarity Tests
8.5. Stationary vs Non Stationary Time Series
8.6. Time Series Decomposition
8.7. Advanced Time Series
8.7.1. FB Prophet
8.7.2. Gluon TS
8.7.3. GreyKite
8.8. Time Series Forecasting using Neural Networks
8.8.1. LSTM
8.8.2. RNN
8.9. TS Evaluation Metrics
9. Deep Learning
9.1. CNN
9.2. RNN
9.3. LSTM
10. NLP
10.1. Represenation Methods
10.1.1. BOW
10.2. Pre-Processing
10.3. Word2Vec
10.4. NLP Applications
11. DS Project Management Framework
11.1. CRISP DM
11.2. SEMMA
11.3. KDD
12. MLOps
12.1. MLOps Overview
12.2. MLOps Landscape
12.3. DevOps vs MLOps
12.4. Feature Stores
12.5. Model Governance
12.6. Model Monitoring
12.7. Model Deployment
Repository
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Handling Outliers
2.6.
Handling Outliers
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This chapter outlines the different ways to detect and handle outliers