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Computer Science
Data Science
Predictive Analytics
1. Foundations of Predictive Analytics
2. Data Foundation and Preparation
3. Regression Modeling
4. Classification Modeling
5. Ensemble Methods
6. Neural Networks and Deep Learning
7. Time Series Analysis and Forecasting
8. Unsupervised Learning
9. Model Evaluation and Validation
10. Model Interpretability and Explainability
11. Model Deployment and Production
12. Business Applications and Use Cases
13. Ethics and Responsible AI
Data Foundation and Preparation
Data Sourcing and Acquisition
Internal Data Sources
Transactional Databases
Customer Relationship Management Systems
Enterprise Resource Planning Systems
Log Files and Event Data
Sensor and IoT Data
External Data Sources
Public Datasets
Commercial Data Providers
Web APIs and Services
Social Media Data
Economic and Market Data
Data Integration Strategies
Data Warehousing Approaches
Extract Transform Load Processes
Real-time Data Streaming
Data Lake Architectures
Data Quality Assessment
Data Profiling Techniques
Completeness Analysis
Accuracy Verification
Consistency Checking
Validity Assessment
Data Quality Metrics
Missing Value Rates
Duplicate Detection
Outlier Identification
Distribution Analysis
Data Cleaning and Preprocessing
Missing Data Handling
Missing Data Mechanisms
Missing Completely at Random
Missing at Random
Missing Not at Random
Imputation Strategies
Simple Imputation Methods
Advanced Imputation Techniques
Multiple Imputation
Deletion Approaches
Listwise Deletion
Pairwise Deletion
Pattern-based Deletion
Outlier Detection and Treatment
Statistical Methods
Z-score Method
Interquartile Range Method
Modified Z-score
Visualization-based Detection
Box Plots
Scatter Plots
Distribution Plots
Treatment Strategies
Removal
Transformation
Capping and Flooring
Data Standardization
Format Standardization
Unit Conversion
Categorical Value Harmonization
Date and Time Standardization
Duplicate Handling
Exact Duplicate Detection
Fuzzy Duplicate Identification
Record Linkage Techniques
Data Transformation
Scaling and Normalization
Min-Max Scaling
Z-score Standardization
Robust Scaling
Unit Vector Scaling
Distribution Transformation
Logarithmic Transformation
Square Root Transformation
Box-Cox Transformation
Yeo-Johnson Transformation
Categorical Data Encoding
One-Hot Encoding
Label Encoding
Target Encoding
Binary Encoding
Frequency Encoding
Binning and Discretization
Equal-width Binning
Equal-frequency Binning
Optimal Binning
Custom Binning Strategies
Feature Engineering
Feature Creation Techniques
Mathematical Transformations
Interaction Terms
Polynomial Features
Ratio and Proportion Features
Domain-specific Feature Engineering
Text Data Features
Bag of Words
TF-IDF
N-grams
Word Embeddings
Time-based Features
Temporal Decomposition
Lag Features
Rolling Statistics
Seasonal Indicators
Geospatial Features
Distance Calculations
Spatial Clustering
Geographic Aggregations
Feature Selection Methods
Filter Methods
Correlation-based Selection
Mutual Information
Chi-square Test
ANOVA F-test
Wrapper Methods
Forward Selection
Backward Elimination
Recursive Feature Elimination
Embedded Methods
L1 Regularization
Tree-based Feature Importance
Elastic Net Selection
Dimensionality Reduction
Linear Methods
Principal Component Analysis
Linear Discriminant Analysis
Factor Analysis
Non-linear Methods
t-SNE
UMAP
Kernel PCA
Autoencoders
Exploratory Data Analysis
Univariate Analysis
Descriptive Statistics
Central Tendency Measures
Variability Measures
Distribution Shape Measures
Distribution Analysis
Histogram Analysis
Density Estimation
Q-Q Plots
Box Plot Interpretation
Bivariate Analysis
Correlation Analysis
Pearson Correlation
Spearman Correlation
Kendall's Tau
Association Analysis
Contingency Tables
Chi-square Tests
Cramér's V
Visualization Techniques
Scatter Plots
Heatmaps
Grouped Visualizations
Multivariate Analysis
Correlation Matrices
Pair Plot Analysis
Parallel Coordinates
Multidimensional Scaling
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1. Foundations of Predictive Analytics
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3. Regression Modeling