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330
Sub Topics
946
MCQs
517
MCOs
738
True/False
405
Fill Blanks
152
Rearrange
382
Matching
183
Comprehensions
360
Flashcard Decks
Curriculum
What You'll Learn
01 Introduction to Data Science 4 topics
1 Data Science Overview
- Definition and Scope
- Historical Development
- The Data Science Process
2 Roles in Data Science
- Data Scientist
- Data Analyst
- Data Engineer
- Machine Learning Engineer
3 Applications of Data Science
- Business Intelligence
- Scientific Research
- Healthcare
- Finance
- Marketing
- Manufacturing
4 Ethics in Data Science
- Privacy Concerns
- Bias and Fairness
- Transparency and Explainability
- Responsible AI
02 Mathematics for Data Science 4 topics
1 Linear Algebra
- Vectors and Matrices
- Matrix Operations
- Eigenvalues and Eigenvectors
- Singular Value Decomposition
2 Calculus
- Derivatives and Gradients
- Partial Derivatives
- Optimization Methods
- Lagrange Multipliers
3 Probability Theory
- Random Variables
- Probability Distributions
- Joint and Conditional Probability
- Bayes' Theorem
4 Statistics
- Descriptive Statistics
- Statistical Inference
- Hypothesis Testing
- Confidence Intervals
- p-values and Statistical Significance
03 Programming Fundamentals 3 topics
1 Python Programming
- Data Types and Structures
- Control Flow
- Functions and Methods
- Object-Oriented Programming
- Package Management
2 R Programming
- Data Structures in R
- Functions and Packages
- Data Manipulation in R
- Statistical Analysis in R
3 SQL and Database Management
- Relational Databases
- SQL Queries
- Joins and Relationships
- Database Design
- NoSQL Databases
04 Data Collection and Storage 4 topics
1 Data Sources
- Structured Data Sources
- Unstructured Data Sources
- Semi-structured Data Sources
- Real-time Data Streams
2 Data Collection Methods
- Web Scraping
- APIs
- Surveys and Forms
- Sensors and IoT Devices
3 Data Storage Systems
- Relational Databases
- Data Warehouses
- Data Lakes
- Cloud Storage Solutions
4 Big Data Technologies
- Hadoop Ecosystem
- Spark
- Distributed Computing
- Stream Processing
05 Data Preprocessing 4 topics
1 Data Cleaning
- Handling Missing Values
- Outlier Detection and Treatment
- Duplicate Detection
- Error Correction
2 Data Transformation
- Normalization
- Standardization
- Log Transformation
- Box-Cox Transformation
3 Feature Engineering
- Feature Creation
- Feature Selection
- Dimensionality Reduction
- One-Hot Encoding
- Binning and Discretization
4 Data Integration
- Merging Data Sources
- Entity Resolution
- Data Fusion Techniques
- Time Series Alignment
06 Exploratory Data Analysis 4 topics
1 Descriptive Statistics
- Measures of Central Tendency
- Measures of Dispersion
- Correlation Analysis
- Distribution Analysis
2 Data Visualization
- Basic Plots (Histograms, Scatter Plots, Box Plots)
- Advanced Visualizations
- Interactive Visualizations
- Geospatial Visualization
3 Visualization Libraries
- Matplotlib
- Seaborn
- Plotly
- Tableau
- D3.js
4 Data Profiling and Reporting
- Automated Profiling
- Dashboards
- Interactive Reports
- Storytelling with Data
07 Statistical Analysis and Inference 4 topics
1 Parametric Methods
- t-tests
- ANOVA
- Linear Regression
- Multivariate Analysis
2 Non-parametric Methods
- Chi-Square Tests
- Mann-Whitney U Test
- Kruskal-Wallis Test
- Wilcoxon Signed-Rank Test
3 Bayesian Statistics
- Bayesian Inference
- Prior and Posterior Distributions
- Bayesian Networks
- Markov Chain Monte Carlo Methods
4 Experimental Design
- Control and Treatment Groups
- Randomization
- A/B Testing
- Multivariate Testing
08 Machine Learning Fundamentals 4 topics
1 Supervised Learning
- Classification
- Regression
- Evaluation Metrics
- Bias-Variance Tradeoff
2 Unsupervised Learning
- Clustering
- Dimensionality Reduction
- Anomaly Detection
- Association Rules
3 Semi-supervised Learning
- Self-training
- Co-training
- Multi-view Learning
- Active Learning
4 Reinforcement Learning
- Markov Decision Processes
- Q-Learning
- Policy Gradients
- Deep Reinforcement Learning
09 Machine Learning Algorithms 5 topics
1 Linear Models
- Linear Regression
- Logistic Regression
- Ridge and Lasso Regression
- Support Vector Machines
2 Tree-based Models
- Decision Trees
- Random Forests
- Gradient Boosting Machines
- XGBoost and LightGBM
3 Ensemble Methods
- Bagging
- Boosting
- Stacking
- Voting
4 Clustering Algorithms
- K-Means
- Hierarchical Clustering
- DBSCAN
- Gaussian Mixture Models
5 Dimensionality Reduction
- Principal Component Analysis
- t-SNE
- UMAP
- Autoencoders
10 Deep Learning 5 topics
1 Neural Networks
- Perceptrons
- Multi-layer Networks
- Activation Functions
- Backpropagation
2 Convolutional Neural Networks
- Convolutional Layers
- Pooling Layers
- Transfer Learning
- Object Detection and Segmentation
3 Recurrent Neural Networks
- LSTM and GRU
- Sequence Modeling
- Time Series Forecasting
- Encoder-Decoder Architecture
4 Generative Models
- Variational Autoencoders
- Generative Adversarial Networks
- Diffusion Models
- Transformer-based Generative Models
5 Transformers and Attention
- Self-Attention
- Multi-head Attention
- BERT, GPT, and Other Architectures
- Fine-tuning Pre-trained Models
11 Model Evaluation and Optimization 4 topics
1 Evaluation Metrics
- Classification Metrics
- Regression Metrics
- Ranking Metrics
- Multi-class and Multi-label Metrics
2 Validation Techniques
- Train-Test Split
- Cross-Validation
- Stratified Sampling
- Time Series Validation
3 Hyperparameter Tuning
- Grid Search
- Random Search
- Bayesian Optimization
- Automated ML (AutoML)
4 Model Selection and Ensemble Techniques
- Model Comparison
- Stacking and Blending
- Bagging and Boosting
- Model Averaging
12 Natural Language Processing 4 topics
1 Text Preprocessing
- Tokenization
- Stemming and Lemmatization
- Stop Word Removal
- Part-of-Speech Tagging
2 Text Representation
- Bag of Words
- TF-IDF
- Word Embeddings (Word2Vec, GloVe)
- Contextual Embeddings (BERT, GPT)
3 Text Analysis
- Sentiment Analysis
- Topic Modeling
- Named Entity Recognition
- Relationship Extraction
4 Language Generation
- Text Summarization
- Machine Translation
- Question Answering
- Conversational AI
13 Computer Vision 4 topics
1 Image Processing
- Filtering and Enhancement
- Feature Extraction
- Edge Detection
- Morphological Operations
2 Image Classification
- Classic Techniques
- CNN-based Methods
- Fine-tuning Pre-trained Models
- Few-shot Learning
3 Object Detection and Segmentation
- Region-based CNNs
- YOLO and SSD
- Mask R-CNN
- Semantic and Instance Segmentation
4 Advanced Computer Vision
- 3D Computer Vision
- Video Analysis
- Action Recognition
- Visual Question Answering
14 Time Series Analysis and Forecasting 4 topics
1 Time Series Components
- Trend
- Seasonality
- Cyclical Patterns
- Irregularity
2 Traditional Time Series Methods
- Moving Averages
- Exponential Smoothing
- ARIMA Models
- SARIMA Models
3 Machine Learning for Time Series
- Feature Engineering for Time Series
- Regression-based Approaches
- Decision Trees and Random Forests for Time Series
- Deep Learning for Time Series
4 Advanced Forecasting Methods
- Vector Autoregression
- State Space Models
- Prophet
- Long Short-Term Memory Networks
15 Recommender Systems 4 topics
1 Content-based Filtering
- Item Feature Extraction
- User Profile Creation
- Similarity Metrics
- Recommendation Generation
2 Collaborative Filtering
- User-based Collaborative Filtering
- Item-based Collaborative Filtering
- Matrix Factorization
- Singular Value Decomposition
3 Hybrid Approaches
- Weighted Hybrid Methods
- Switching Hybrid Methods
- Feature Combination Methods
- Cascade Hybrid Methods
4 Advanced Recommender Systems
- Context-aware Recommenders
- Deep Learning for Recommendations
- Reinforcement Learning for Recommendations
- Explainable Recommendations
16 Big Data Analytics 4 topics
1 Distributed Computing
- MapReduce
- Spark RDDs
- Dataframes and Datasets
- Streaming Analytics
2 Big Data Frameworks
- Hadoop Ecosystem (HDFS, YARN, HBase)
- Spark Ecosystem (SparkSQL, MLlib, GraphX)
- Real-time Processing (Kafka, Flink)
- Cloud-based Big Data Services
3 Scalable Machine Learning
- Distributed Training
- Parameter Servers
- Federated Learning
- Online Learning
4 Big Data Visualization
- Visualizing Large Datasets
- Interactive Dashboards
- Real-time Visualization
- Dimensionality Reduction for Visualization
17 Data Science in Production 4 topics
1 Model Deployment
- REST APIs
- Containerization (Docker)
- Serverless Deployment
- Edge Deployment
2 Model Monitoring and Maintenance
- Performance Monitoring
- Drift Detection
- A/B Testing
- Model Updating
3 MLOps
- CI/CD for Machine Learning
- Versioning Data and Models
- Reproducibility
- Automated Machine Learning Pipelines
4 Scaling Data Science Solutions
- Horizontally vs. Vertically Scaling
- Load Balancing
- Caching
- Distributed Systems Architecture
18 Business Applications of Data Science 4 topics
1 Marketing Analytics
- Customer Segmentation
- Campaign Optimization
- Attribution Modeling
- Churn Prediction
2 Financial Analytics
- Risk Assessment
- Fraud Detection
- Algorithmic Trading
- Credit Scoring
3 Healthcare Analytics
- Disease Prediction
- Treatment Optimization
- Healthcare Resource Management
- Medical Image Analysis
4 Retail and E-commerce
- Demand Forecasting
- Inventory Optimization
- Price Optimization
- Personalization
19 Data Science Management and Communication 4 topics
1 Project Management for Data Science
- Requirements Gathering
- Scope Definition
- Timeline Planning
- Resource Allocation
2 Building Data Science Teams
- Roles and Responsibilities
- Skill Sets and Competencies
- Collaboration Models
- Knowledge Sharing
3 Communicating Results
- Data Storytelling
- Executive Presentations
- Technical Documentation
- Visualization Best Practices
4 Stakeholder Management
- Expectation Setting
- Progress Reporting
- Handling Feedback
- Managing Scope Changes
20 Emerging Trends in Data Science 4 topics
1 AutoML and Low-Code Solutions
- Automated Feature Engineering
- Hyperparameter Optimization
- Neural Architecture Search
- Model Selection
2 Explainable AI
- Local Explanations
- Global Explanations
- Counterfactual Explanations
- Visualization for Interpretability
3 Edge Computing and IoT Analytics
- Edge ML Algorithms
- Federated Learning
- Real-time Analytics
- Resource-constrained ML
4 Quantum Computing for Data Science
- Quantum Machine Learning Algorithms
- Quantum Neural Networks
- Quantum Optimization
- Hybrid Classical-Quantum Approaches
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