Data Analytics Course Outline

Course Outline: Data Analytics and Machine Learning

Understanding Data Analytics
- Definition and significance
- Real-world applications
Data Analysis vs. Data Analytics
- Key differences
- Use cases of each approach
Types of Analytics
- Descriptive Analytics – Understanding historical data
- Diagnostic Analytics – Finding causes of past events
- Predictive Analytics – Forecasting future trends
- Prescriptive Analytics – Recommending optimal decisions
- Exploratory Analysis – Identifying patterns in data
- Mechanistic Analysis – Understanding causal relationships
Mathematical Models in Analytics
- Role of mathematical models in data-driven decision-making
- Building and interpreting models
Model Evaluation Metrics
- Metrics for Classification Models
  - Accuracy, Precision, Recall, F1-Score
  - ROC Curve and AUC (Receiver Operating Characteristic)
  - Handling class imbalance
- Metrics for Value Prediction Models
  - RMSE (Root Mean Square Error)
  - MAE (Mean Absolute Error)
  - R-Squared

Introduction to Machine Learning, Deep Learning, and AI
- Definitions and distinctions
- Role of AI in modern data analytics
Applications of Machine Learning in Data Science
- Industry examples (finance, healthcare, marketing, etc.)
The Modeling Process
- Understanding raw data
- Feature Engineering – Selecting relevant attributes
- Selecting a Model – Choosing the right algorithm
- Training a Model – Fitting data to an algorithm
- Validating a Model – Cross-validation techniques
- Predicting New Observations – Making future forecasts
Types of Machine Learning
- Supervised Learning – Classification & Regression
- Unsupervised Learning – Clustering & Anomaly Detection
- Semi-Supervised Learning – Combining labeled and unlabeled data
- Ensemble Techniques – Boosting, Bagging, Random Forest
Regression and Classification
- Concept of Linear Regression and Logistic Regression
- Understanding Classification vs. Clustering
- Basics of Reinforcement Learning

Pattern Mining Overview
- Characterization & Discrimination of data patterns
- Role of frequent pattern mining in predictive analytics
Frequent Pattern Mining
- Market Basket Analysis and its applications
- Frequent itemsets, closed itemsets, and association rules
Frequent Itemset Mining Methods
- Apriori Algorithm
  - Basic approach
  - Steps involved
  - Generating association rules
- Improving Apriori Algorithm
  - Reducing computational complexity
  - Enhancements for better efficiency
- FP-Growth Algorithm
  - Differences from Apriori
  - Advantages and use cases

Introduction to Social Media Analytics
- Importance and applications
- Role in business and public sentiment analysis
Social Media Analytics Process
- Data collection & preprocessing
- Seven layers of social media analytics
- Accessing social media data (APIs, web scraping)
Key Social Media Analytics Methods
- Techniques for extracting insights from social platforms
Social Network Analysis
- Understanding networks and relationships
- Key Techniques:
  - Link Prediction
  - Community Detection
  - Influence Maximization
  - Expert Finding
Prediction of Trust and Distrust Among Individuals
- Trust score modeling
- Identifying credibility in social networks
Introduction to Natural Language Processing (NLP)
- Basics of text processing in machine learning
- NLP applications in social media
Text Analytics Techniques
- Tokenization – Splitting text into meaningful words
- Bag of Words – Representation of text in machine learning
- Word Weighting – TF-IDF, n-Grams
- Text Preprocessing
  - Stop words removal
  - Stemming and Lemmatization
  - Synonyms and parts of speech tagging
Sentiment Analysis
- Understanding sentiment classification
- Techniques for analyzing emotions in text
Document or Text Summarization
- Extractive vs. Abstractive summarization
- Applications in news and research articles
Trend Analytics