Lecture on Data Science and Machine Learning Algorithms

Introduction to Data Science

• Data Science: Deriving useful insights from data to solve real-world complex problems.
• Agenda:
  1. Introduction to Data Science (fundamentals)
  2. Statistics and Probability (math behind data science and ML algorithms)
  3. Basics of Machine Learning (types, algorithms)
  4. Supervised Learning Algorithms
     • Linear Regression
     • Logistic Regression
     • Decision Trees
     • Random Forest
     • k-Nearest Neighbor (k-NN)
     • Naive Bayes
     • Support Vector Machine (SVM)
  5. Unsupervised Learning (clustering, association rule mining)
  6. Reinforcement Learning
  7. Deep Learning (neural networks)
  8. Data Science Interview Prep

Importance of Data Science

• Data generated at an unstoppable pace; needs processing.
• Helps in Business growth e.g., Walmart's use of Data Science for pattern analysis.

Components of Data Science

1. Sources of Data: IoT, Social Media, Transactions.
2. Data Scientist Skill Sets
   • Mathematics: Statistics, Probability, Linear Algebra.
   • Technology: SQL, Python, R, SAS.
   • Business Acumen.
3. Job Roles: Data Analyst, Data Scientist, Data Architect, Data Engineer.
4. Data Life Cycle: Extraction, Processing, Exploration, Modeling, Deployment.

Statistics and Probability

• Descriptive Statistics: Measures of Center (Mean, Median, Mode); Measures of Spread (Range, IQR, Variance, Standard Deviation)
• Probability Concepts: Marginal, Joint, Conditional Probability; Bayes Theorem.
• Inferential Statistics: Point Estimation, Confidence Interval, Hypothesis Testing.

Machine Learning Algorithms

1. Basics of Machine Learning

   • Supervised Learning: Uses labeled data.
     • e.g., Regression (Linear, Polynomial), Classification (Logistic Regression, SVM).
   • Unsupervised Learning: Uses unlabeled data.
     • e.g., Clustering (k-Means, Hierarchical), Association Rule Mining.
   • Reinforcement Learning: Learning from environment (rewards and punishment).
   • Deep Learning: Neural Networks, types of neural networks.

2. Types and Details

   • Linear Regression: Predicting a continuous value.
   • Logistic Regression: Binary classification problems.
   • Decision Trees: Tree structure for decisions.
   • Random Forest: Ensemble of decision trees.
   • k-NN: Classifies data points based on proximity to k nearest neighbors.
   • Naive Bayes: Based on Bayes' theorem; assumes independence between predictors.
   • Support Vector Machine (SVM): Classifies by finding hyperplane; used for both classification and regression.
   • k-Means Clustering: Partitions data into k clusters based on feature similarity.
   • Association Rule Mining: Market Basket Analysis, e.g., Apriori algorithm.

3. Advanced Topics

   • Reinforcement Learning: Markov Decision Process, Q-Learning.
   • Deep Learning Fundamentals: Understanding layers, activation functions, training neural networks.

Practical Implementation

• Examples and Use Cases: Netflix recommendation system, Facebook Auto-tagging, Amazon Alexa, Google spam filter.
• Industry Applications: Walmart's use of data science, Netflix's patterns in movie viewing, predicting disease outbreaks.
• Coding Demos: Implementing algorithms using Python libraries such as scikit-learn and TensorFlow.
• Evaluation and Optimization: Methods such as R-squared for regression, accuracy, precision, recall, F1 score, and ROC curve for classification.

Final Module: Data Science Interview Prep

• Key Concepts
• Tips for Acing Interviews

Summary

• Data Science, Machine learning algorithms play a crucial role in processing and deriving insights from large data sets for various applications.