Data Science

Week 1: Introduction to Data Science

• Session 1: What is Data Science?

  • Overview of data science and its significance
  • Applications in various industries
  • Key components: statistics, machine learning, and data analysis

• Session 2: Data Science Lifecycle

  • Understanding the data science process (CRISP-DM)
  • Problem definition, data collection, data preparation, modeling, evaluation, and deployment

Week 2: Programming Fundamentals

• Session 3: Introduction to Python/R

  • Setting up the environment (Anaconda, Jupyter Notebook, RStudio)
  • Basic syntax and data types in Python/R
  • Control structures and functions

• Session 4: Data Manipulation with Pandas (Python) / dplyr (R)

  • Loading and exploring datasets
  • Data cleaning and preprocessing
  • Filtering, grouping, and aggregating data

Week 3: Data Visualization

• Session 5: Principles of Data Visualization

  • Importance of data visualization in data science
  • Types of visualizations and best practices

• Session 6: Visualization Libraries

  • Creating visualizations with Matplotlib and Seaborn (Python) or ggplot2 (R)
  • Interactive visualizations with Plotly

Week 4: Statistical Foundations

• Session 7: Descriptive Statistics

  • Measures of central tendency and variability
  • Data distributions and visualization

• Session 8: Inferential Statistics

  • Hypothesis testing and p-values
  • Confidence intervals and significance testing

Week 5: Introduction to Machine Learning

• Session 9: What is Machine Learning?

  • Types of machine learning: supervised, unsupervised, and reinforcement learning
  • Overview of machine learning workflows

• Session 10: Linear Regression

  • Understanding linear regression models
  • Implementation in Python/R
  • Evaluating model performance (R², RMSE)

Week 6: Advanced Machine Learning Techniques

• Session 11: Classification Algorithms

  • Logistic regression, decision trees, and k-nearest neighbors (KNN)
  • Implementing classification models in Python/R

• Session 12: Ensemble Learning

  • Introduction to Random Forest and Gradient Boosting
  • Techniques for model improvement and evaluation

Week 7: Unsupervised Learning

• Session 13: Clustering Techniques

  • K-means clustering and hierarchical clustering
  • Applications and evaluation of clustering results

• Session 14: Dimensionality Reduction

  • Principal Component Analysis (PCA)
  • Use cases for PCA and visualization of high-dimensional data

Week 8: Working with Big Data

• Session 15: Introduction to Big Data Technologies

  • Overview of big data frameworks (Hadoop, Spark)
  • Introduction to NoSQL databases (MongoDB)

• Session 16: Data Acquisition and APIs

  • Techniques for web scraping
  • Working with APIs to gather data

Week 9: Deployment and Productionizing Models

• Session 17: Model Deployment

  • Overview of deployment strategies (batch vs. real-time)
  • Introduction to Flask for building APIs

• Session 18: Monitoring and Maintenance

  • Model monitoring and performance tracking
  • Handling model drift and retraining

Week 10: Data Ethics and Privacy

• Session 19: Ethical Considerations in Data Science

  • Data privacy regulations (GDPR, CCPA)
  • Ethical implications of data usage

• Session 20: Responsible AI Practices

  • Fairness, accountability, and transparency in AI
  • Strategies for mitigating bias in models

Week 11: Capstone Project Preparation

• Session 21: Defining the Capstone Project

  • Selecting a real-world problem
  • Formulating questions and hypotheses

• Session 22: Data Collection and Preparation for Projects

  • Gathering data and initial exploration
  • Preparing data for analysis

Week 12: Capstone Project Presentation and Review

• Session 23: Project Work Session

  • Finalizing projects and preparing presentations
  • Peer feedback sessions

• Session 24: Final Project Presentations

  • Presenting capstone projects to the class
  • Discussion and feedback from instructors and peers

Assessment:

• Participation in Sessions: 10%
• Homework Assignments: 30%
• Quizzes on Key Concepts: 20%
• Capstone Project: 40%