Data Science

Data Science Course Curriculum

1. Introduction to Data Science

• Overview of Data Science
• History and Evolution
• Applications of Data Science in various industries
• Roles and Responsibilities of a Data Scientist

2. Programming for Data Science

• Python for Data Science
  • Introduction to Python
  • Data Structures and Libraries (NumPy, Pandas)
  • Data Manipulation and Cleaning
• R for Data Science (optional)
  • Introduction to R
  • Data Manipulation with dplyr
  • Data Visualization with ggplot2

3. Mathematics and Statistics

• Mathematics for Data Science
  • Linear Algebra
  • Calculus
  • Probability Theory
• Statistics for Data Science
  • Descriptive Statistics
  • Inferential Statistics
  • Hypothesis Testing

4. Data Visualization

• Principles of Data Visualization
• Tools and Libraries (Matplotlib, Seaborn, Plotly)
• Creating Visualizations in Python
• Dashboarding with Tableau/Power BI

5. Data Wrangling and Preprocessing

• Data Collection Techniques
• Handling Missing Values
• Data Transformation and Feature Engineering
• Dealing with Outliers
• Data Scaling and Normalization

6. Databases and Big Data

• SQL for Data Science
  • Basics of SQL
  • Advanced SQL Queries
• NoSQL Databases
  • Introduction to MongoDB
• Introduction to Big Data
  • Hadoop Ecosystem
  • Spark for Big Data Processing

7. Machine Learning

• Supervised Learning
  • Linear Regression
  • Logistic Regression
  • Decision Trees
  • Support Vector Machines
  • Ensemble Methods (Random Forest, Gradient Boosting)
• Unsupervised Learning
  • Clustering (K-means, Hierarchical)
  • Principal Component Analysis (PCA)
  • Anomaly Detection
• Reinforcement Learning (optional)
  • Basic Concepts
  • Q-Learning
  • Applications

8. Deep Learning

• Introduction to Neural Networks
• Deep Learning Frameworks (TensorFlow, Keras, PyTorch)
• Convolutional Neural Networks (CNNs)
• Recurrent Neural Networks (RNNs)
• Natural Language Processing (NLP)
• Practical Projects with Deep Learning

9. Model Evaluation and Optimization

• Model Evaluation Techniques
  • Cross-Validation
  • Metrics for Regression and Classification
• Hyperparameter Tuning
  • Grid Search
  • Random Search
• Model Deployment
  • Introduction to MLOps
  • Deploying Models using Flask/Django

10. Capstone Project

• End-to-end Data Science Project
  • Problem Definition
  • Data Collection and Cleaning
  • Exploratory Data Analysis (EDA)
  • Model Building and Evaluation
  • Final Presentation

11. Ethics and Best Practices in Data Science

• Data Privacy and Security
• Ethical Considerations
• Best Practices in Data Handling and Reporting

12. Career Preparation

• Building a Data Science Portfolio
• Resume and LinkedIn Optimization
• Interview Preparation
• Networking in Data Science