Prerequisites

• Basic mathematics and statistics knowledge
• Basic computer literacy
• Familiarity with spreadsheet software
• Recommended: Basic programming skills (helpful but not mandatory)
• Computer with internet access
• Recommended laptop/desktop with minimum 8GB RAM

12-Week Course Syllabus

Week 1: Foundation and Programming Fundamentals

• Introduction to Data Science
• Python programming basics
• Development environment setup
• Basic data types and structures
• Fundamental programming concepts
• Version control with Git
• Jupyter Notebook introduction

Week 2: Data Manipulation and Analysis

• NumPy and Pandas fundamentals
• Data cleaning techniques
• Data preprocessing
• Handling missing values
• Data transformation
• Basic statistical analysis
• Exploratory Data Analysis (EDA)

Week 3: Statistical Analysis and Probability

• Descriptive statistics
• Probability theory
• Hypothesis testing
• Confidence intervals
• Statistical inference
• Parametric and non-parametric tests
• Statistical modeling techniques

Week 4: Data Visualization

• Matplotlib and Seaborn
• Creating interactive visualizations
• Data storytelling techniques
• Dashboard creation
• Advanced charting methods
• Visualization best practices
• Communicating insights effectively

Week 5: Machine Learning Fundamentals

• Introduction to machine learning
• Supervised learning algorithms
• Regression techniques
• Classification methods
• Model evaluation metrics
• Cross-validation
• Feature engineering

Week 6: Advanced Machine Learning

• Decision trees
• Random forests
• Support vector machines
• Ensemble methods
• Hyperparameter tuning
• Model optimization
• Advanced feature selection techniques

Week 7: Deep Learning Basics

• Neural network fundamentals
• TensorFlow and Keras
• Deep learning architectures
• Convolutional neural networks
• Recurrent neural networks
• Transfer learning
• Basic deep learning projects

Week 8: Big Data and Cloud Technologies

• Introduction to big data
• Apache Spark fundamentals
• Cloud computing basics
• AWS/Google Cloud Platform
• Distributed computing
• Big data processing techniques
• Scalable data solutions

Week 9: Natural Language Processing

• Text preprocessing
• Sentiment analysis
• Named entity recognition
• Word embeddings
• Text classification
• NLP libraries (NLTK, spaCy)
• Practical NLP applications

Week 10: Database Management

• SQL advanced techniques
• NoSQL databases
• Database design
• Data warehousing
• ETL processes
• Query optimization
• Database management best practices

Week 11: AI and Ethics

• Artificial intelligence overview
• Machine learning ethics
• Bias detection in algorithms
• Responsible AI development
• Fairness in data science
• Privacy considerations
• Ethical decision-making frameworks

Week 12: Capstone Project and Career Preparation

• End-to-end data science project
• Portfolio development
• Industry best practices
• Interview preparation
• Career guidance
• Professional networking
• Final project presentations

Learning Outcomes

• Proficiency in Python programming
• Advanced data analysis skills
• Machine learning model development
• Statistical analysis expertise
• Data visualization capabilities
• Understanding of AI and big data technologies
• Practical project experience
• Professional portfolio creation

Certification

Upon successful completion, students receive a comprehensive data science certification recognizing their advanced skills and knowledge.

Note: Course content may be adjusted based on emerging industry trends and technological advancements.