Module 1: Introduction to Data Science

• Overview of data science and its applications
• Historical context and evolution of data science
• Basics of data types and data structures
• Introduction to Python programming languages commonly used in data science
• Tools and environments for data science (e.g., Jupyter Notebook, Visual Studio Code, Google Collab)
• Ethics and responsible use of data in data science
  
  

Module 2: Statistical Analysis and Probability

• Fundamentals of probability theory
• Descriptive statistics and summary metrics
• Inferential statistics (e.g., hypothesis testing, confidence intervals)
• Correlation and regression analysis
• Time series analysis
  
  

Module 3: Data Collection, Preprocessing and Manipulation

• Techniques for data collection (web scraping, APIs)
• Data cleaning and preprocessing
• Handling missing data and outliers
• Data wrangling techniques (e.g., reshaping, merging, pivoting)
• Exploratory Data Analysis (EDA) techniques and visualization
  
  

Module 4: Machine Learning Fundamentals

• Introduction to machine learning concepts
• Supervised learning techniques (e.g., regression, classification)
• Unsupervised learning techniques (e.g., clustering, dimensionality reduction)
  
  

Module 5: Neural Networks and Deep Learning

• Introduction to Artificial Neural Networks (ANNs)
• Building Neural Networks with TensorFlow and Keras
• Convolutional Neural Networks (CNNs) for Image Recognition
• Recurrent Neural Networks (RNNs) for Sequence Data
  
  

Module 6: Natural Language Processing (NLP)

• Text Preprocessing and Tokenization
• Sentiment Analysis with VADER and TextBlob
• Named Entity Recognition (NER) with SpaCy
• Building Text Classification Models with NLTK
  
  

Module 7: Feature Engineering and Model Optimization

• Feature Scaling and Normalization Techniques
• Handling Categorical Variables: One-Hot Encoding
• Bias-variance tradeoff and overfitting
• Hyperparameter tuning Model evaluation and validation techniques
• Ensembling Methods: Bagging, Boosting, and Stacking
• Model evaluation and validation techniques
  
  

Module 8: Capstone Project and Portfolio Development

• Applying Data Science and AI Techniques to a Real-World Dataset
• Project Planning, Execution, and
• Presentation
• Building a Comprehensive Data Science Portfolio
• Showcasing Analytical Skills and AI
• Solutions
  
  

Module 9: Emerging Trends in Data Science

• Introduction to emerging technologies in data science (e.g., AI ethics, explainable AI)
• AI Ethics, Explainable AI, and Responsible AI Practices
• Understanding Ethical Considerations in AI Development
• Bias and Fairness in Machine Learning Models
• Ethical AI Implementation (Case Studies )
• Privacy and Security Measures in AI Systems