COURSE CURRICULUM

Module Description:
Lay the groundwork with core data science concepts, statistical analysis, and data-driven problem-solving approaches.

• Data types and structures: Numbers, Strings, Lists, Arrays, and DataFrames
• Descriptive statistics: Mean, Median, Mode, Standard Deviation, Variance
• Probability theory and distributions: Normal, Binomial, Poisson
• Sampling techniques and hypothesis testing
• Data visualization: Matplotlib, Seaborn, Plotly
• Introduction to exploratory data analysis (EDA)

Module Description:
Master Python programming and key data science libraries to perform data manipulation, analysis, and machine learning tasks.

• Advanced Python programming concepts
• Working with data structures: NumPy, Pandas
• Data preprocessing and cleaning techniques
• Introduction to machine learning algorithms: Linear Regression, Logistic Regression, KNN, Decision Trees
• Model evaluation and performance metrics
• Handling missing data, outliers, and categorical variables

Module Description:
Understand the foundations of Generative AI, including models like GANs, VAEs, and diffusion models.

• Introduction to Generative AI: Concepts and Applications
• Building and training Generative Adversarial Networks (GANs)
• Variational Autoencoders (VAEs) and their use in generative models
• Diffusion models for high-quality image generation
• Advanced loss functions and training techniques for generative models
• Applications of Generative AI in image, text, and data generation

Module Description:
Deep dive into neural networks, deep learning models, and their applications in data science.

• Introduction to neural networks and deep learning
• Building and training neural networks with TensorFlow and Keras
• Convolutional Neural Networks (CNNs) for image classification
• Recurrent Neural Networks (RNNs) and LSTMs for sequence data
• Transfer learning and fine-tuning pre-trained models
• Hyperparameter tuning and model optimization

Module Description:
Learn the principles of Natural Language Processing (NLP) and how Generative AI is transforming the field.

• Text preprocessing and tokenization
• Text classification using machine learning models
• Word embeddings (Word2Vec, GloVe) and transfer learning
• Building chatbots with deep learning models
• Text generation with Recurrent Neural Networks (RNNs) and Transformers
• Generative models for text completion and creative writing

Module Description:
Apply data science principles to business contexts for decision-making and strategic planning.

• Introduction to business analytics and data-driven decision making
• Data visualization for business insights using Tableau, Power BI
• Predictive modeling for forecasting and trend analysis
• Risk assessment and optimization techniques
• Using data science to solve business problems and improve operations

Module Description:
Gain expertise in advanced machine learning techniques and deploying models into production.

• Advanced machine learning algorithms: XGBoost, Random Forest, SVM
• Model evaluation and tuning: Cross-validation, Grid Search
• Model deployment using Flask and FastAPI
• Deploying machine learning models to the cloud: AWS, GCP, Azure
• Monitoring and maintaining deployed models

Module Description:
Apply your skills to a real-world data science project using Generative AI models to solve a practical problem.

• End-to-end project development: data collection, cleaning, model building
• Generative AI application in data augmentation and synthetic data generation
• Integration of machine learning models and generative models for complex data science solutions
• Model performance analysis and optimization
• Real-world use cases and applications of Generative AI in data science
• Presentation of project to peers and instructors