Data Science with AI

Course Overview

This intensive 5-month program transforms beginners into job-ready data scientists with expertise in artificial intelligence applications. The curriculum progresses from foundational concepts to advanced AI techniques, emphasizing hands-on projects and real-world applications throughout.

Month 1: Foundations of Data Science

Week 1-2: Programming Fundamentals for Data Science

• Python Essentials
• Python syntax and data structures
• Control flow and functions
• Object-oriented programming concepts
• Package management and virtual environments
• Development Environment Setup
• Jupyter notebooks and IDEs
• Git and GitHub for version control
• Command line basics
• Docker introduction
• Practical Exercises
• Solving data problems with Python
• Building reusable data processing functions
• Version control workflows

Project: Data processing utility with proper documentation and testing

Week 3-4: Data Manipulation and Analysis

• Data Manipulation with Pandas
• DataFrame operations
• Data cleaning techniques
• Aggregation and grouping
• Time series basics
• Exploratory Data Analysis
• Statistical summaries
• Data visualization with Matplotlib and Seaborn
• Interactive visualizations with Plotly
• Identifying patterns and outliers
• SQL for Data Scientists
• Relational database concepts
• Complex queries and joins
• Database design principles
• SQL vs. NoSQL databases

Project: Exploratory analysis of a real-world dataset with integrated SQL and Python

Month 2: Statistical Foundations and Machine Learning Basics

Week 5-6: Statistical Methods for Data Science

• Probability Theory
• Random variables and distributions
• Central limit theorem
• Bayesian vs. frequentist approaches
• Statistical Inference
• Hypothesis testing
• Confidence intervals
• A/B testing methodology
• Statistical power analysis
• Regression Analysis
• Linear regression
• Multiple regression
• Assumption validation
• Interpretation of results

Project: Statistical analysis report with hypothesis testing and regression models

Week 7-8: Machine Learning Fundamentals

• ML Concepts and Workflow
• Supervised vs. unsupervised learning
• Training, validation, and test sets
• Feature engineering and selection
• Model evaluation metrics
• Classification Algorithms
• Logistic regression
• Decision trees
• Random forests
• Support vector machines
• Regression Algorithms Linear models
• Regularization techniques (Ridge, Lasso)
• Tree-based regression models
• KNN and SVR

Project: End-to-end ML pipeline for solving a business problem

Month 3: Advanced Machine Learning and Deep Learning

Week 9-10: Advanced Machine Learning

• Ensemble Methods
• Bagging and boosting
• XGBoost, LightGBM, CatBoost
• Stacking and blending
• Unsupervised Learning
• Clustering algorithms (K-means, DBSCAN, hierarchical)
• Dimensionality reduction (PCA, t-SNE, UMAP)
• Anomaly detection
• Model Optimization
• Hyperparameter tuning
• Cross-validation strategies
• Model selection

Project: Developing an optimized ML solution for a complex dataset

Week 11-12: Deep Learning Fundamentals

• Neural Networks Basics
• Perceptrons and multilayer networks
• Activation functions
• Backpropagation
• Gradient descent optimization
• Deep Learning with TensorFlow and Keras

 

• Building and training models
• Convolutional Neural Networks (CNNs)
• Recurrent Neural Networks (RNNs)
• Transfer learning
• Computer Vision Applications Image classification
• Object detection
• Image segmentation

Project: Image classification system using transfer learning

Month 4: Advanced AI Techniques and Applications

Week 13-14: Natural Language Processing

• Text Processing Fundamentals
• Tokenization and normalization
• Bag-of-words and TF-IDF
• Word embeddings (Word2Vec, GloVe)
• Advanced NLP with Transformers
• BERT, GPT, and other transformer architectures
• Fine-tuning pre-trained models
• Sentiment analysis and topic modeling
• Named entity recognition
• Generative AI for Text
• Text generation techniques
• Prompt engineering
• RAG (Retrieval-Augmented Generation)

Project: Building a domain-specific question-answering system

Week 15-16: Reinforcement Learning and Time Series

• Reinforcement Learning Concepts
• Markov decision processes
• Q-learning and policy gradients
• Multi-armed bandits
• Applications in recommendation systems
• Time Series Analysis and Forecasting
• Time series components and preprocessing
• ARIMA and seasonal models
• Prophet and neural forecasting models
• Evaluation metrics for time series

Project: Building a reinforcement learning agent or time series forecasting system

 

 

 

Month 5: Production Systems, Ethics, and Capstone Project

Week 17-18: MLOps and Production Systems

• Model Deployment
• API development with Flask and FastAPI
• Containerization and orchestration
• Cloud platforms (AWS, GCP, Azure)
• ML Engineering
• Data and model versioning
• Experiment tracking
• CI/CD for ML pipelines
• Monitoring and maintenance
• Big Data Technologies
• Distributed computing concepts
• Spark for large-scale data processing
• Data lakes and warehouses

Project: Deploying a model as a scalable API service

Week 19-20: AI Ethics, Advanced Topics, and Capstone

• Responsible AI
• Bias and fairness in ML
• Explainable AI techniques
• Privacy-preserving machine learning
• Ethical frameworks and governance
• Emerging AI Topics
• Multimodal AI models
• Graph neural networks
• Generative models (Diffusion models, GANs)
• Large language model applications
• Capstone Project
• End-to-end data science solution
• Real-world problem addressing
• Comprehensive documentation
• Presentation to industry panel
• Learning Components
• Hands-On Labs
• Weekly coding exercises with solutions
• Interactive Jupyter notebooks
• Cloud-based computed resources for deep learning
• Assessments
• Quizzes on theoretical concepts
• Code reviews and pair programming
• Project evaluations with rubrics
• Peer review sessions
• Industry Connection
• Guest lectures from practitioners
• Case studies from real companies
• Industry-sponsored projects
• Career development workshops
• AI Tools Integration
• GitHub Copilot for assisted coding
• LLM-based debugging and code optimization
• AutoML for model selection and optimization
• AI-assisted data exploration and visualization
• Projects Portfolio

By the end of this course, students will have completed:

• 5 major projects (one per month)
• 10+ mini-projects and coding exercises
• 1 comprehensive capstone project
• GitHub portfolio showcasing all work
• Career Support
• Resume and LinkedIn profile review
• Technical interview preparation
• Job search strategies for data science roles

Professional network building

Mock interviews with industry professionals

Post-Course Resources

Alumni community access

Continued learning materials

Job placement assistance

Mentorship opportunities