The Data Science track provides a comprehensive education in the complete data science workflow, from data exploration and analysis to model development and deployment. This 12-week intensive program is designed to transform students into professional data scientists with expertise in modern tools and methodologies.

Program Structure

This track covers the full spectrum of data science skills across three progressive months, combining theoretical understanding with extensive hands-on practice using real-world datasets and business scenarios.

Month 1: Data Science Foundations

• Week 1-2: Advanced SQL for Data Science

  • SQL for analytics and complex time-series analysis
  • Advanced window functions and statistical SQL queries
  • Data extraction and preparation for machine learning
  • Query optimization for large datasets

• Week 3: Database Programming & Automation

  • PL/SQL for data preprocessing and automation
  • Stored procedures for data science workflows
  • Database-driven feature engineering
  • Automated data pipeline creation

• Week 4: Mathematical Foundations

  • Probability theory and statistical distributions
  • Linear algebra for machine learning
  • Calculus concepts for optimization
  • Statistical inference and hypothesis testing

Month 2: Core Data Science & Machine Learning

• Week 5: Advanced Data Wrangling & EDA

  • Master-level Pandas and NumPy techniques
  • Comprehensive exploratory data analysis methodologies
  • Data quality assessment and missing data handling
  • Advanced statistical analysis and correlation studies

• Week 6: Machine Learning Fundamentals

  • Supervised learning: regression and classification algorithms
  • Unsupervised learning: clustering and dimensionality reduction
  • Model selection, cross-validation, and hyperparameter tuning
  • Performance metrics and model evaluation techniques

• Week 7: Advanced ML & Feature Engineering

  • Feature selection and engineering best practices
  • Time series analysis and forecasting methods
  • Ensemble methods and advanced algorithms
  • Handling imbalanced datasets and outlier detection

• Week 8: Specialized Analytics & Visualization

  • Natural Language Processing fundamentals
  • Advanced data visualization with multiple libraries
  • Statistical modeling and experimental design
  • Business intelligence and reporting for data science

Month 3: Advanced Methods & Deployment

• Week 9: Data Science Lifecycle Management

  • End-to-end project management and methodology
  • Data science project planning and execution
  • Stakeholder communication and business impact measurement
  • Research methodologies and scientific rigor

• Week 10: Deep Learning & Advanced Methods

  • Neural network fundamentals with TensorFlow/PyTorch
  • Deep learning for structured and unstructured data
  • Transfer learning and pre-trained models
  • Computer vision and NLP with deep learning

• Week 11: Version Control & Collaboration

  • Git workflows for data science projects
  • Jupyter notebook best practices and documentation
  • Code review and collaborative development
  • Reproducible research and experiment tracking

• Week 12: MLOps & Production Deployment

  • Model deployment strategies and best practices
  • CI/CD pipelines for data science projects
  • Model monitoring and maintenance in production
  • Automated testing and quality assurance for ML models

Technology Stack

Core Data Science Tools

• Programming: Python 3.x, R (optional), SQL
• Data Manipulation: Pandas, NumPy, Dask for big data
• Machine Learning: scikit-learn, XGBoost, LightGBM
• Deep Learning: TensorFlow 2.x, PyTorch, Keras
• Visualization: Matplotlib, Seaborn, Plotly, Bokeh

Statistical & Mathematical Libraries

• Statistics: SciPy, statsmodels, pingouin
• Mathematics: NumPy, SymPy for symbolic math
• Time Series: Prophet, ARIMA, seasonal decomposition
• Optimization: scipy.optimize, hyperopt, optuna

Development & Deployment

• Environment: Jupyter Lab/Notebook, VS Code, Google Colab
• Version Control: Git, GitHub, GitLab for collaboration
• MLOps: MLflow, Weights & Biases, DVC (Data Version Control)
• Deployment: Flask, FastAPI, Streamlit, Docker

Cloud & Big Data

• Cloud Platforms: Google Cloud, AWS, Azure basics
• Big Data: Spark (PySpark), Dask, Vaex
• Databases: PostgreSQL, BigQuery, MongoDB
• APIs: RESTful services, GraphQL, web scraping

Hands-On Projects

Project 1: Predictive Analytics for Business

• Comprehensive business problem solving with machine learning
• End-to-end pipeline from data collection to model deployment
• Feature engineering and model selection for business KPIs
• Statistical analysis and hypothesis testing for business insights

Project 2: Time Series Forecasting System

• Build forecasting models for business metrics
• Implement multiple forecasting techniques and ensemble methods
• Create automated forecasting pipeline with model retraining
• Develop interactive dashboard for forecast visualization

Project 3: NLP & Text Analytics Platform

• Natural language processing for business applications
• Sentiment analysis, topic modeling, and text classification
• Build recommendation systems using NLP techniques
• Deploy text analytics API for real-time processing

Capstone Project: End-to-End Data Science Project

• Complete data science project from problem definition to deployment
• Real-world dataset with business context and constraints
• Implement full ML lifecycle including monitoring and maintenance
• Present findings to business stakeholders with actionable insights
• Deploy production-ready solution with proper documentation

Prerequisites

Required: Completion of Data Foundations track or equivalent experience including:

• Strong SQL skills for data extraction and manipulation
• Python programming proficiency with Pandas and NumPy
• Basic understanding of statistics and probability
• Experience with data visualization and analysis

Recommended:

• Mathematics: Linear algebra and calculus fundamentals
• Statistics: Statistical inference and experimental design
• Programming: Object-oriented programming concepts
• Business: Understanding of business problems and KPIs

Career Outcomes

Graduates will be prepared for data scientist, research analyst, and ML engineer positions across industries, with the skills to drive data-driven decision making and build predictive models.

Target Roles & Compensation

• Data Scientist: $90,000 - $150,000+ annually
• Senior Data Scientist: $120,000 - $200,000+ annually
• ML Engineer: $110,000 - $180,000+ annually
• Research Scientist: $130,000 - $220,000+ annually
• Principal Data Scientist: $150,000 - $250,000+ annually

Industry Applications

• Technology: Product analytics, recommendation systems, A/B testing
• Finance: Risk modeling, algorithmic trading, fraud detection
• Healthcare: Predictive diagnostics, drug discovery, clinical analytics
• Retail: Customer analytics, demand forecasting, price optimization
• Manufacturing: Predictive maintenance, quality control, supply chain
• Marketing: Customer segmentation, campaign optimization, attribution

Core Competencies

• Statistical Analysis: Advanced statistical methods and experimental design
• Machine Learning: Full spectrum of ML algorithms and techniques
• Programming: Production-quality Python code and software development
• Business Impact: Translate business problems into technical solutions
• Communication: Present complex findings to technical and non-technical audiences
• Research: Scientific methodology and reproducible research practices

Professional Development

Industry Certifications

• Google Cloud: Professional ML Engineer, Professional Data Engineer
• Microsoft: Azure Data Scientist Associate
• AWS: Machine Learning Specialty
• Cloudera: Data Science Essentials

Academic & Research

• Contribute to open-source data science projects
• Publish research papers or technical blog posts
• Participate in Kaggle competitions and data science challenges
• Attend and present at data science conferences

Continuous Learning

• Stay current with latest ML research and techniques
• Develop domain expertise in specific industries
• Learn advanced topics like causal inference and Bayesian methods
• Build expertise in emerging areas like MLOps and AutoML

Next Steps

Advanced Specialization

• Machine Learning Track: For production ML systems and MLOps
• AI Foundations Track: For deep learning and neural networks
• Generative AI Hero Track: For large language models and generative AI

Leadership & Career Growth

• Lead data science teams and mentor junior scientists
• Transition to Principal or Staff Data Scientist roles
• Move into Chief Data Officer or VP of Analytics positions
• Start data science consulting practice or join research labs

Emerging Technologies

• Specialize in cutting-edge areas like causal AI and explainable ML
• Develop expertise in quantum computing for machine learning
• Focus on ethical AI and responsible machine learning practices
• Build skills in real-time ML and edge computing applications