Techtern FullStack Data Science & Machine Learning Professional Program -Spring2024

Project-Based Full Stack Data Science Course Outline

The Techtern Program in Project-Based Full Stack Data Science prepares aspiring data scientists for dynamic careers by combining internship opportunities at Prognoz.ai with project-based training at OneCampus. Graduates emerge with a robust skill set, practical experience, and readiness to contribute effectively to data-driven decision-making processes across various industries.

Duration: 4-6 months (flexible, depending on intensity and student pace)

Overview: The Techtern Program in Project-Based Full Stack Data Science offers a unique blend of internship experience at Prognoz.ai and project-based training at OneCampus. This comprehensive program equips students with practical skills and theoretical knowledge necessary for success in data science roles, preparing them for real-world challenges in data-driven industries.

Course Objectives:

• Master Essential Tools: Gain proficiency in programming languages such as Python and R, along with libraries like NumPy, Pandas, and Matplotlib for data manipulation and visualization.
• Hands-on Experience: Work on a variety of projects spanning data acquisition, cleaning, exploratory data analysis (EDA), machine learning (ML), big data handling, and deployment of ML models.
• Full Stack Development Skills: Learn to develop front-end interfaces using Streamlit, Gradio and Plotly, as well as back-end services with frameworks like Flask and Django, ensuring end-to-end deployment of data solutions.
• Specializations and Advanced Topics: Explore advanced concepts including deep learning for image and text data, natural language processing (NLP), and ethical considerations in data science.
• Capstone Project: Apply all acquired skills to solve a complex data problem from inception to deployment, demonstrating readiness for professional roles in data science.

Program Structure:

Foundations and Setup

• Introduction to data science tools and environments (Python, Jupyter Notebook, Git)
• Review of essential mathematical and statistical concepts for data analysis

Data Acquisition and Cleaning

• Project: Analyze COVID-19 data to understand trends and patterns
• Techniques for cleaning and preprocessing data for analysis

Exploratory Data Analysis (EDA)

• Project: Explore economic indicators to derive insights and make data-driven decisions
• Visualization techniques using Matplotlib, Seaborn, and other tools

Machine Learning Fundamentals

• Project: Build predictive models for house price estimation and customer segmentation
• Evaluation metrics, model selection, and interpretation of results

Big Data and Data Engineering

• Project: Process and analyze large datasets using PySpark, implement ETL pipelines for data transformation
• Database management with SQL databases

Advanced Topics in Data Science

• Project: Develop image classification models using deep learning techniques like CNNs
• NLP applications: sentiment analysis, text classification, and topic modeling

Deployment and Full Stack Integration

• Project: Deploy machine learning models as RESTful APIs using Flask or Django, containerization with Docker
• Build interactive data visualization dashboards using D3.js, Plotly, and other tools

Capstone Project

• Collaborative capstone project tackling a complex real-world data challenge
• Integration of all learned concepts and skills, culminating in a final presentation

Professional Development

• Career skills workshop: Resume building, interview preparation, networking strategies
• Ethical considerations in data science: Privacy, bias, fairness, and responsible AI practices

Techtern Program Structure:

• Internship at Prognoz.ai: Gain hands-on industry experience in data science under the guidance of seasoned professionals.
• Project-Based Training at OneCampus: Participate in structured project-based learning sessions, enhancing technical skills through real-world applications.

Delivery and Assessment:

• Learning Format: Combination of lectures, workshops, and intensive project-based learning
• Resources: Access to datasets, online platforms (Kaggle, GitHub), and cloud services (AWS, Azure)
• Assessment: Continuous evaluation through project submissions, code reviews, quizzes, and a final assessment based on the capstone project