Data Science Program

Program Overview

The Data Science Program offers comprehensive training in programming, data tools, soft skills, and data analysis. Delivered in flexible formats (12-week bootcamp or 30-week flex program), it equips students with the knowledge and skills necessary for careers in data science, including data scientist, data analyst, machine learning engineer, and data engineer.

Program Outline

Outline:

• Content: The Data Science Program covers a wide range of topics, including:
• Programming for Data Science (Python, PostgreSQL, Git, Jupyter Notebook)
• Data Tools (SQL, Jupyter Notebook, Data Wrangling, Data Visualization, Machine Learning, Data Engineering and DevOps)
• Data Soft Skills (Communication and Presentation for Technical and Non-Technical Audiences, Teamwork, The Scientific Method, Experimental Design, Interview Skills)
• Data Analysis (Probability and Statistics, Data Ethics and Access, Extract-Transform-Load Process, SQL (PostgreSQL), Data Types and Datasets, Data Manipulation, QA Processes, Pandas, NumPy)
• Structure: The Data Science Program is offered in two formats:
• 12-Week Bootcamp: 5 lectures per week, ~50 hours of study time per week
• 30-Week Flex Program: 2 lectures per week, ~25 hours of study time per week
• Course Schedule:
• 12-Week Bootcamp: Classes are held Monday through Friday.
• 30-Week Flex Program: Classes are held on Mondays and Wednesdays or Tuesdays and Thursdays.
• Modules:
• Programming for Data Science:
This module introduces students to the basics of programming, including Python, PostgreSQL, Git, and Jupyter Notebook.
• Data Tools: This module covers a variety of data tools, including SQL, Jupyter Notebook, Data Wrangling, Data Visualization, Machine Learning, Data Engineering and DevOps.
• Data Soft Skills: This module focuses on developing the soft skills that are essential for success in data science, such as communication, teamwork, and problem-solving.
• Data Analysis: This module covers the fundamentals of data analysis, including probability, statistics, and data mining.
• Assessment: Students are assessed through a variety of methods, including:
• Quizzes
• Exams
• Projects
• Presentations
• Teaching: The Data Science Program is taught by a team of experienced instructors who are passionate about data science.
The program uses a variety of teaching methods, including:
• Lectures
• Discussions
• Hands-on exercises
• Projects
• Careers: The Data Science Program prepares students for a variety of careers in data science, including:
• Data Scientist
• Data Analyst
• Machine Learning Engineer
• Data Engineer