May 20, 2019 Stacey Williams

“I was advised to take data science because it would help me be competitive in the world and towards medical programs. I however did not realize that it actually became a bit fun after a while and once I found the part of data science I really enjoyed (Machine learning, Neural networks, and real world problem-solving through the Capstone course).”

“This is the challenge I wanted in life and it makes me happy to get to make progress each day in and outside of the class. I would highly recommend to anyone who doesn’t know what they want to do to try a data science class and see how expansive it can really get and the various places/roads it can bring you down.”

Joseph McElroy ’21

This certificate must be paired with a transfer associates degree or higher in any field (recommended fields include mathematics, science, computer science, computer programming, business, marketing, and web design).
Data Science + Business information sheet

Data Science + Computer Science information sheet

Data Science + Engineering information sheet

Data Science + Natural Resources information sheet

PROGRAM ADVISOR

Crystal Wiggins, cwiggins@nwcc.edu, 860.738.6310

OUTCOMES

Upon successful completion of all program requirements, graduates should be able to:

  1. Master key facets of data investigation, including data wrangling, cleaning, sampling, management, exploratory analysis, regression and classification, prediction, and data communication
  2. Implement foundational concepts of data computation, such as data structure, algorithms, simulation, and analysis
  3. Utilize various technologies to organize, analyze, explore, and visualize data
  4. Execute data organization, exploration, and develop proficiency in the programming language of R
  5. Apply advanced statistical techniques
  6. Understand machine learning models and their applications

COURSEWORK

SEMESTER 1
CSA*135 Spreadsheet Applications – 3 credits
MAT*167 Principles of Statistics – 3 credits

SEMESTER 2
MAT*222 Statistics II with Technology Apps – 3 credits
DTS*201 Data Science in R – 3 credits

SEMESTER 3
DTS*220 Intro to Machine Learning – 3 credits
**Directed Elective – 3/4 credits

Total Credits 18 (19)

** Directed Elective (see faculty advisor)