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July 27, 2020 Kenneth Colangelo

Data Science

Certificate in Data Science (Online)

(Parent program: Technology Studies: Data Science Option, A.S.)

The Certificate in Data Science provides exposure to key elements of data science including data structures and data sources, programming languages, statistical principles, computing and analytics, data management, machine learning tools, and data science applications. This certificate needs to be paired with a transfer associates degree in any field (recommended fields include mathematics, applied sciences, computer science, computer programming, business, marketing, web design).

  • For students pursuing another degree, but want to add a marketable skill to their resume
  • Students will focus on statistics, programming, and writing professional reports
  • Can be added to any degree

What Does a Data Scientist Do?

  • Collects massive amounts of data and converts it to an analysis-friendly format;
  • Helps solve STEM or business-related challenges while using data-driven techniques and tools;
  • Uses a variety of programming languages and programs for data collection and analysis;
  • Communicates findings and suggestions through effective visuals and comprehensive reports;
  • Identifies patterns and trends in data;
  • Provides improvement plans and anticipate future demands;
  • Contributes to data mining architecture, modeling, reporting, and analysis methods;
  • Invents new algorithms to solve problems and build analytical tools.

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)

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

Contact: Nick Stugard

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