Online Graduate Certificate in Data Science

students working with laptops, tablets, and cell phones with internet of things icons in the background
 

The GCDS will afford students an asynchronous path to completion.

Applications are now being accepted for our next session starting Fall, 2019.

APPLY NOW 

Program Overview

The online Graduate Certificate in Data Science program will offer data science & analytics quantitative skills in a self-paced environment meant for the professional who wishes to make a greater impact within their business or organization with advanced data analytic and coding skills.  This program will have flexibility to be completed in as few as 4 months.  The four required courses include: Applied Analytic Statistics, Programming for Data Science, Data Architecture, Data Mining & Predictive Modeling. The University of New Hampshire's Online Graduate Certificate in Data Science exposes students to current, cutting edge data programming, statistical modeling and visualization tools through guided, online instruction and applied case studies.

  • Learn the skills to analyze and leverage big data and earn analytical team promotions and the opportunity to work on analytics projects across the organization.
  • Help your organization achieve a competitive advantage by providing them with data for better decision making and performance.
  • This certificate program offers a flexible, short-turnaround time to completion allowing busy employees to participate. Enjoy applied learning in a self-paced but facilitated environment with course instructors and a student success coach.

 

Benefits of the GCDS

The University of New Hampshire's Online Graduate Certificate in Data Science exposes students to current, cutting edge algorithms, coding languages, statistical modeling and visualization tools through guided, online instruction and applied case studies.

  • Connect analytics to actionable insights that you can take back to your organization.
  • Gain mastery of Python, using it to wrangle and explore data while learning industry standard tools like version control (with Git & GitHub) and UNIX.
  • Learn to build out cross-validated predictive models, selecting and engineering the best features and learn how to turn it into reproducible data products.
  • Master the fundamentals of SQL and NoSQL databases and gain practice in distributed computing.
  • Build a valuable skill set and a portfolio of work that can be leveraged to gain employment upon completion.

This graduate certificate program offers a short-turnaround time to completion allowing busy employees to participate.  Enjoy applied learning in an asynchronous but facilitated environment with course instructors and a student success coach. 

 

Who Should Enroll?

Professionals who want to increase their earning potential, advance their careers, and make a greater impact within their business or organization with advanced data analytic and coding skills. This certificate is beneficial to those in the fields of business analysis, data analysis, financial analysis, computer science, programing, database administration, research, statistics,  science, and marketing.

 

Admissions Information

Applicants must hold a baccalaureate degree (no specific field of study is required) from an accredited college or university with a 3.0 GPA or higher. Applicants should have demonstrated quantitative aptitude in undergraduate coursework or similar work experience in analytic field. Prior coursework may include: statistics, chemistry, physics, mathematics or other quantitative courses.

  • Submit an application to the UNH Graduate School (http://gradschool.unh.edu/apply.php) with the following:
    • Prior college transcripts
    • Two letters of recommendation
    • Resume
    • Personal Statement Essay (click here to view a pdf of the essay questions)

International Students can learn more about UNH Admissions here.

Classes

Sample Schedules:

2 E-terms to Completion

4 E-terms to Completion

E-term 3 (Spring Jan-Mar) – DATA 800 & DATA 820
E-term 4 (Spring Mar-May) – DATA 821 &DATA 822
or
E-term 1 (Fall Aug-Oct) – DATA 800 & DATA 820
E-term 2 (Fall Oct-Dec) – DATA 821 & DATA 822
E-term 3 (Spring Jan-Mar) – DATA 820
E-term 4 (Spring Mar-May) – DATA 821
E-term 5 (Summer) – no course
E-term 1 (Fall Aug-Oct) – DATA 800
E-term 2 (Fall Oct-Dec) – DATA 822

 

 DATA 820 Programming for Data Science

This course covers using Python as a programming language to write, implement, and design programs that are relevant to various aspects of Analytics.  After completion of this course, students should be comfortable with the basic data structures in Python (including arrays, dictionaries, and dataframes), conditional logic and iterators, writing Python functions, and using Python libraries to read external data and perform data manipulations and data analysis.

Data 800 Introduction to Applied Analytic Statistics

This course covers the foundations of probability and inferential statistics. After completion of this course, students should be comfortable with performing basic analysis of data including descriptive statistics, data visualization and appropriate statistical tests. Different probability distributions will be introduced along with hypothesis testing, confidence intervals, linear regression, and ANOVA. Python will be used for all statistical analysis in this course. Learning objectives will be achieved with an emphasis on the application of statistical techniques to real world data sets. Pre-requisite of DATA 820.

 DATA 821 Data Architecture

In this class, students will learn the foundations of databases and large datasets: upon completion, students will be able to explore out-of-ram datasets through traditional SQL databases and NoSQL databases. Students will also be introduced to distributed computing. All learning objectives are achieved through active application of these techniques to real world datasets.

DATA 822 Data Mining & Predictive Modeling

This module covers the foundations of data mining and statistical modeling. After completion of this course, students should be comfortable with creating predictive models. Topics include data preprocessing and classification techniques using logistic regression, decision trees, support vector machines and neural networks, with some emphasis on using these models for regression also. Unsupervised learning techniques such as dimensionality reduction and clustering are also included. Learning objectives will be achieved with an emphasis on the application of statistical techniques to real world data sets. Prerequisites: Data 800 and Data 820.

 

Search all courses here.

 

Questions

Academic: phani.kidambi@unh.edu 

Non-Academic: unh.online@unh.edu 

 

You can also browse through other UNH online offerings here