Data Science
Diploma Program
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Reach your career goals with a diploma program in data science
Diploma programs provide professional and/or technical skills for career transition in a wide variety of public and private business sectors. These programs are intended for those who have an interest in gaining additional knowledge for career transition or licensure.
| Location | On-Campus | Online |
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Washington, DC |
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Virginia |
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$12,000 | Calculate Your Cost
MSCHE Accredited
Our program follows best-practice standards for business education.
Real-World Practitioners
Learn from experienced business professionals.
Convenient Classes
Take a class online or
on-site -- it's your choice.
Data Science Diploma Program Overview
Adityah Singh
Principal Business Data Analyst
Potomac is more than an education. The university provides better career prospects, valuable employability skills, personal development and a world of opportunity.
This Program Is Accredited By
Data Science Courses & Curriculum
Program Outline
General Admission Requirements
- Complete an admissions interview conducted in person or via online methods.
- Sign and submit an attestation of high school (or equivalent) completion.
- Equivalencies include a GED Certificate. Home schooled students must present a diploma that meets the requirements of the state in which it was issued. (Students with non-US credentials please see International Student Admissions Requirements below).
- Submit a completed application
- Arrange for official transcripts from all colleges/universities previously attended to be submitted to the Office of Records and Registration, University of the Potomac.
- Submit grade reports or scores from any recognized college equivalency examinations (e.g., CLEP, DANTES, and Advanced Placement).
- Submit certificates from any corporate education training or professional development programs. (Note: An ACE evaluation form may be required to determine appropriate credit for corporate educational training.)
- Submit military training documents. (Note: An ACE military evaluation form may be required to determine appropriate credit for military training.)
Students are required to participate in the final group project as an active member of the team. Daily evaluations are done as to their involvement in the final projects.
Additionally, students must complete an individual project on a topic of their choosing. The project may include experimental design and data collection, and will be completed using several of the following techniques to bring the data to life:
- Experimental design and hypothesis testing
- Modeling
- Machine Learning techniques
- Process monitoring
- Visualization
- Student projects must be approved by an instructor or director.
- Student must be a helpful, active participant in the group project
- Student must complete the Resume Building and Interview Preparation exercises
- Student must be current on financial obligations
Complete Listing of Subjects & Synopsis
Subject Identifying Number: Week 1-2
Lecture Hours: 20
Lab Hours: 20
Total Hours: 40
Prerequisites: Admission to course
Synopsis: Students will learn the fundamentals needed to be successful throughout the rest of the program. Topics covered here are probability, Bayes Theorem, variable types, descriptive statistics, common distributions, and statistical inference.
Subject Identifying Number: Week 3-4
Lecture Hours: 20
Lab Hours: 20
Total Hours: 40
Prerequisites: Weeks 1-2
Synopsis: Students will learn the fundamentals of programming using the Python language. Topics covered here are algorithms, Boolean logic, data types, data structures, object oriented programming, best 142 practices, and debugging.
Subject Identifying Number: Week 5-6
Lecture Hours: 20
Lab Hours: 20
Total Hours: 40
Prerequisites: Week 3-4
Synopsis: Students will learn the fundamentals of organizing and extracting data using SQL and NoSQL databases.
Subject Identifying Number: Week 7-8
Lecture Hours: 20
Lab Hours: 20
Total Hours: 40
Prerequisites: Week 5-6
Synopsis: Students will learn the fundamentals of using the statistical software package R, including loading data, accessing libraries to utilize functions, and data manipulation. R will be used throughout the course to conduct analyses.
Subject Identifying Number: Week 9-10
Lecture Hours: 20
Lab Hours: 20
Total Hours: 40
Prerequisites: Week 7-8
Synopsis: Students will learn the fundamentals of creating and monitoring metrics, and will be exposed to the common practices in contemporary business settings. The principles of statistical process control will be taught and practiced. Other methods of monitoring data, such as cusum charts and moving average charts will also be taught and practiced.
Subject Identifying Number: Week 11-12
Lecture Hours: 20
Lab Hours: 20
Total Hours: 40
Prerequisites: Week 9-10
Synopsis: Students will learn the fundamentals of manipulating data to facilitate analysis. In addition, several common tools for visualization will be taught and practiced. Supporting metrics and measures that accompany the visualizations will be used.
Subject Identifying Number: Week 13-14
Lecture Hours: 20
Lab Hours: 20
Total Hours: 40
Prerequisites: Week 11-12
Synopsis: Students will learn to use hypothesis testing as part of the scientific method, and will learn and practice various basic scenarios for hypothesis testing, including one sample z- and ttests, two sample tests (paired and unpaired), analysis of variance, one- and twoproportion tests, and the Chi- 143 square test for independence.
Subject Identifying Number: Week 15-16
Lecture Hours: 20
Lab Hours: 20
Total Hours: 40
Prerequisites: Week 13-14
Synopsis: Students will learn the fundamentals and practices for several machine learning techniques, including clustering, decision trees, random forests, Bayesian networks, etc. and will understand the difference between supervised and nonsupervised systems. In addition to machine learning, students will learn useful modeling techniques, including linear regression, nonlinear regression, logistic regression, and step-wise regression.
Subject Identifying Number: Week 17-18
Lecture Hours: 20
Lab Hours: 20
Total Hours: 40
Prerequisites: Week 15-16
Synopsis: Students will learn the fundamentals and history of big data, and will practice with exercises in distributed computing. Other popular big data tools will be introduced.
Subject Identifying Number: Week 19-24
Lecture Hours: 20
Lab Hours: 20
Total Hours: 40
Prerequisites: Week 1-16
Synopsis: Students will learn to complete a thorough data mining, analysis and modeling exercise in a group setting.
Total Hours for Course Completion
Lecture: 200 / Lab: 280 / Total: 480
Diploma Program Requirements & Outcomes
After completing this course, students will be able to:
- Mine datasets for better understanding
- Create metrics, and implement monitoring plans
- Create models for prediction and planning
- Implement Machine Learning algorithms
- Use regression analysis to explain relationships
- Create visualizations
- Test various hypotheses in a designed experiment
- Prepare and deliver findings reports to all audiences.
This program is delivered by ONLINE COMPUTER BASED LEARNING.
The program requires a PC running Windows 7 or newer with a minimum of 4GB of RAM
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