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About the Course

The Data Analytics Certificate Program provides you with the strategic skills and techniques needed to advance your organization’s ability to perform data driven decision making.  Discuss and widen your depth of knowledge in data governance, data mining , data visualization and data quality among other subjects in this comprehensive course.

Program Snapshot

The Data Analytics Certificate Program is specifically designed for employees who are leading or will be participating in a program that involves data analytics.  You will learn the essential elements needed to chart the path to take your data analytics program to the next level.  The certificate program is designed to:

  • Empower employees to identify the components of a successful data analytics program
  • Discriminate between some of the analytical buzzwords like ‘artificial intelligence,’ ‘machine learning,’ and ‘internet of things.’
  • Summarize the relationship among data governance, data quality and data management.
  • Learn how to create data visualizations that are appealing to a wide variety of users.

Led by Harrisburg University Analytics faculty, program facilitators, individual seminars are presented by subject matter experts, and include guest speakers, and interactive group exercises.

Target Audience

The Data Analytics Certificate Program is specifically designed for employees who are leading or will be participating in a program that involves data analytics.

Curriculum

The 7-module curriculum consists of a combination of presentations, industry guest speakers, case studies, and hands-on exercises.  The key areas of focus are on the components of implementing and maturing a successful data analytics program that include a data analytics culture, data quality, and data governance.  In addition, advanced topics such as data visualization, big data, data mining, predictive analytics, machine learning, and artificial intelligence will be covered.

A Capstone project, applying the program’s core curriculum to a data analytics assessment project will be produced and presented by participants.  Or, the Capstone project can be customized to meet specific data analytics challenge you would like to address.  Each learning module is linked to the Capstone to ensure you are assessing every aspect of your organization’s data maturity.

Learning Methods

The class includes lecture and discussion, case studies, exercises, and discovery.

Schedule & Topics – 7 Modules

Classes meet from 1:00 pm – 4:30 pm, ET on Day 1 and 8:30 am – 4:30 pm, ET on Day 2 of each Month.

7 Modules (1.5 days per month)

Month 1: Introduction to Analytics & Machine Learning
Recognition and awareness of Data Analytics & Machine Learning (ML) is essential for Decision Makers to shape their organizations, be proactive and agile, and gain a competitive edge using data driven decisions.

Month 2: Building a Data-Driven Culture & Organization
Success in today’s business world necessitates transformation to a data-driven culture, building teams within the organization that can shift from an instinct-driven enterprise to one that is based on data and insights is key.

Month 3: Data Governance & Quality
Building the proper foundation for accountability and enterprise-scale data quality (applying DataOps) is an indispensable investment that organizations must make if they truly want to become data driven.

Month 4: Communication & Visualization of Data
Organizations must design and enable a culture of collaboration to deploy and maintain an effective communication-minded visualization model that will be accepted enterprise-wide.

Month 5: Tools of Machine Learning & Intelligent Automation
Drivers of change are making it imperative for organizations to improve operational efficiency, giving rise to the need for / and adoption of intelligent automation enabled solutions.

Month 6: Limits and Risks of ML / AI Applications
While Machine Learning and Artificial Intelligence have the potential for powerful innovations – decision makers must be cognizant of their limitations to recognize misconceived benefits, predictive inaccuracies, and biases in algorithms

Month 7: Capstone
Course objectives are synthesized into an applied project – for example: the student will develop a written analytical maturity assessment report including an executive summary and formal classroom presentation describing results and an action plan.

Pre-Requisites (Qualifications)

To be considered for the Data Analytics Certificate Program, an employee must be nominated by their supervisor and have:

  • Experience in an organization working with data and analytics OR
  • Experience managing a data analytics team or project

Previous knowledge of analytical methods is helpful, but not required.

Expectations & Class Attendance Policy

Participants will be expected to complete readings and some written work prior to sessions.  Additionally, each participant is expected to complete the Capstone project outlined in the “Curriculum” section.

Attendance at a minimum of 90% of the sessions is required.

Certification of Completion

Awarding of a certificate of completion will be based on a pass-fail assessment of the program’s curricular, attendance, and project-based requirements.

Materials

The following materials are included in the cost of the course:

  • Course syllabus
  • Analytics at Work: Smarter Decisions, Better Results by Thomas H. Davenport and Jeanne G. Harris

The following are not included in the cost of the course:

  • Parking fees

Registration Fees & Policies

Fees

  • For-profit organizations: $3,745
  • Non-profit organizations and government: $2,745

Those accepted into the program or their organizations are responsible for the per-participant cost of the program.

Payment is not due upon application. It is due upon acceptance into the program, with invoices emailed with acceptance letters.

Should COVID-19 mitigation efforts end and CDC guidelines change allowing the course to be offered in person, the tuition would cover any meals provided.

Parking and lodging are not included in the tuition fee. Students are responsible for parking and lodging costs.

For information on parking in the Harrisburg University garage, and to view current hourly rates, please visit Standard Parking’s website at: www.parkharrisburg.com

Cancellation & Refunds

Harrisburg University reserves the right to cancel or reschedule courses/events at its discretion. Should a program or event be cancelled by the University, a full refund will be issued.

If you need to cancel your registration and would like a refund, please email MMafnas@HarrisburgU.edu. Please attach the confirmation email that you received at time of registration.

  • A refund of 100% will be issued within 7 business days of your registration date.
  • A refund of 50% will be issued up until 8 days prior to the start of the course/event.
  • No refunds will be issued for cancellations 7 or less days prior to the start of the course/event.

Application Process

CANDIDATE APPLICATION

The application form allows candidates to present their learning goals, past experiences, expertise, and knowledge they will bring to the cohort for consideration.  Candidates will be notified in a timely manner as to their acceptance into the Data Analytics Certificate program cohort.

NOMINATION LETTER

Candidates will demonstrate the support of their senior leadership by submitting a letter of nomination written on their behalf.  The nomination letter should include current position and responsibilities, along with skills, knowledge, and any special projects in which you are responsible.

The nomination letter should be addressed to “Data Analytics Certificate Program Directors” and emailed to ProfessionalEd@HarrisburgU.edu with the subject line as, “Data Analytics Certificate Program Nomination Letter for [YOUR FIRST AND LAST NAME]”

ACCEPTANCE NOTIFICATION

Applicants will be notified regarding whether they have or have not been accepted into the  program.

See the specific deadlines under “Next Class” at the bottom of the page.

7 Modules (1.5 days per month)

Month 1: Introduction to Analytics & Machine Learning

Recognition and awareness of Data Analytics & Machine Learning (ML) is essential for Decision Makers to shape their organizations, be proactive and agile, and gain a competitive edge using data driven decisions.

Month 2: Building a Data-Driven Culture & Organization

Success in today’s business world necessitates transformation to a data-driven culture, building teams within the organization that can shift from an instinct-driven enterprise to one that is based on data and insights is key.

Month 3: Data Governance & Quality

Building the proper foundation for accountability and enterprise-scale data quality (applying DataOps) is an indispensable investment that organizations must make if they truly want to become data driven.

Month 4: Communication & Visualization of Data

Organizations must design and enable a culture of collaboration to deploy and maintain an effective communication-minded visualization model that will be accepted enterprise-wide

Month 5: Tools of Machine Learning & Intelligent Automation

Drivers of change are making it imperative for organizations to improve operational efficiency, giving rise to the need for / and adoption of intelligent automation enabled solutions.

Month 6: Limits and Risks of ML / AI Applications

While Machine Learning and Artificial Intelligence have the potential for powerful innovations – decision makers must be cognizant of their limitations to recognize misconceived benefits, predictive inaccuracies, and biases in algorithms.

Month 7: Capstone

Course objectives are synthesized into an applied project – for example: the student will develop a written analytical maturity assessment report including an executive summary and formal classroom presentation describing results and an action plan.


Logistics

Location

VIRTUAL | Harrisburg University of Science and Technology

Contact

717-901-5100 |  ProfessionalED@harrisburgu.edu

Next Cohort

Classes meet from 1:00 pm – 4:30 pm, ET on Day 1 and 8:30 am – 4:30 pm, ET on Day 2 of each Month.

Application deadline: Wednesday, August 11, 2021

  • All applications and nomination letters are due by this date
  • See the “Application Process” above for detailed information.

Acceptance Notification: August 13, 2021

  • All applicants will be notified of whether or not they have been accepted into the program.
APPLY NOW

Class Dates

Student Orientation – Tuesday, August 24, 2021

Module 1 – Wednesday/Thursday – September 8-9, 2021

Module 2 – Monday/Tuesday – October 4-5, 2021

Module 3 – Wednesday/Thursday – November 3-4, 2021

Module 4 – Wednesday/Thursday – December 8-9, 2021

Module 5 – Wednesday/Thursday – January 12-13, 2022

Module 6 – Wednesday/Thursday – February 2-3, 2022

Module 7 – Wednesday/Thursday – March 16-17, 2022 (Capstone Presentations & Graduation)


Graduate Testimonials

This is an excellent program for both analysts and leaders who want to learn how data analytics can propel their organizations forward. The course is taught by an exceptional professor who is passionate about the topic. The best practices I learned during the class as well as the connections I made with classmates will help my organization grow in analytical maturity.

Craig Troop | Deputy Director of Research Pennsylvania Lottery

This program offered much more than expected. I was pleased with the in-depth training provided by Dr. Anderson – her enthusiasm and expertise kept the class exciting and engaging. As an analyst/statistician, I was provided with a wealth of information concerning the world of analytics I was not aware of previous to this class.

Valerie Scott | Program Analyst 3 PA Department of Military & Veterans Affairs

326 Market St, Harrisburg, PA 17101
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