Certification in Practice of Data Analytics
Data Analytics is a burgeoning field due to the exponential growth of information created with today’s sophisticated technologies. The Certification in Practice of Data Analytics (CPDA) offers working professionals the opportunity to acquire the knowledge and skills that support the management and use of big data. In addition, you will be able to apply what you're learning immediately to real-world situations. Learn to ask the right questions, make inferences, and publish results.
This program is offered through the College of Engineering, but it is designed for people in any profession or industry and requires minimal knowledge about data analytics. The CPDA program currently includes five, non-credit courses and at least four must be successfully completed in order to earn the certificate of completion. All of the courses are delivered in asynchronous, online format, while the instructors are also available to assist students and to answer questions. A certificate of completion is provided after a student completes each course. Once a student completes at least four, they receive the Certification in Practice of Data Analytics from the College of Engineering.
What You Will Learn
The Certification in Practice of Data Analytics is designed to teach you to:
- Describe data graphically and numerically using statistical software.
- Interpret linear association and conduct simple linear regression data analysis.
- Learn the data mining methodology and its application for projects including data preparation techniques, modeling, evaluating results, and how data can solve business problems.
- Be competent using a Python integrated development environment (IDE) to write well-structured code and Python libraries and toolkits to import/export and analyze data for machine learning.
- Be familiar with current machine learning models for different data types and how to evaluate and tune the performance of machine learning models.
- Be able to construct, train, and apply deep neural networks and identify which deep learning methods are best suited for a given task.
- Use Python and TensorFlow to build flexible and efficient deep machine learning models.
- Perform a Work Domain Analysis to reveal informative data relationships.
- Use Tableau, a high-fidelity tool to create data-driven visualizations.
Building Your Portfolio
Throughout the data analytics courses, students will use applied learning and work with real-world data sets. This combination enables students to build a portfolio of projects that demonstrate their newly acquired data skills to advance with their existing employer or stand out when applying for new opportunities.
CPDA Courses and Registration Links
Students must complete four of the five courses listed below to earn the certification.
Click on a course to learn more!
2. Data Mining
Advantages of this Data Analytics program
Busy professionals find this program meets their professional goals while providing a flexible learning experience. As a student in the program, you’ll enjoy:
- All courses are delivered in 100% distance learning format and asynchronously.
- Being taught by top faculty from The Ohio State University College of Engineering and Department of Statistics.
- Courses that focus on the most important components of data analytics.
- The option to take standalone courses for targeted learning or complete four courses to earn the certification.
- Students select their own pace towards completion of the Certification.
What students can expect
The online delivery of each course is comprised of:
- Instructional material equivalent to a one semester credit hour class.
- Approximately 40 hours of class time per course that consists of 12-13 hours of recorded online instruction and 23-24 hours of additional out-of-class work (note - this is not the amount of time it will take a person to complete the course work).
- All course lectures are recorded and available to you 24/7 through the university's Learning Management System (LMS) called - CarmenCanvas.
- Course duration of approximately 7 weeks. See course pages for exact schedule.
- 4 CEUs are granted upon successful completion of each course.
Course Sequence and Prerequisites
The courses can be taken individually for specific interests, or students can pursue the full certification. If you intend to pursue the certification the courses must be taken in this sequence: Introductory Statistics for Data Analytics first, Data Mining second, followed by Applied Machine Learning, then Neural Networks and Deep Learning. Visualization Analytics for Sensemaking can be taken at any time. Please be sure you understand all of the specific course pre-requisites that are noted on the course information web pages.
How to Get Started in the Program
When you're ready to start, register for the first course. You do not have to apply to this program or be accepted. You can register and pay for one course at a time. When you register at least three weeks before a course start date, you can also receive a discounted price and save $125!
After you register for a course, you will be contacted by the program administration with information on how to set-up your OSU account in-order to obtain access to the Learning Management System (LMS) before the course begins. We also provide an online orientation to prepare you for online learning and it allows you to become familiar with Carmen, the LMS.