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Predictive Analytics Diploma
Programs Page
Stay ahead of the competition with this cutting-edge applied program focused on harnessing big data for smart business decisions that optimize competitive and social benefits within an organization. Graduates of this program master a range of analytical models to become strategic leaders in any industry.
Domestic
In-Class
Next Start Dates
Monday, September 11, 2023
Wednesday, August 21, 2024
Tuition & Fees
$15,100: Tuition cost includes the $100.00 UWSA Fee. Costs are tentative.
$1,000-$1,500: Textbook costs may vary depending on new, rental, used, or electronic purchasing choices. Textbooks are a requirement
For more information regarding tuition & fees and payment options, click here.
Duration
12 months in duration, Mon-Fri, hours may vary.
Two breaks of one week throughout the program and two weeks off for winter break.
Objectives & Outcomes
The use of large volumes of data to forecast and predict human behaviour, business, and socio-economic outcomes has accelerated rapidly in recent years. This program builds foundational skills in data science with a specialization in the use of predictive models to forecast outcomes. Students will learn to apply data science and predictive modelling to provide insights, predictions, and operationalize the use of data for competitive and social benefits within an organization.
Successful graduates of this program shall be able to:
- Design and implement data acquisition, management, and cleansing solutions to support data science applications;
- Use effective visualizations to communicate patterns, trends, and other findings stemming from the analysis of data;
- Use statistical techniques in the development of data science applications;
- Describe legal, ethical, and societal considerations arising from the application of data science and predictive analytics;
- Identify suitable applications and recommend the use of predictive analytical models in a business context;
- Design the overall solution architecture required to support predictive modeling applications; and
- Design, test, and implement predictive analytical models.
Admission Requirements
Academic Requirements
a. Undergraduate degree or college diploma in Business, Computer Science, Engineering, Mathematics, Statistics, or Physics
or
b. In lieu of a degree/diploma; significant work experience involving analysis of data in an organizational or applied research context may be considered on an exceptional basis
Programming knowledge is strongly recommended.
Work Experience
Predictive Analytics Diploma applicants must have a minimum of two years of professional work experience in applied computer science, applied statistics, engineering, or a business role involving the use/analysis of data. This must include a minimum of one year of experience within the last five years using Python and SQL.
Additional work experience in Java and XML is preferred.
English Language Requirements
Applicants whose previous education was completed outside of Canada and where English may or may not have been the medium of instruction, must submit proof of meeting one of the accepted English language proficiency measures.
Download the UWPACE English Language Requirements (PDF) for additional English proficiency tests and programs and waiver options for the English language requirement.
International Students
Please visit our detailed Admission Requirements for international students.
The University reserves the right to request English language proficiency testing result documentation for any international applicant.
Courses & Descriptions
Student Life
Find more information on student life at PACE here.
Career Opportunities
Certifications
Academic Credentials
Upon completion of this program students will earn the Predictive Analytics Diploma
The Predictive Analytics Diploma prepares students to seek the following professional certifications:
- Certified Analytics Professional (CAP)
- Certified Business Intelligence Professional (CBIP)
Tech Requirements
Students must have a laptop that meets the tech requirements for their program (not included in tuition)
PC hardware
- CPU: Multicore Intel® or AMD processor (2 GHz or faster processor with SSE 4.2 or later) with 64-bit support.
- RAM: 8GB of RAM (16GB recommended)
- Graphics card: 2 GB of VRAM
- Hard Disk space: 256GB (500GB recommended)
- Operating system: Microsoft Windows 10 or 11 (64 bit) (versions Windows 10 versions 20H2, 21H1, 21H2, and Windows 11 version 21H2)
Apple hardware
- Processor: Multicore Intel® processor (2 GHz or faster processor with SSE 4.2 or later) with 64-bit support or Apple silicon/M1
- Operating system: macOS 12.0 (Monterey), version 11 (big sur) and version 10.15 (Catalina)
- RAM: 8GB (16GB recommended)
- Hard disk space: 256GB (500GB SSD recommended)
- Software to run Virtualization for Microsoft Windows
- Parallels Desktop or VirtualBox
Additional Comments
Student Success Story
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Predictive Analytics Industry Memberships
Admittance into the program includes student membership to the following: