Predictive Analytics Post-Degree 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.
International
In-Class
Next Start Dates
Monday, September 11, 2023
Application Deadlines
When Should I Apply?
Applications open one year before the next program start date.
We strongly advise applicants to apply early as space is limited and demand for full-time programs is extremely high.
Key Application Deadline
- May 15
NOTE: This program may reach capacity prior to application deadline.
*Note if you do not see the program listed for the intake you are interested in on the online application portal, we have reached capacity and are no longer accepting applications.
Tuition & Fees
$21,850: 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 reguarding 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 on 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 can:
- 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
- International students are required to hold a minimum of an undergraduate degree in Business, Computer Science, Engineering, Mathematics, Statistics, or Physics
- A Higher National Diploma from Nigeria and Ghana are exceptions
- Advanced programming knowledge is strongly recommended.
- In lieu of an undergraduate degree in the above fields, a combination of a bachelor degree AND work experience involving analysis of data in an organizational or applied research context may be considered on an exceptional basis.
- Advanced programming knowledge is strongly recommended.
- The Cumulative Grade Point Average (GPA) or Final Percentage on these transcripts will be assessed for eligibility.
- If a higher degree is attained (post-graduate diploma, Master’s, or PhD) the minimum GPA or percentage must be attained in all degrees.
For complete admission details by country, please visit our detailed Admission Requirements for international students.
Work Experience
We recommend applicants demonstrate professional work experience in applied computer science, applied statistics, engineering, or a business role involving the use/analysis of data.
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.
|
Test |
Score |
|
Duolingo |
Overall score: miniumum 120 Production score: minimum 100 |
|
IELTS Academic |
6.5 |
|
TOEFL (internet-based) or TOEFL MyBestScore |
86
20 on each component |
|
Heartland International English School |
80% |
|
International Language Academy of Canada |
3.3 |
|
UWinnipeg ELP |
B |
The University reserves the right to request English language proficiency testing result documentation for any international applicant.
Download the UWPACE English Language Requirements (PDF) for additional English proficiency tests and programs and waiver options for the English language requirement.
Courses & Descriptions
Career Opportunities
Certifications
Academic Credentials
On successful completion of the full-time program, you will be eligible for the following academic credentials:
- Predictive Analytics Post-Degree Diploma
Industry Credentials
The Predictive Analytics Post-Degree Diploma prepares students to seek the follow 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
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Predictive Analytics Industry Memberships
Admittance into the program includes student membership to the following associations: