Data Analysis — post-baccalaureate certificate
Program Overview
The UFV Data Analysis Post-Baccalaureate Program
The UFV Data Analysis post-baccalaureate program was designed to respond to the exploding demand for data analysts across all sectors of the economy. The ten-month program equips you with the practical skills you need to turn data into the business intelligence that companies and institutions highly value in the marketplace.
Program Description
You learn the computing techniques required to analyze vast amounts of data from varied sources, identify patterns, and present your findings compellingly to decision makers. Through UFV's Co-operative Education option, you can opt to combine your studies with a paid work term to gain valuable work experience and develop your network.
Career Outlook for Data Analysis
A recent survey by McKinsey Global Institute reports that finding the right talent is the biggest hurdle companies face in trying to integrate data and analytics into their operations. Approximately half of executives across regions and industries reported greater difficulty recruiting analytical talent than filling any other kind of role.
Jobs for data analysts are opening in a wide range of fields, from banking to health care, business to education, science to telecommunications. Salaries match the demand, with impressive entry-level wages rising to the six-figure range for senior analysts.
Career Progression
Experienced data analysts with strong leadership and business skills may progress to become senior or lead analysts, managers, chief information officers, or consultants.
Program Eligibility
The program is an ideal option if you have an undergraduate university degree and want to acquire specialized skills that can set you apart in the job market. Upgrading options are available for those who do not quite meet the entrance requirements.
Program Outline
Degree Overview
Overview:
The Data Analysis post-baccalaureate certificate program at the University of the Fraser Valley (UFV) is designed to equip individuals with the specialized skills and knowledge required to succeed in the rapidly growing field of data analysis.
This 10-month program is ideal for individuals who possess an undergraduate degree and seek to enhance their career prospects by acquiring in-demand data analysis expertise.
Objectives:
- Develop the ability to analyze large datasets from diverse sources using various computing techniques.
- Identify meaningful patterns and trends within data.
- Communicate findings effectively and persuasively to decision-makers.
- Gain a comprehensive understanding of the data analysis field, including relevant theoretical and practical concepts.
Description:
The program emphasizes a hands-on approach, equipping students with the practical skills necessary to excel in the workplace. Students gain proficiency in data manipulation, analysis, and visualization techniques using industry-standard software. The curriculum covers topics such as data mining, statistical analysis, machine learning, and data ethics.
Outline
Program Content:
The program covers a wide range of topics related to data analysis, including:
- Data collection and preparation
- Data visualization
- Statistical analysis
- Machine learning
- Data ethics
Structure:
The program consists of four modules:
- Module 1: Foundations of Data Analysis
- Module 2: Statistical Analysis and Modeling
- Module 3: Machine Learning and Data Mining
- Module 4: Capstone Project
Course Schedule:
The program is offered in a blended format, with a combination of online and on-campus courses.
- Module 2: This module focuses on statistical analysis and modeling, covering topics such as descriptive statistics, hypothesis testing, and regression analysis.
- Module 3: This module introduces students to machine learning and data mining techniques, including decision trees, clustering, and neural networks.
- Module 4: The capstone project allows students to apply their acquired skills to a real-world data analysis problem.
Assessment
Assessment Methods:
The program utilizes various methods to assess student learning, including:
- Assignments
- Quizzes
- Exams
- Presentations
- Project work
Assessment Criteria:
The assessment criteria for each module are based on the specific learning outcomes of that module. Criteria typically include:
- Accuracy
- Completeness
- Clarity
- Critical thinking
- Problem-solving
Teaching
Teaching Methods:
The program employs a variety of teaching methods to cater to different learning styles, including:
- Lectures
- Tutorials
- Case studies
- Group projects
- Guest speakers
Faculty:
The program is taught by experienced faculty members with expertise in data analysis, statistics, and computer science.
Unique Approaches:
The program incorporates real-world case studies and industry-standard software, providing students with hands-on experience and industry-relevant skills.
Careers
Potential Career Paths:
Graduates of the program may pursue careers in a wide range of fields, including:
- Data analyst
- Market research analyst
- Business intelligence specialist
- Data scientist
- Statistician
Career Opportunities:
Job opportunities for data analysts are available in various sectors, including:
- Banking and finance
- Healthcare
- Business and marketing
- Education
- Science and technology
- Telecommunications
Salary Range:
Salaries for data analysts vary depending on experience, industry, and location. However, entry-level salaries are typically in the six-figure range, with experienced data analysts earning even more.
Other
The program also includes a Co-operative Education option, allowing students to gain valuable work experience and develop their network.
Entry Requirements:
High School:
- Completion of a
- Canadian secondary school diploma or equivalent with a minimum average of
- 70% (B) in the
- final two years
- Applicants from outside Canada must hold an international credential deemed equivalent to the above by the UFV International Education department.
English Requirements:
- one of the following
- approved English for Academic Purposes (EAP) program level of proficiency
- recognized English language proficiency test:
- Test of English as a Foreign Language (TOEFL) with a score of at least:
- 86 on the internet-based test (iBT)
- 213 on the computer-based test (CBT)
- 560 on the paper-based test (PBT)
- 6.5
- 60
- 70
Additional Requirements:
- Submission of transcripts from all post-secondary institutions attended
- Mathematics: Completion of
- one university-level mathematics course (e.g., math for business, calculus, statistics), with a minimum grade of
- C
- Basic knowledge of computers and software
University Admission Requirements:
- Undergraduate degree from a recognized post-secondary institution
- two years of full-time university study
- specific prerequisite courses
Language Proficiency Requirements:
As mentioned in the entry requirements section, students whose native language is not English must demonstrate English proficiency by achieving a minimum score on one of the specified tests. The minimum scores for TOEFL, IELTS, PTE Academic, and CAEL have been detailed above.
