- January 07, 2020
Tuition & Fees
International: CAD $34,016
The Data Management and Analytics Post-baccalaureate Certificate prepares learners to uncover insights from unstructured data sets to inform data-driven decision making. Learners will determine data requirements, plan for the data life cycle, model data, and use information technology tools to gather data and interpret results. Graduates of the program will have experience with relational database systems, data warehousing, data quality improvement, and visual analytics, along with an introduction to working with big data. Graduates will be able to design data analytics projects to help organizations across sectors make informed and actionable decisions. Resolving real-life business and organizational challenges will be central to project work.
Potential Graduate Career Opportunities
Exciting career opportunities await in the public and private sectors, including companies and corporations, non-profit agencies, and government agencies.
- Data Analyst
- Database Administrator
- Data Merger Specialist
- Data Analytics Professional
- Data Architect
- Completion of a degree or equivalent in business administration, information technology, engineering, or software development
- Credit in Math 30-1 or Math 30-2 or equivalent
English language proficiency requirements
For applicants whose first language is not English, please review English language proficiency requirements.
- Learners are expected to have programming experience or to complete a preparatory course
- A laptop computer meeting minimum specifications is required for this program (see below)
- Additional course-specific software may be required
- Intel quad core CPU (i5 or i7)
- 8GB RAM (16GB recommended)
- 13 inch 1080p screen (15 inches recommended)
- a dedicated graphics card with 2GB of VRam
- 128GB solid state hard drive (256GB recommended)
- portable hard drive (for data backup)
- Windows 10
** Equivalent specification in an Apple MacBook Pro is acceptable.
Large, complex, and diverse data sets require manipulation to parse, split, edit and establish correlations between sets. In this course students examine common methods and tools that can be utilized to efficiently parse, query and display raw data sets. Students learn how to programmatically extract data from a variety of file formats and sources.
In today's globally connected world, there are countless sources of information that can be mined, correlated and leveraged by an organization. The open data movement provides organizations with the ability to access scientific, government and social research that could greatly enhance their operational and strategic effectiveness. Students in this course learn how to collect, gather, and interpret social influencers, as well as access and utilize numerous open and proprietary data sources.
Business intelligence is a set of technologies and methodologies that are capable of analyzing large amounts of data to help identify or create business opportunities. In this course, students gain experience in extracting data from a variety of sources, as well as manipulating and combining this information with other data to produce meaningful output in various formats.
Understanding the data-driven programming methodology and having a sound programming background are foundational skills for anyone interested in working with data. This course introduces students to the principles of programming and application design. In addition, students are exposed to the concepts of data structures and algorithms. Using a hands-on approach, students gain experience developing data driven software applications.
This course is specifically focused towards supporting the mathematical principles required to apply the concepts of data analysis and big data analytics. Students work through a series of hands-on assignments covering topics such as probability, distributions, regression, topological analysis, and descriptive and inferential statistics.
The collection and preservation of data allows data scientists to reuse and repurpose data sets for different applications. This course provides a strong emphasis on proper auditing techniques during the collection process to ensure validity, accuracy, completeness, consistency, and uniformity of the data. Students learn different collection methodologies that can be used to gather information, as well as proper storage techniques that can be used to make the information accessible, accurate and readily available for future use.
Data intensive applications present unique challenges for systems architects and require specialized technology solutions to support real time and deep data analytics. In this course students learn how to install, configure and administer common architecture solutions that are used to manage scalable and reliable distributed systems in real time or near real-time
Understanding business processes helps data engineers design and develop information systems that are aligned with organizational needs and goals. By examining and modeling common business workflows, processes and management strategies, students gain a deeper understanding of the diverse data needs of organizations. Students examine the data needs of common core business processes such as sales, marketing, accounting, quality improvement, product/service delivery, product development, and human resources.
Large and complex data sets often make it difficult for stakeholders to really understand the story behind the data. Accurate and appropriate visualizations highlight the main features of an information set, as well as clearly and effectively communicate information to users. In this course, students produce visualizations such as histograms, graphs, plots and treemaps that could be used in reports, dashboard widgets or infographics.
Enterprise analytics focus on the effective use of data and information to help organizations make quality decisions. Evidence-based decision making requires large amounts of high quality data to accurately reflect on past experiences and predict trends and future needs. Utilizing different analytical methodologies, students learn how descriptive, predictive, and prescriptive analytics can be applied to a variety of industries such as commerce, finance, health care, marketing, supply chain, retail, and transportation. Ultimately, students develop a Performance Evaluation Framework for their topic of choice that replicates that of actual industry scorecards.
Working alone or in a small team, students research, design, develop, and implement an applied big data analytics research project to satisfy a real organizational or community need. Students are expected to apply all of their knowledge and skills to produce a functioning prototype of their project idea.