Location
Start Dates

  • January 10, 2022
Duration

2Terms

Program Delivery

  • Any-time Online
Tuition Per Half-Term

Domestic: $2,400

Based on a typical eight months (two terms) to complete program, the estimated tuition total is $9,200 

Program Description

The Data Management and Analytics Post-diploma 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.

This program is taught through an innovative and flexible learning option called competency-based education. 

Competency-based education (CBE) is a flexible learning option that gives you more control over pacing and workload.

LEARN ABOUT COMPETENCY-BASED EDUCATION 



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
Course Listings

Domestic Applicants

Welcome Centre
South Campus – Main Floor
info@bowvalleycollege.ca
403-410-1402


Admission Requirements

Academic requirements
  • Completion of a diploma 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.

Please note
  •  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

Laptop specifications
  • 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.

Course Listings

Domestic Applicants

Welcome Centre
South Campus – Main Floor
info@bowvalleycollege.ca
403-410-1402

Course Listing


Full course outlines are available here.

Curriculum subject to change.

Courses

Required CoursesCredit

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 begin to apply the concepts of data structures and algorithms to develop data-driven software applications.

Course Delivery

Effective data analysis requires the integration of business understanding and data understanding. In this course, learners synthesize information about common business processes, workflows, and management strategies across a range of sectors (such as sales, marketing, accounting, quality improvement, product/service delivery, product development, and human resources), and their associated data needs. Learners then apply this knowledge to real-world business contexts to identify and define business goals and design appropriate data projects

Course Delivery

The quality of a data analysis project is limited by the quality of the data used. In this course, learners locate and select relevant, high-quality data that meets the requirements and constraints of a project. Learners then extract data from different sources (a database, a website) using the appropriate techniques(SQL, web-scraping, and Application Programming Interface (API)).

Additional Performance Standards:
This course, along with Data Wrangling, make up the Data Acquisition and Wrangling competency; competency assessments in both courses must be successfully completed to be deemed competent.

In order to be successful in this course, learners must be competent in the Data Programming course outcomes.

Course Delivery

Data wrangling is an incredibly important step in a data analysis project. Once data has been collected, it must be prepared - reviewed, cleaned, structured, and enriched - prior to analysis. In this course learners profile a dataset, reshape the data structure, identify and clean data issues (such as missing data, duplicates, outliers, coding issues), and enrich data through augmentation, aggregation, or calculation. The result of data wrangling is a finalized dataset that is well documented and ready for analysis.

Additional Performance Standards:
This course, along with Data Acquisition, make up the Data Acquisition and Wrangling competency; competency assessments in both courses must be successfully completed to be deemed competent.
In order to be successful in this course, learners must be competent in the Math for Data Analytics and Data Programming course outcomes.

Course Delivery

A key role of an analyst is to present insights in a meaningful and compelling way so that stakeholders can fulfill business objectives. In this course, learners apply the principles of visual storytelling to design visualization elements, reports, and interfaces (e.g. dashboards) that meet stakeholder needs and support decision making.

Additional Performance Standards:
This course, along with Building and Presenting Data Visualizations, make up the Visualizing Data and Insights competency; competency assessments in both courses must be successfully completed to be deemed competent.
In order to be successful in this course, learners must be competent in the Math for Data Analytics and Data Programming course outcomes.

Course Delivery

The first step in exploratory data analysis (EDA) is pattern identification. In order to identify patterns within the data, learners utilize descriptive statistical methodology that is relevant and meaningful to the project goals. Learners use graphical analysis to explore relationships and correlations within the dataset. A variety of methods are used to handle missing data and outliers within the dataset. To categorize the data, learners build unsupervised learning models. Learners summarize the outcomes of their analysis from unsupervised learning models and use the outcomes to develop actionable business insights and recommend further analysis.

Additional Performance Standards:
This course, along with Data Analysis II, make up the Performing Data Analysis competency; competency assessments in both courses must be successfully completed to be deemed competent.
In order to be successful in this course, learners must be competent in the Math for Data Analytics and Data Programming course outcomes.

Course Delivery

Using identified relationships within the data learners build simple predictive models based on regression and classification. Learners fit the data to the model, assess the model's performance and make adjustments the model's parameters. Learners summarize the outcome(s) of their analysis from predictive models and use the outcome(s) to develop actionable business insights. Learners assess further modelling opportunities and make recommendations for further data analysis.

Additional Performance Standards:
This course, along with Data Analysis, make up the Performing Data Analysis competency; competency assessments in both courses must be successfully completed to be deemed competent.

Course Delivery

In this course, learners apply a range of techniques to realize any design vision. Learners integrate their technical and communication skills to build and refine simple and complex visualizations to optimize effectiveness. Finally, learners close off a project, ensuring that decision makers have what they need to make decisions.

Additional Performance Standards:
This course, along with Designing Effective Visualizations, make up the Visualizing Data and Insights competency; competency assessments in both courses must be successfully completed to be deemed competent.

Course Delivery

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.

Course Delivery

This course is specifically focused towards supporting the mathematical principles required to apply the concepts of data analysis and big data analytics. Learners apply concepts such as probability, distributions, regression, topological analysis, and descriptive and inferential statistics to data-related contexts.

Course Delivery
  • If you are currently studying in a post-secondary program at BVC, please register for your courses via mybvc to ensure your enrolments and fees are processed appropriately.
  • Cart total based on domestic tuition rates. For information about International Tuition rates please see Fee Information
  • If you anticipate applying for a student loan, your payment will be refunded to you based on your loan award once your funding has been received by BVC.

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