Data science specialists require a unique set of skills to analyze the numbers and information that support organizational strategy and direction. Hone your knowledge in data sources, statistics, predictive analytics and business framing and become a sought after Big Data or Data Science professional.
View courses available this term under Program of Studies.
You do not need to apply to begin this program, simply ensure you meet the admission requirements (listed on the Admission & Registration tab) and then you may register for any available courses.
Interested in taking a single course? No problem. Provided you have the appropriate prerequisites, these courses can be taken individually or as part of the full program.
Admission & Registration
- Ontario Secondary School Diploma (OSSD) or equivalent (Mohawk Academic Upgrading, GED) OR 19 years of age or older.
You do not need to apply to begin this program. Start by ensuring you meet the admission requirements listed above and then register for the courses listed in the Program of Studies when they are offered. Some courses have prerequisites, so please check the Program of Studies for details. Find out how to register for a course.
Program of Studies
The program of studies displayed below applies to students starting the program in the 2019/20 academic year.
Visit the Mohawk College Bookstore Textbook Search (opens in new window) for information on textbooks.
Successfully complete all seven courses in this suggested (NOT Mandatory) order: BUSN10120; INFO10291; INFO10290; INFO10293; INFO10295; INFO10292; INFO10289 course should be taken as the last course in the program.
Click on the course number below to check current availability and for registration information.
|Course Number||Course Name||Course Description|
|BUSN10120||Intro to Analytics and Big Data||Examine the organizational goals and value provided by analytics and big data systems and processes. Learn the terminology and operating principles of these powerful technologies including the key planning aspects of implementation. Review the goals and roles of the stakeholders involved in analytics projects. 21 hours.|
|INFO10293||Advanced Statistics for Data Science||Learn concepts and techniques for data mining. Apply a variety of probability distribution techniques to evaluate the probability of real world events. Discover logistic and multiple regression and how it is used in making predictions. Explore pattern evaluation methods for marketing data. 42 hours.|
|INFO10292||Big Data Tools||
Introduces popular Big Data tools such as the Hadoop framework and NoSQL databases. Discover the basic concepts of MapReduce and Python scripting. Through various exercises, explore widely used software for Big Data like Hive, Pig, and Spark. 42 hours.
|INFO10291||Business Problem Analysis and Data Modeling||Understand the steps in the business analytics model cycle including transforming a business problem into an analytics problem, collecting and preparing data for analysis, building an analytic model and deployment. Use software for data preparation and analysis. 42 hours.|
|INFO10290||Data Exploration and Basic Statistics for Data Science||Learn use statistical methods for collecting, analyzing, interpreting and presenting data. Apply descriptive statistics to problems to describe and interpret data. Explore inferential statistics for making probability decisions and accurate predictions. Use software to perform analysis. 42 hours.|
|INFO10289||Data Science Applied Project||In the field of data science, the ability to craft sound recommendations and business strategies is essential. Work through a full data cycle to verify data, develop visualizations of multivariate data, and construct predictive analytics visualizations. Perform exploration, analysis and prediction processes while using appropriate visualization, reporting techniques and best practices to present findings to stakeholders. 42 hours.|
|INFO10295||Predictive Analytics and Machine Learning||Introduces machine learning concepts and techniques. Learn to create and evaluate supervised and unsupervised machine learning models through the application of python libraries. Implement predictive models for diverse data types including natural language. 42 hours.|
The list of scheduled course offerings for this program is below. Please note that all offerings are subject to change. Not every course is offered every semester, so please consider this schedule when planning your academic path.
(May to Aug)
Check back soon for
|INFO10293||Advanced Statistics for Data Science||
|INFO10292||Big Data Tools||OntarioLearn|
|INFO10291||Business Problem Analysis and Data Modeling||OntarioLearn|
|INFO10290||Data Exploration and Basic Statistics for Data Science||OntarioLearn|
|INFO10289||Data Science Applied Project||
|INFO10295||Predictive Analytics and Machine Learning||OntarioLearn|
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