Big Data allows users to visualize past, present, and future patterns by linking and presenting information in meaningful ways. Data Analytics offers deeper insight into the meaning of data sets by telling the story behind the information. This enables stakeholders to make more informed decisions, predict trends and better understand the needs and sentiments of customers. This program provides students with a unique blend of theoretical knowledge and applied skills. Students learn how to collect, curate, manipulate, encode, and store data sets so they can be analyzed and mined in such a way that they can be reused and repurposed to solve challenges that don’t yet exist.
What are the admission requirements for the Big Data Analytics program?
Big Data Analytics admission requirements
- Post-secondary diploma, degree or equivalent. It is recommended that the applicant have a specialty in science, technology, engineering, mathematics, or business.
To be successful in this program, students are required to have a personal notebook computer (either PC or Mac architecture) prior to the start of the program that meets or exceeds the following hardware specifications:
- Intel i5 processor or AMD equivalent
- 8GB of memory (16 GB recommended)
- 250GB hard drive (SSD recommended)
Flex MORE. Study Big Data Analytics part-time
Benefits of studying part-time:
- Register on a course-by-course basis, paying as you go
- Online and evening courses that fit your schedule
- MORE choices. We are always adding more flexible delivery programs and courses
What courses are included in the Big Data Analytics graduate certificate program?
12 Program Courses
Semester 1 courses are listed below. For a full list of courses in the program including course descriptions, view the Big Data Analytics program outline.
- BDAT 1000 – Data Manipulation Techniques
- BDAT 1001 – Information Encoding Standards
- BDAT 1002 – Data Systems Architecture
- BDAT 1003 – Business Processes and Modelling
- BDAT 1004 – Data Programming
- BDAT 1005 – Mathematics for Data Analytics
What is big data analytics?
Simply put, big data analytics refers to the process of extracting meaningful insights that support decision-making by examining very large and complex sets of data. One of the main things that set big data analytics apart from other forms of analysis is sheer size of the datasets being examined. Big data also can exist in different formats (e.g., a mix of text, numeric, and video data) or in different places (e.g., separate technologies, systems, or databases)*. So here, we are talking about datasets that are as large and complex as to be impossible to examine in an efficient and meaningful way without the application of technologies, as well as the knowledge and expertise to use those technologies and interpret the results in an effective way. This is where the expertise of a person trained in big data analytics comes in.
What are some real-world examples of big data and how it is used?
There are many examples, but here’s just a few:
- In the energy sector, the Weather Company is currently using big data analytics to help utility company managers better predict, plan for, and respond to weather-related risks to infrastructure assets, thereby improving efficiency and delivering better service to customers.*
- In the health care sector, big data analytics is being used to help sequence and analyze genomes to assist in the fight against cancer, infections, and non-communicable diseases, as well as to analyze clinical datasets to understand the cost effectiveness of new drugs and treatments.**
- In the entertainment sector, companies like Disney are using big data analytics to track consumer behaviour on websites and on site to improve service offerings, efficiency, and profits.***
Big data is also being used in manufacturing (e.g., quality control analysis), engineering (e.g., structural stress analysis), marketing (e.g. social media brand perception analysis), agriculture (e.g., crop analysis), information management (e.g., digitized, searchable archival records), and many other sectors.
*Hertell, B. (2016), How analytics is helping utility companies weather storms.
**Canadian Health Infoway (2013), Big Data Analytics in Health.
***Coyne, E.M. (2015), The Disney take on the value of big data. For other examples, see Cloudera (2011), Ten common Hadoopable problems