Mathematics and Statistics

The rise of data science, including big data and data analytics, has recently attracted enormous attention in the popular press for its spectacular contributions in a wide range of scholarly disciplines and commercial endeavours.  With today's computing capabilities and the ubiquitous nature of the internet, more data is being generated than ever before, at a faster rate, and this data provides a wealth of information for businesses and for researchers.  Frequently, however, there is simply too much information for many traditional methods to be able to cope with, and professionals working in the field of Data Science must use a combination of mathematics, statistics and computer science to analyze the data, pull out the important trends and patterns, and then convert this information into a useable form.

As a field, Data Science seeks to structure diverse and massive datasets, and use sophisticated models to extract information and transform it into actionable policies, discoveries and decisions. Framing questions statistically is a central part of modelling, allowing us to leverage data resources to extract knowledge and obtain better answers. The fundamental statistical idea of randomness in data enables researchers to formulate questions in terms of underlying processes and to quantify uncertainty in their answers. A statistical framework allows researchers to distinguish between causation and correlation and thus to identify interventions that will cause changes in outcomes. It also allows them to establish methods for prediction and estimation, to quantify their degree of certainty, and to do all of this using algorithms that exhibit predictable and reproducible behaviour.  

Acadia's Mathematics & Statistics Data Analytics Option is designed with this in mind: to provide students with a stream of courses introducing them to the most important relevant Mathematics and Statistics concepts, as well as providing a fundamental background in the related fields of Computer Science, Business, and Economics. 

DATA ANALYTICS OPTION COURSES:
In addition to required 1000- and 2000-level Math courses:

Math 3233  Regression
Math 3283  Time Series
Math 3293  Statistical Learning
Math 3603  Operations Research 1
Math 3633  Operations Research 2
Math 3713  Ordinary Differential Equations
Math 4213  Mathematical Statistics
Math 4223  Generalized Linear Models
Math 4233  Statistical Consulting

Comp 1113  Computer Programming 1
Comp 1123  Computer Programming 2

Econ 1013  Microeconomic Principles
Econ 1023  Introduction to Macroeconomics

Busi 1013  Financial Accounting 1
Busi 2013  Management Accounting
Busi 2513  Operations Management
Busi 3603  Management Science

Students considering this area as a career field may elect to take further courses in these other disciplines, in order to either increase their background on the technical side (computer science) or enhance their understanding of the business and economics applications of these methods. Examples of further courses include Comp 2113 (Data Structures and Algorithms), Comp 3753 (Data Base Management Systems), Comp 3503 (Knowledge Discovery and Data Mining), Busi 3813 (Management Science 2), and Busi 4433 (Digital Marketing).