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Statistics & Data Analytics Graduate Programs in Quebec

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Statistics Graduate Programs encompass certificates, masters and doctoral degree programs in data analytics, data science, and applied statistics. Earning a graduate degree in statistics could help students refine the mathematical skills and knowledge used to collect and analyze data.

Applied statistics graduate programs may allow students to focus on using probability theory and other statistical methods to help solve real-world problems. Because statistics may be used in business, marketing, government, healthcare, research and beyond, there could be relevant and fascinating program options for diverse goals.

DID YOU KNOW?

A Statistician is one of the top 5 fastest growing careers with a graduate degree in 2016. Learn more about Statistician salary and employment outlook.

^{iii}

Statistics Graduate Programs have both a practical and theoretical side to them. A branch of mathematics, statistics uses techniques to collect, classify, analyze and interpret numerical facts based on probability theory. These methods are used to observe and draw conclusions about all types and quantities of data. And data is everywhere, generated from Internet search, social media, smartphones, tablets and more.

*Data Science, Data Analysis and More*

Today’s statistician often work with big data and data analysis software. In fact, per the Bureau of Labor Statistics, “some people with a degree in statistics, or who collect and analyze statistical data, may not be formally known as statisticians. Instead, they may work in related fields and professions.” ^{i }What do they do? It may depend on the industry. Some are known as quantitative analysts, market research analysts, data analysts, or data scientists.^{i} This close interplay of data science, data analysis and statistics may reflected be in a graduate statistics program's courses, emphasis and goals.

Students in Statistics Graduate Programs might study the concepts, principles and theories that underlie the science of statistics. A general curriculum may explore the relationships between data sets using probability theory and methods derived from it. Some examples of possible statistics graduate courses are listed below.

- Regression analysis
- Math statistics
- Stochastic processes
- Monte Carlo method
- Bayesian statistics
- Sampling theory
- Statistical techniques (e.g. surveys)

Courses vary and may differ from those listed above. Contact individual schools for a full course list.

Applied statistics graduate programs focus on the practical application of statistics rather than on pure statistical theory. Students often engage with content that relates to the use of statistics in a specific context. Business analytics, marketing, biostatistics, and health informatics are some examples. Applied Statistics courses could include some of the following.

- Data mining
- Predictive analytics
- Statistical software
- Statistical theory and methods
- Design of experiments

Statistics is also a tool of Data Science. A broad term, data science makes use of scientific methods, like math and statistics, to extract and capture useful information about data sets. Data scientists use their know-how and abilities to analyze large, complex data sets in the context of real-world problems. They then cleanse, prepare and align data to generate actionable insight for decisions and solutions.

Data science graduate programs often discuss elements of statistics, programming and product knowledge. Coursework might include statistical topics such as hypothesis testing, survey sampling and Bayesian analysis. Students are also likely to study programming tools such as SQL, R and SAS, along with machine learning methods.

Data analytics graduate programs tend to focus on the study of raw data to find patterns and draw conclusions. The processes of data analysis are applied to data with a specific goal in mind. For instance, to enable a business to make better decisions. Students might learn strategic and predictive uses of data analytics across a broad range of industries. A general set of courses might explore topics such as data mining, visualization, modeling, optimization and the ethical uses of data.

DID YOU KNOW?

When surveyed, 48% of statisticians said they had a masters degree, 20% a doctorate degree.^{ii}

Masters in statistics degrees may be available as Master of Arts (MA), Master of Science (MS) and Master of Professional Studies degrees. Most programs entail about 30 to 42 credits and include core courses, electives, exam(s), a practicum and a capstone or final thesis. The time it takes to complete these requirements could depend on whether you pursue your studies on a full-time or part-time basis. A masters degree in statistics might be earned in anywhere from two to five years.

*Admissions *

Applicants to a masters program in statistics must hold a bachelor's degree from a regionally- accredited school, and may need a minimum GPA of at least 3.0. Students often also need to have completed undergraduate work in mathematics such as multivariable calculus (e.g. calculus III) and linear algebra. Some programs require students to have taken statistics and probability courses as well and know at least one programming language. Other admissions material could include letters of reference, a current resume and GRE Scores. Contact individual programs for exact admission requirements.

A master of arts (MA) in statistics program could provide students with a solid foundation of practical statistics knowledge. The curriculum might be like the MS in that students take multiple statistics and math courses. For instance, MA core courses might include probability, linear algebra, regression and applied multivariate analysis. In some schools, students also take a lab component that stresses the practice of modern data analysis and might expose them to methods, software and theory used in the application of statistics. Additionally, MA students might be able to take doctoral level courses as electives. This could be useful for those who intend to continue onto a PhD program.

A master of science (MS) in statistics program typically explores key topics such as regression, math statistics, statistical methods and SAS/R. Beyond the core courses, students usually must take electives and choose an area of emphasis. Emphases could highlight biostatistics, business analytics or methods of applied statistics. Most MS statistics programs include diagnostics exams to evaluate students’ grasp of the course material. Also, some programs include consulting work and a final project.

A master of science (MS) in applied statistics program could help students develop a broad knowledge base in a wide range of statistical application areas. Most programs include core or required courses in topics such as probability theory, mathematical statistics and regression methods. In these courses, students might learn about statistical inference, variables and research design. Electives expand on a student’s interests and goals, and might be chosen with the help of an advisor. Applied statistics electives could include epidemiology research methods, applied data mining and procedures in SAS.

Students further pursue their interests through a program concentration. Applied statistics emphases might include predictive analytics, data mining and machine learning, industrial analytics, and statistical theory. A practicum in statistical consulting is often required at the end of the program. This type of experience might provide students with some real-life context on how to make and present reports, and communicate their findings.

A master of professional studies (MPS) in data analytics is a practice based program which could help students learn to design, implement and apply data management techniques. Descriptive, prescriptive and predictive analytics are all central to a data analytics degree. Students usually take a set of core courses that are based on data mining and applied statistics. Other prescribed courses might examine data storage technologies, database design and the application of analytics to decision making. Electives could comprise another third of a 30 credit MPS program. Students might choose to explore analytics strategies, technical project management, applied data mining or another topic in line with their goals. A final capstone might give students the opportunity to design and implement data science and data analytics systems using current tools and techniques.

A master of science (MS) in applied data science program could help students learn to apply math and statistics concepts to the analysis of data. Courses often span multiple statistics topics like hypothesis testing, survey sampling and neural networks, in addition to programming. Students work to acquire the knowledge and skills to analyze large, complex data sets in the context of real-world problems.

A master in business analytics program could help students learn to translate big data into actionable plans to solve some of today’s business problems. Courses could draw content from statistical modeling, computer programming, business intelligence and predictive analysis. Students might also learn to use data visualization tools to convert numbers and texts into pictures or interactive visual presentations. Other topics might include optimization methods, machine learning, data mining and storytelling.

A master of science (MS) in health informatics could focus on the broad area of health data analytics. Health informatics courses often reflect the intersection of biomedical science, analytics and clinical practices. A focus on health data analytics could prepare students to use health information technology for things like decision support, strategic planning and outcomes assessment.

A graduate certificate in applied statistics program is often planned-out for engineers, scientists, analysts, and other professionals who want a solid education in the statistical methods that are related to their work. Often a series of about 12 credits, an advanced certificate in statistics might be completed by a full-time student in about one year. Courses vary between programs, but usually include core courses in regression analysis and experimental design, along with electives based on student interests and school syllabi.

Applicants may need to have a bachelors degree from an accredited college or university, with some requisite courses in statistics and probability. Other admission requirements could be similar to those required by masters in statistics programs. In some schools, credits from a graduate certificate might be transferrable to a related masters degree.

A graduate certificate in applied demography program could explore the scientific study of human populations and the trends associated with it. Applied demography focuses on practical applications of demographic methods and materials for decision-making purposes. For instance, students could study of fertility, mortality and migration to determine the effects of growth or age. They are also likely to explore key demographic data sets and GIS.

A graduate certificate in business analytics program might help students learn to make informed and data-driven decisions. Courses could draw content from a Masters in Data Analytics curriculum, with a focus on business analytics. Topics could include data analytics business strategies, prescriptive analytics, and marketing analytics.

PhD statistics programs award terminal research degrees in statistics and applied statistics. Students who work towards their PhD might explore the breadth of the field, as well as focus on one area of statistics for their research and dissertation. Typically, students take required courses and electives, as well as complete several credit hours of statistical consulting experiences and research. Most statistics PhD programs include exams based on course content and a dissertation project based on original thought or theory.

A Doctor of Computer Science – Big Data Analytics is a terminal practice degree. A relevant masters degree is ordinarily required for admission. Candidates might take courses that blend theory, research and application. Core courses might present topics in database systems, computer science and information systems. Students might also take advanced courses in quantitative analysis, and business intelligence. The research component may provide a broad overview of the student’s area of emphasis to put the research into context and inform the student’s selection of a research topic. To successfully complete their doctoral program, students may have to defend a dissertation. This might be based on a review of literature, independent study and a proposal for further inquiry.

Statistics graduate programs might be found in two formats – on campus and online. Online graduate programs in statistics, data science and analytics might suit at-work professionals who can’t make it to class. Often, courses are recorded in a live classroom for grad students who prefer in-person learning. Recorded sessions are then uploaded to a course management system where students might log in from wherever, and whenever, they can, respecting deadlines. On campus programs are a more traditional route and entail regular, scheduled class time at an university facility.

There are some great statistics graduate programs to choose from. We have listed just a few examples of the types of statistics and data analytics degrees available. Now it’s time for you to start your search. We suggest you use the filters on this page to find statistics degrees by level- masters, PhD, and certificate, and program format – online or on campus. You can also look for statistics graduate programs and schools in a specific city or state. Find a few that interest you? Use the contact form to request information and get started right away.

[i] bls.gov/ooh/math/statisticians.htm | [ii] onetonline.org/link/summary/15-2041.00 | [iii] bls.gov/emp/ep_table_103.htm

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