Campus | Start Date | Tuition/Fees |
---|---|---|
Saint John | September 2024 (Blended Delivery) | Domestic | International |
Moncton | January 2025 (Blended delivery) | Domestic | International |
The world of business is rapidly evolving, and data is changing that landscape. Making informed decisions supported by data is critical to remaining competitive in today’s global economy. Traditional analytics is no longer enough. Businesses need individuals who can not only conduct advanced analyses, interpret their insights and recommend solutions but who can also act as a bridge between the business team and the data analyst. Graduates of the Business and Advanced Analytics program fill this need.
Our graduates establish business requirements and conduct advanced analyses to formulate data-driven solutions that support business decisions and promote competitive advantage. They also possess the insight needed to fluently articulate a range of business matters to diverse stakeholders. They develop knowledge and skill in areas such as: business requirements, processes, and strategy, statistics, data programming, data engineering, predictive analytics, Machine Learning, Cloud, visualizations, and storytelling.
The program content also covers the domains of INFORMS’ Certified Analytics Professional (CAP) certification.
The requirements for this diploma of advanced studies may be achieved within two years of full-time study.
Profile F
Graduates of the Information Technology: Business and Advanced Analytics program may be employed within a range of industries and is only limited by businesses and organizations that do not have or collect data to support business decisions. Graduates may also choose to be self-employed consultants. Careers could range from Business Analyst to Data Scientist.
It is highly recommended that students have a solid foundation in mathematics, statistics, Excel, and basic programming. Students who do not have experience in these subjects are strongly encouraged to upgrade their skills through courses, workshops, or online tutorial sites such as Khan Academy or LinkedIn Learning prior to beginning this program.
Technology Requirements
ΒιΆΉΤΌΕΔ is a connected learning environment. All programs require a minimum specification, including access to the internet and a laptop. Your computer should meet your program technology requirements to ensure the software required for your program operates effectively. Free wifi is provided on all campuses.
Courses are subject to change.
This course is designed to provide learners with foundational knowledge of data analysis and the variables involved in its collection, storage, organization, maintenance, use, and distribution. Students learn data types and applications, the data pipeline, data infrastructure, and the tools commonly used in the data analysis process. Students then apply basic data analysis principles and practices using spreadsheet software.
This course is designed to provide learners with knowledge of fundamental data management principles and practices as it relates to structured data and to apply these principles using Structured Query Language (SQL).
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This course is designed to build upon the knowledge and skill acquired in Data Management I. Here, learners apply data management principles and practices as it relates to unstructured data using a NoSQL database management (DB) program such as MongoDB.
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A Business Case captures in detail the business drivers, costs, benefits, and economic justification for the investment and resources needed to implement a proposed solution. The purpose of this course is to provide learners with the skills and tools to analyze, build, and present a business case to justify the building and deployment of a solution. Course concepts and content are applied through the use of real-world case studies.
This course provides learners with the knowledge to help facilitate and support data-driven decision making. They learn the impact of data analytics integration into key business processes and how the data findings influence business decisions. They focus on commonly used analytical techniques for application in industry environments.
This course provides students with knowledge of data visualization and storytelling principles and practices. Students learn the techniques used to create a powerful data narrative as well as those to create clear and impactful visualizations to support their data story.
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This course provides students with the foundational knowledge and skills using Power BI to perform data modelling and create interactive insights.
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This course builds upon the knowledge and skill acquired in Power BI I: Data Modelling and Visualization. Here, students learn to setup data models using Data Analysis eXpressions (DAX) and perform advanced analytics using Power BI.
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This course provides learners with the knowledge and skills to determine the business and data objectives for a given data project. They acquire knowledge of general business data analytics concepts, business and data understanding, as well as the Software Development Life Cycle (SDLC). They use this knowledge to formulate the analytics problem, produce a project proposal, and apply elements of the SDLC.
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This course provides learners with the knowledge and skills to analyze data to meet business objectives. Through theoretical knowledge and practical application, learners navigate the analytical journey, from generating the data description report through to communicating the analysis findings and recommendations.
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This course builds upon the knowledge and skills acquired in Business Analysis for Data Projects I and II, providing learners with knowledge of the operational components of machine learning models. It emphasizes the integration of data solutions and the software life cycle, highlighting the essential processes and considerations necessary to transition machine learning models from conceptual frameworks to operational software.
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This course provides learners with the knowledge and skills to assess and apply ethical and privacy standards throughout the data analytics life cycle. Learners gain insight into the ethical and social implications of the data age. Through real-world examples, they critically examine the decisions and actions of individuals, corporations, and governments related to the collection, protection, and use of consumer information and big data. They also critically examine the algorithms used to process data and automate reasoning. Learners use this insight to later plan, implement, and evaluate their own projects with ethical responsibility.
This course provides learners with foundational knowledge and skills in the programming and analytical tool “R”. It provides another method of data manipulation, calculation, analysis, and graphical display. By the end of this course, learners can build a basic R program using built-in data structures and custom functions.
This course builds upon the knowledge and skills acquired in RI: Foundations and Data Operations. Here, learners build R programs to explore and visualized data as well as apply statistical analyses.
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This course is designed to provide learners with knowledge of key machine learning concepts, principles, and practices. This knowledge is then applied in other Machine Learning courses to develop applications that learn and adapt without following specifics instructions and to analyze the patterns of data drawn from the machine learning models.
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This course builds upon the knowledge acquired in Machine Learning I. Here, learners build and implement supervised machine learning models using regression models to classify data or predict outcomes. They utilize Python, R, and/or cloud-based tools to build and run the models.
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In this course, learners continue to build their machine learning skills. They build and implement supervised and unsupervised machine learning models using classification and clustering models to classify data or predict outcomes. They utilize Python, R, and/or cloud-based tools to build and run the models.
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This course provides learners with the foundational knowledge and skills to build a basic python application using built-in data structures and user-defined functions. They learn key programming concepts as well as problem-solving, code debugging, and basic software development practices.
This course builds upon the knowledge and skills acquired in Python I. Learners acquire knowledge of key object-oriented programming concepts and apply these concepts to manage data. They also learn to install and manage third-party packages and implement file input/output (I/O) operations with the intention of applying data acquisition techniques.
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This course builds upon the knowledge and skills acquired in Python II and focuses on using Python packages for data preprocessing and statistical analysis. The preprocessing packages may include those such as Pandas and NumPy. The analysis packages may include those such as Scikit Learn and Statsmodel. Learners also continue to reinforce their statistical skills by applying statistical techniques to analyze data.
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A safe and healthy workplace is the responsibility of the employer and the employee. This course introduces students to the importance of working safely and addresses how employers and employees can control the hazards and risks associated with the workplace. Students will also learn about the roles and responsibilities of key stakeholders including WorkSafeNB, the employer and the employee in ensuring workplaces are safe.
This course introduces students to key statistical concepts as it relates to data science. It focuses on areas such as descriptive statistics, sampling distribution, and plotting techniques for data analysis.
This course builds upon the knowledge and skills acquired in Statistics I to focus on areas such as hypothesis testing, regression analysis, linear regression, and nonparametric testing. Learners apply standard statistical techniques for the purpose of analysis, visualization, and interpretation of data.
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This course provides learners with the foundational knowledge of cloud concepts, security, and architecture. This knowledge is applied in future program courses for analytics purposes.
This course is designed to provide students with the skills to implement a machine learning (ML) model in a cloud-based environment and using cloud-based tools. More in-depth knowledge and skills of machine learning concepts and models are taught and applied in the Machine Learning courses.
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This Capstone course represents the culmination and integration of students’ learning across the Business and Advanced Analytics program. Here, learners apply the full spectrum of their learning to a new, real-world situation. They solve a business problem/need using supervised or unsupervised machine learning models and data analytics tools. As part of this self-directed application, they submit a project proposal to their instructor for review and approval. If/as approved, they carry out the complete analytics workflow/cycle and the business analytics components related to it. They analyze, visualize, and present their business solutions to their instructor, peers, and/or key stakeholders.
11201 - Professional occupations in business management consulting
21211 - Data scientists
21221 - Business systems specialists
Disclaimer: This web copy provides guidance to prospective students, applicants, current students, faculty and staff. Although advice is readily available on request, the responsibility for program selection ultimately rests with the student. Programs, admission requirements and other related information is subject to change.