-Grade 12 or equivalent
- Mature student status (18 years of age or older) and a passing score on the entrance examination

Benefits of this program

-Prepares you for valuable industry certifications
- Market-driven programs
- Growing demand for professionals in this area

Employment Opportunities

-Data Scientist
- Statistician/Statistics Officer
- Big Data Developer
- Statistician/Statistics Officer

Program Objective

Data is becoming increasingly large, complex, and unstructured, becoming “big data”. There is ample opportunity to better realize and unlock its value. As the Internet, mobile technology, and user habits and demands continue to evolve, resulting in the mass accumulation of data, more businesses are increasingly accepting and using this data in all forms and sizes as part of their critical decision-making processes.

This program will provide students with sufficient knowledge and skills in the areas of big data analysis, analytics, machine learning, and other related technologies, so that they may competently apply them in the field of data science.

Required Skills and Personal Attributes

  • Strong technical background in computing and data work
  • Excellent analytical and technical problem solving skills
  • Excellent English communication skills (written and oral)
  • Good knowledge of database architectures
  • Collaborative team player with a positive self-motivated can-do attitude
  • Self-motivated
  • Detail-oriented
  • Ability to effectively manage time and stress

Method of Delivery

Combination of:

  • Integrated Learning System™ training facilitated by Academy of Learning Career College facilitators
  • Instructor-led classroom learning

Program fees include student manuals and all other course materials. Financial assistance may be available to those who qualify. Graduation requirements: In order to obtain a diploma, students must obtain an overall average of 75% or better, with no final course mark below 60%.

Duties and Responsibilities

In this area, duties and responsibilities may include, but are not limited to the following:

  • Perform statistical analyses and develop algorithms to be used in automated analysis
  • Evaluating state-of-the-art statistical modelling and machine learning approaches using large amounts of historical data
  • Design artificial neural networks that extract patterns from large-scale sequencing databases
  • Perform data analysis, visualization, and modeling with large datasets
  • Perform data acquisition, cleaning, and transformation
  • Take analytical objectives and define data requirements
  • Work with unstructured data from social media, video feeds, audio, or other sources to extract, clean, and transform customer and item-level statistical data for purposes analysis, modelling/segmentation, and reporting
  • Implement advanced machine learning techniques and statistical and econometric models to pricing, assortment, and marketing mix
  • Interpret, document, and present/communicate analytical results to multiple business disciplines, providing conclusions, and recommendations based on customercentric data
  • Identify, develop, and make recommendations for process improvements and best practices

Competencies upon Completion

  • Identify, gather, and implement requirements
  • Use modelling tools and develop enhanced models
  • Develop and document models
  • Develop functional, business, and system interface or capability interaction
  • Gather and analyze information to establish the technical needs of a system or project
  • Develop and maintain functional standards for the functional framework
  • Document and develop forms, manuals, programs, data files, and procedures
  • Provide advice, analysis, configuration, implementation, and problem resolution for Hadoop
  • Create Attribute, Analytic, and Calculation views and use them in various reports
  • Develop complex business reporting and analytics models
  • Be familiar with methodologies used in business
  • Communicate and take leadership
  • Good understanding of most popular data science tools used in Canada
  • Efficient usage of modelling and analytics tools
  • In-depth knowledge of big data management and data modelling
  • Reporting and documentation skills
  • Proficiency with programming languages including SQL, Python, and R
  • Ability to do automation through artificial intelligence
  • Understand and work with major big data tools in Canada


Department Contact

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