Program Information

Developing new ways to interpret large amounts of information collected through web sites, transactions, records, and images can help provide solutions to many business, social, society, and technological challenges of today. Big data enables users to make informed decisions and better predict future outcomes. 

The Lambton College Big Data Analytics, Ontario College Graduate Certificate is focused on utilizing big data technology for unstructured data to help guide executive, management, and industry decision-making.

The innovative curriculum will focus on topics such as information administration, development, project management, and business communications. In combination with Business Intelligence tools, data analytics, and cloud computing, students will blend theoretical knowledge with hands-on skills to learn how to capture, collect, curate, search, analyze and store complex data sets that are utilized by organizations to guide business decisions. The final semester will enhance all learning outcomes through a co-operative education work term or applied project.

In addition to a full range of enhanced virtualization deployment skills, data analytics, cloud computing theory, project management and business communications students will apply these skills to a variety of cutting-edge open-source and vendor-specific virtualization solutions. 

Fall 2021 Delivery Format

For full details about the fall delivery format of this program, please visit the Academic Delivery Approach page. 

This Lambton College program is licensed to and delivered by Queen's College (PDF), a licensed private career college in Mississauga, Ontario. Students that are registered at Lambton in Mississauga are students of a public college and as such, will receive full credit from Lambton College for all Lambton College courses completed at the Queen's College campus in Mississauga. Students who meet program graduation requirements will graduate with a credential from Lambton College.

See Course List

Admission Requirements

A university degree in computers, mathematics, engineering, statistics or related discipline.


The admissions process is competitive and meeting the minimum academic requirements does not guarantee admission.

Lambton College reserves the right to alter information including admission requirements and to cancel at any time a program or course; to change the location and/or term in which a program or course is offered; to change the program curriculum as necessary to meet current competencies or changes in the job market; to change the pathways third-party certification bodies; or to withdraw an offer of admission both prior to and after its acceptance by an applicant or student because of insufficient applications or registrations or over-acceptance of offers of admission. In the event Lambton College exercises such a right, Lambton College’s sole liability will be the return of monies paid by the applicant or student to Lambton College.

English Language Requirements

Applicants must demonstrate language proficiency by submitting one of the following scores:

  • IELTS of 6.5
    - or -
    IELTS of 6.0 + Completion of EAP-3106 (English for Academic Purposes) during first term of study.
  • TOEFL iBT 79
    -or-
    TOEFL 70 + Completion of EAP-3106 (English for Academic Purposes) during first term of study.
  • Passed Lambton Institute of English placement test 

Please Note: IELTS is the only proficiency score accepted by the Study Direct Stream (SDS) program. Additional country-specific requirements may also be applicable.

Meeting the minimum English requirements does not guarantee admission. Students with higher English proficiency scores will receive priority in the admission assessment process.

Not all students will qualify for EAP-3106 in place of the required IELTS or TOEFL test scores.


Course List

BDM-1003: Big Data Tools

Students are introduced to analyzing, parsing, splitting, modifying and identifying key correlations between large and diverse data sets. Students then apply methods and tools to efficiently query, parse, and display raw data sets. Students also apply cleansing methods and pre-processing tasks to prepare data for analytics. Students programmatically extract data from various sources and file formats. Python and the Pandas environment are utilized for development.

BDM-1213: Data Encoding Principles and Collection Methods

Students are introduced to the tasks, issues and problems with data management in modern organizations and businesses. They examine key issues impacting data management function and costs when these issues are left unchecked; issues impacting legal, regulatory, privacy, recordkeeping, information accessibility, knowledge management, governance, quality, and accountability for data storage and data repositories. They review the data management function and how data specialists, data scientists, and the associated professional roles and responsibilities fit within data management. Data Encoding Principles introduces students to the comparison and application of technologies, standards and approaches used to encode, encrypt, secure, migrate, and synchronize information between different systems and file formats.

BDM-1024: Data Technology Solutions

This course introduces the core concepts of Big Data Technology Solutions, its various aspects, suitability and application for the organizations. The theory part includes build-versus-buy considerations, application life-cycle management, design patterns and specialized technology solutions to support Big Data, Business Intelligence, business processes, decision-making, organizational needs, mobile-devices and real-time deep data analytics. The laboratory portion provides students with hands-on practices and the opportunity to apply their knowledge on business process modelling, data analysis, and data modelling to design, install, configure, administer, and optimize architecture solutions used to manage real-time scalable distributed systems. Optimization topics include reducing data movement and parallel-processing performance-tuning the Hadoop Ecosystem: algorithms, storage (reading/writing), CPU, memory and disk space. Technology overviews include IBM, SAS, R, Python, Gephi, Tableau, Simba, AWS Lambda, Hadoop, Spark, Azure, and SAP.

BDM-1043: Big Data Fundamentals

Big Data Fundamentals introduces students to Big Data, the data-driven organization and how to leverage Big Data to gain insights to support, improve, and reinvent the enterprise to better service customers and better address data variety, velocity, and volume. Students explore Big Data best practices, corporate data governance, data practitioner roles, the data scientist and the data science workflow. Students further explore, mobile devices, sensors, artificial intelligence and robotics, security, privacy, ethics and society, and informatics. Students also explore how Big Data supports business processes, operational models, and business models in different industries from sales, to marketing, operations, supply chain, human resources, and finances. Industries reviewed range from healthcare, to financial, biology, manufacturing, and retail. Similarities and differences between Big Data solutions across different industries is explored. Students review information system history, the Big Data solution landscape and how to modernize organizations to better leverage Big Data.

AML-1214: Python Programming

This course introduces the core concepts of Python programming. The theory part includes an introduction to python and its properties, primitive data types, modules, functions, loops, and conditions. The laboratory portion is designed to provide students with the opportunity to work with a set of practical problems that Canadian businesses and industries have to resolve on a day-to-day basis.

BDM-3014: Introduction to Artificial Intelligence

Students are introduced to Artificial Intelligence (AI) 's core concepts, its related fields, history, and practical applications for AI. The theory part covers the foundations of artificial intelligence and its use in dealing with real-life problems. Students will assess AI approaches to problem-solving and decision-making utilizing large data sets, parallel processing, swarm intelligence, knowledge representation and manipulation. The laboratory portion provides students with hands-on practices and the opportunity to apply their knowledge to real-life AI challenges.

BDM-1034: Application Design for Big Data

Students are introduced to performance estimation and full-lifecycle data-driven programming for algorithms, data-structures, and application design for various file-formats, storage, analytics and Big Data technologies. Students apply methods such as indexing, parallel processing, waiting-line-queuing, time series analysis and discrete-event simulation. Students further develop, utilize, and performance-tune algorithms that use linear, nonlinear, Naive Bayes, decision-trees and deep learning approaches. Students program with SQL, NoSQL, MapReduce, R, and Python. Students also use the MAHOUT algorithm libraries.

AML-2304: Natural Language Processing

This course introduces Natural Language Processing (NLP) and its key concepts. The theory part includes the use of classic machine learning methods to solve machine translation, language modeling, and sequence tagging. The laboratory portion is designed to provide students with the opportunity to work with a set of NLP problems and the opportunity to apply their knowledge to resolve them.

AML-2203: Advanced Python AI and ML Tools

This course introduces advanced concepts of Python programming language. The theory part includes designing, implementing, and using APIs, and advanced modules for AI and ML. The laboratory portion is designed to provide students with the opportunity to work with a set of practical problems and the opportunity to apply their knowledge to real-life software application challenges.

BDM-2053: Big Data Algorithms and Statistics

Big Data Algorithms and Statistics introduces students to data set reduction, data-mining, Big Data analytics, artificial intelligence, and establishing probability-driven models. Students apply data analysis, probabilities, distributions, regression, topological analysis, optimization, descriptive and inferential statistics, support vector machines, Bayesian networks, decision trees, random forests, k-means and clustering, neural networks, deep learning, natural language processing, and others. Students also apply simulations, the data science workflow, and blend models and algorithms to better increase their overall effectiveness for machine learning, pattern recognition, and statistical modelling. Implementations are completed in python.

BDM-2203: Big Data Visualization for Business Communication

Students deepen their understanding of best practices and tools for presenting data analyses aligned with business ethics. They will learn to acquire, parse, and analyze large datasets. They will assess rhetoric presentation approaches, executive presence techniques, metrics, and strategic change practices to better communicate and motivate business stakeholders to act as a group. Students will recommend how to best lead, communicate with and influence politically driven decision-makers that are resistant to change at various points of the organization's lifecycle.

CPP-1001: Co-op Preparation

This course will provide students with employment preparatory skills specifically related to Co-operative Education. This will include understanding the Co-operative Education & Internship Policy, understanding the support system available through the Co-op and Career Services department, utilizing social media, preparing effective cover letters & resume as well as the fundamentals of behavioural based interviewing.

Academic Break

BDM-3023: Project Management for Analytics

Students are introduced to best practices, approaches, and tools for managing and delivering analytics, predictive analyses, ETL, and data projects. They will assess approaches around estimation, scoping, planning, data cleaning, data migration, data quality, and risk mitigation. Students will recommend how to best communicate their assessments to business stakeholders.

BDM-3203: Hadoop Ecosystems for Big Data

Students apply knowledge and design patterns for configuring, troubleshooting and estimating the effort for realtime multi-node / multi-clustered Hadoop implementations for physical, virtual, and cloud environments. Students install Hadoop Ecosystem technologies; and configure and test the appropriate networks, firewalls, security, and performance levels. Students will assess and address the problems around Hadoop implementations, routers, switches, and virtual LANs.

BDM-3053: Infonomics

Students apply principles and practices to determine data's value using Infonomics and its intersecting knowledge areas and disciplines: information theory, information economics, macroeconomics, intellectual capital, intangible asset valuation, asset management and Generally Accepted Accounting Principles (GAAP). Students assess information's value for projects and budgets. And also recommend approaches used for determining information's value to best prioritize and support investments, decision-making and projects.

AML-3104: Neural Networks and Deep Learning

This course introduces the core concepts of neural networks. The theory part includes an introduction to the foundations of neural networks and key parameters in a neural network's architecture. The laboratory portion provides students with hands-on practices and the opportunity to build, train and apply fully connected neural networks.

BDM-3035: Big Data Capstone Project

In the job market, employers usually look for team players with competence and expertise. They expect candidates to have experience with teamwork in project environments. This course should prepare students to work on concrete goals in a small team. They will develop an application or design to address a Big Data problem based on pre-set requirements. Students should apply the necessary project management skills to manage planning, deadlines, milestones and deliverables with a client. Appropriate documentation should supplement the design to cover the motivation, methods and test cases. Students are expected to capture reasonable market research and a business plan to cover the business aspects.

CPL-1049: Co-op Work Term

Co-operative education provides students with the opportunity to apply classroom learning to the workplace, undertake career sampling and gain valuable work experience that may assist students in leveraging employment after graduation. For further information regarding co-op, please refer to: https://www.lambtoncollege.ca/co-op_and_career/

-or-

CPL-5559: WIL Project

Work Integrated Learning Project is aimed at enriching student success by connecting different program areas of study, cutting across subject -matter lines, and emphasizing unifying concepts. The focus is on making connections, allowing students to engage in relevant, meaningful activities that are connected and practiced in real life. This will attempt to enhance and strengthen the student's employability prospects post-graduation by exposing them to skills and knowledge in demand from today's employers. Students are required to attend the scheduled shifts in the WIL office, reporting to the WIL Supervisor. Weekly real-world challenges are presented in the WIL office, designed by industry professionals. In addition to the weekly assigned deliverables, students are also offered professional development sessions, and exposed to industry guest speakers, enhancing their opportunity to develop their professional network.

*In order to be eligible to participate in a full-time Co-op Work Term (CPL-1049) students must have a GPA of 2.8 or greater.

Failing to do so will require the students to enroll in CPL-5559 WIL Project at an additional cost to the student.

See the Costs tab for current fees.

Program Maps

Current Students

Current students can view program maps from previous years on the mylambton website. 

You will need to login with your C# and password in order to access your program map.

Technology Requirements

This program requires a laptop.

Employment Opportunities

Graduates of the program may be employed in roles such as Data Analyst, Analytics Specialist, Business Analytic Specialist, Project Manager or related fields.

Career positions may include, but are not limited to: Analytics Visualization Analyst, Applications Developer, Big Data Administrator, Big Data Applications Developer, Big Data Architect, Big Data Analytics Developer, Big Data Developer (Hadoop, Sparks, R, Java, Python), Big Data Hardware Engineer, Big Data Security/Threats Analyst, Business Analyst, Business Intelligence Analyst, Business Analytics Specialist, Cloud Systems Administrator, Compliance Analyst, Database Administrator, Data Analyst, Data Hardware Engineer, Data Insights Analyst, Data Networking Analyst, Data Networking Engineer, Data Reporting Analyst, Data Scientist, Identity Access Analyst, Information Analyst, Marketing Analytics Analyst, Marketing Intelligence Analyst, Network Manager, Operations Analytics Analyst.

For more information, please contact the appropriate campus:

Toronto
416-485-2098
lambton@cestarcollege.com
Mississauga
905-890-7833 x 222
lambton@queenscollege.ca
Sarnia/Main Campus
international@lambtoncollege.ca
Back to Top