Big Data Analytics

BDM-1003: Big Data Tools

This course overviews different big data tools such as Hadoop,Spark, HBase, Phoenix, Cassandra, MongoDB, Sqoop, Flume, Kafka, Oozie, etc. Studentswill have a detailedunderstanding of how various big data tools are used to solve different businessproblems. They 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. They programmatically extractdata from varioussources and file formats. Pythonand the Pandasenvironment are utilized for the development.

BDM-1213: Data Encoding Principles and Collection Methods

This course provides hands-on experience in data management and data encoding. Students solve real-world problems to examine the key issues impacting the data management function and costs. They discuss the issues impacting legal, regulatory, privacy, recordkeeping, information accessibility, knowledge management, governance, quality, and accountability for data storage and data repositories. Students will compare and apply technologies, standards, and approaches used to encode, encrypt, secure, migrate, and synchronize information between different systems and file formats. Using Python, they will implement encoding and encryption techniques such as one-hot encoding, binary encoding, AES encryption, DES encryption, etc.

BDM-1024: Data Technology Solutions

This course introduces the core concepts of Big Data Technology Solutions with hands-on practices in business process modeling, data analysis, and data modeling. Students install, configure, administer, and optimize a Hadoop ecosystem. They overview technologies such as SAS, R, Python, Gephi, Tableau, Simba, AWS Lambda, Hadoop, Spark, Azure, and SAP. Students will study build-versus-buy considerations, application life-cycle management, design patterns, etc. They will be able to choose specialized technology solutions to support deep data analytics and optimize the big data ecosystem by reducing data movement and applying performance-tuning measures to a Hadoop parallel-processing environment.

BDM-1043: Big Data Fundamentals

This course provides a fundamental overview of big data and existing big data frameworks. Students use a data-driven approach to leverage the features of big data to improve a company's business components, such as business processes, operational models, and business models, to handle tasks from sales to marketing, operations, supply chain, human resources, and finances. They understand how big data is used in different industries, for example, healthcare, financial institutions, pharmaceutical companies, manufacturing, and retail. Students will understand the impact of key big data features variety, velocity, and volume by analyzing the best practices for big data and corporate data governance. Studentswill also exploredifferent roles, the use of artificial intelligence and robotics, security and privacy policy, and informatics in a big data environment. Students will have hands-on expertise in Hadoop, MapReduce, Yarn, Apache Hive, and Pig.

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-1113: NoSQL Database

This course will provide the students with the core concepts of NoSQL databases. It explores the four types of NoSQL databases, i.e., Document-oriented, Key-Value Pair based, Column-oriented, and Graph-based databases. Students will explore different ways to search, create, and analyze data using the MongoDB NoSQL database. They will have hands-on experience of learning MongoDB design basics, including navigating, computing, and querying the database by solving practical problems that Canadian businesses and industries have.

BDM-1034: Application Design for Big Data

In this course, students use statistical models such as linear, nonlinear, Naive Bayes, decision trees, deep learning, etc., to solve real-life business problems. They will use various algorithms, data structures, and applications to handledifferent file formats,Big Data, etc.,for full life-cycle data-driven development. Studentswill have a clear understanding of how performance estimation is done. They solve real-life problems using different methods such as indexing, parallel processing, waiting-line-queuing, time series analysis, and discrete-event simulation. MongoDB, CouchDB, HBase, Hive, Pig, Sqoop, ZooKeeper, Maven and SBT are used to develop Big Data models.Students also use a high-performance machine learning framework: MAHOUT.

BDM-3014: Introduction to Artificial Intelligence

This course presents the basics of artificial intelligence (AI) by exploring AI's core concepts, related fields, history, and practical applications. Students will solve real-life problems by implementing various AI algorithms. They will compare the AI approaches to optimize the problem-solving process 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 using supervised classification techniques based on artificial neural networks, regression, unsupervised learning (clustering), etc. They will use PyTorch and TensorFlow to develop different AI-based solution models.

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

This course focuses on the algorithms used for big data processing and the statistical models used for data analytics. Students will use various algorithms for dimensionality reduction, data mining, big data analytics, and developing probability-driven models. They will use regression models, descriptive and inferential statistics, support vector machines, Bayesian networks, decision trees, k-means clustering, artificial neural networks, natural language processing, etc., to solve real-life problems. Students will optimize the problem-solving process by blending combinations of algorithms and models. Furthermore, they will use different visualization techniques to visualize the output of a model. Students will develop the analytic models, statistical models, and visualizations using R and Python.

BDM-2203: Big Data Visualization for Business Communication

In this course, students deepen their understanding of best practices and tools for presenting data analyses aligned with business needs. They are introduced to the basics and principles of data visualization and will analyze quantitative and qualitative data to create meaningful visualizations, promoting organizational decision-making. Students will learn how to present the insights of their analysis to the target audience by creating visualizations incorporating the diverse viewpoints of the company stakeholders. They will learn to acquire, parse, and analyze large datasets and assess rhetoric presentation approaches, executive presence techniques, metrics, and strategic change practices to communicate better and motivate business stakeholders to act as a group. Students will use the highly demanding visualization tools Tableau, Power BI, and AWS to create meaningful and interactive visualizations.

JSS-1001: Job Search and Success

This course is designed to give the student an understanding of how to conduct a job search and how to succeed in the work place. This includes self-reflection, effectively designing a cover letter and resume, online job searches utilizing social media, behavioural based interviewing as well as marketing oneself effectively in a job interview. Job safety, successful work strategies and harassment and discrimination plan of action is also discussed.

Academic Break

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.

BDM-3603: Big Data Network

This course provides the students with practical Apache Spark and Apache Kafka skills. Students will learn how to use Apache Spark for Data Engineering and Machine Learning applications. They will have real-world experience with Spark MLlib and Spark Structured Streaming. They will also learn about Resilient Distributed Datasets, or RDDs, that enable parallel processing across the nodes of a Spark cluster. The course will also help the students to get started with the fundamental Kafka operations. Students will use the Kafka streaming platform to learn how to handle data in motion. They will be able to build their own Kafka producers and consumers.

AML-3204: Social Media Analytics

This course introduces the core concepts of social media analytics. The theory part includes an introduction to social media data, the foundations of collecting and storing social media data and how to use AI and ML tools to analyze social media data. The laboratory portion provides students with hands-on practices and the opportunity to build, train and apply models that analyze social media data and generate valuable social, marketing, and business insights.

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: Work Term - Full-Time*

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/

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CPL-5559: WIL Project

Work Integrated Learning (WIL) Project is aimed at enriching students by connecting different program areas of study, cutting across subject-matter lines, and emphasizing unifying concepts. The focus of the WIL Project is to make connections between study and industry by engaging students in relevant and meaningful activities that are connected to and practiced within the professional workplace. WIL Project allows students to enhance and strengthen their employability prospects post-graduation by fine tuning skills and knowledge and meeting the expectations of 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.