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Big Data Programming & Architecture

Uncover the Hidden Value of Big Data

Bridging theory and practical experience

Course Descriptions

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To qualify for the Certificate in Big Data Programming and Architecture, students must complete at least five (5) elective courses from the following Big Data Programming and Architecture stream = 15 academic units. Students have 3 years complete the Certificate.

DAT 202 is recommended for students who have limited background and work experience in data management. Students should enrol in the courses in the listed order. DAT 304 is recommended to students interested in pursuing designation such as the Certified Cloud Practitioner, and Cloud Solutions Architect.​

Students in Big Data Programming and Architecture wishing to take multiple courses in one term should select courses from the recommended course group for each term:

  • First term elective courses: DAT 202, DAT 301
  • Second term elective courses: DAT 302, DAT 303
  • Third term elective courses: DAT 304, DAT 305

DAT 202 Data Management (3 Units)

Data analytics problems require new tools/technologies to store and manage the data to realize the business benefit. This course explores the importance of managing data as an enterprise asset and the data management components required in terms of the acquisition, storage, sharing, validation and accessibility of data for addressing business problems. An examination of Database Management Systems, database architectures, the differences between OLTP (Online transaction processing) OLAP (online analytical processing), and the administrative processes that guide the data lifecycle will be a focus of the course.

Prerequisite: Introductory statistics course, or DAT 101 Statistics for Data Analysis, or DAT 200 Statistical Analysis for Data Science

DAT 301 Machine Learning for Big Data Analytics (3 Units)

Building on the fundamental principles of data analytics, this course advances to modern machine learning techniques such as neural network, deep learning, and reinforcement learning as well as NLP and text analysis. Application activities will be structured to provide an introductory level of how machine learning techniques are applied to big data analytics. Learners should have a strong level of data analytics for this course. DAT 203 Predictive Modelling and Data Mining is recommended prior to registering in this course.

Prerequisite: Intermediate or advanced statistics course, DAT 200 Statistical Analysis for Data Science, or DAT 201 Data Analytics & Modelling.

DAT 302 Data Programming I (3 Units)

This course examines developing solutions for extracting and analyzing big data sets using various technologies. Students will learn Scala and Java, which are the fundamental part of Spark, Kafka, and HBase. The focus will be on Apache Spark and its different aspects. Students will explore real-time analytics tools such as Kafka and HBase. NoSQL will be covered in this course. A laptop computer with Minimum 8 GB RAM dedicated on your 64-bit OS (16 GB RAM is strongly recommended for DAT 302), Core i5 CPU, 500 GB storage is required.

Prerequisite: Intermediate level of statistics, data analytics, and computer programming

DAT 303 Data Programming II (3 Units)

The course will begin with an exploration of MongoDB, which is a document database with scalability and flexibility for queries and indexing. Students will progress to the ELK stack - a technology stack used for logging with different components, such as Elasticsearch, Logstash, and Kibana. Elastic search is a NoSQL database that stores data as JSON documents, and it can be used to search large data sets. Kibana is an open-source analytics tool that can be used with Elasticsearch for visualizations. Logstash will be covered as a log management tool. Students also learn how to implement real-time scenarios. A review of different Cloud providers will also be covered. A laptop computer with Minimum 8 GB RAM dedicated on your 64-bit OS (16 GB RAM is strongly recommended for DAT 303), Core i5 CPU, 500 GB storage is required.

Prerequisite: Intermediate level of statistics, data analytics, and computer programming

DAT 304 Essentials of Cloud Computing (3 Units)

Explore the principles and practices of cloud computing with this introductory course. Students will discover the importance of cloud computing for today’s business and IT sectors through an examination of the development of cloud technologies over time. Common practices for delivery, deployment, architecture, and security will be presented. Students will explore various cloud computing platforms to understand and assess current service options and to discuss future developments for cloud computing.

DAT 305 Capstone Project - Big Data Programming and Architecture (3 Units)

This course provides students with a real-world business problem/project in order to apply analytics models, methodologies and tools learned in the program. Faculty mentors will work with students to ensure the capstone project reflects, and encompasses, best practices for project management and data programming and architecting.

Prerequisite: Students should plan to complete this course in the final term of their studies.