BDA 101 Data Analytics and Modelling (3 Units)
This course offers an introduction to data science and machine learning paving the way for students to learn big data principles. In particular, this course begins with a brief history of data science, followed by regression analysis, regression and classification trees, and ends with introductions to K-means clustering, principal component analysis (PCA). Each lecture has associated with it a practical lab session which students will put "theory into practice" offering students a hands-on approach to learning the material. *Revised course description pending approval
Prerequisite: Admission to program
Instructors: Mian Shah
, Owais Hashmi
, Omotayo Akinbode
, Quang Nguyen-Luong
BDA 102 Big Data Analytics (3 Units)
Building on the fundamental principles of data analytics, the course content progresses to identifying and using common analytics tools to process big data. Students will work on the identification of model structure, the processes to run, evaluate and calibrate model and data structures using applicable industry standards and software tools.
Prerequisite: BDA 101. BDA 104 is recommended before, or concurrently for BDA 102.
Instructors: Karim Souidi
, Omotayo Akinbode
BDA 103 Data Management (3 Units)
Big Data 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 term 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: BDA 101
Instructors: Dario Medina
, Guido DiCesare
, Eleanor Smith
BDA 104 Predictive Modelling and Data Mining (3 Units)
The course will introduce predictive modelling techniques as well as related statistical and visualization tools for data mining. The course will cover common machine learning techniques that are focused on predictive outcomes. Students will learn how to evaluate the performance of the prediction models and how to improve them through time.
Prerequisite: BDA 101. BDA 104 is recommended before, or concurrently for BDA 102
Instructors: Haitham Amar
BDA 105 Big Data Programming (3 Units)
Developing solutions for extracting and analysing big data sets using Hadoop and Spark is the focus of the course. Students will build upon the knowledge and skills of earlier program courses to analyze large-scale network data and to problem solve potential solutions. A laptop computer with Minimum 8 GB RAM dedicated on your 64 bit OS (16 GB RAM is strongly recommended for BDA 105), Core i5 CPU, 500 GB storage is required.
Prerequisite: BDA 101, BDA 102
Instructors: Pedram Habibi
BDA 106 Big Data Analytics Capstone Course (3 Units)
The 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 big data analytics and project management.
Prerequisite: BDA 103, BDA 104, or permission of Program Manager. May be taken concurrently with BDA 105
Instructors: Jim Green
, Guido DiCesare