DAT 201 Data Analytics & Modelling - Formerly BDA 101 (3 Units)
This course offers an introduction to data science and machine learning paving the way for students to learn data analytics principles. In particular, this course begins with a brief history of data analytics and 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 in which students will put "theory into practice" offering students a hands-on approach to learning the material.
Prerequisite: Introductory statistics course, or DAT 101 Statistics for Data Analysis
DAT 202 Data Management - Formerly BDA 103 (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 203 Predictive Modelling and Data Mining - Formerly BDA 104 (3 Units)
The course will introduce predictive modeling 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: Introductory statistics course, or DAT 101 Statistics for Data Analysis.
Instructors: Haitham Amar
, Karim Souidi
, Mohammad Esmalifalak
DAT 205 Data Science Capstone Project - Formerly BDA 106 (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: Students should plan to complete this course in the final term of their studies.
Instructors: Jim Green
DAT 301 Machine Learning for Big Data Analytics - Formerly BDA 102 (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 - Formerly BDA 105 (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
Instructors: Pedram Habibi