Lifelong Learning for a Brighter World

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Big Data Analytics

Master digital data for meaningful insights.

Big Data. Bigger Career.

Course Descriptions

Students active in the program before September 1, 2020 may complete the Big Data Certificate requirements by August 31, 2021. To qualify for the Certificate in Big Data Analytics, students must complete 6 courses listed (5 core courses + capstone project course = 18 units). Please refer to course equivalency table below to select the correct courses from the schedule to complete your Certificate. If you have any questions, please contact us

Data Analytics Course Equivalency 

Former Course ​# New Course # Course Name
 BDA 101  DAT 201  Data Analytics & Modelling
 BDA 102  DAT 301  Machine Learning for Big Data Analytics
 BDA 103  DAT 202  Data Management
 BDA 104  DAT 203  Predictive Modelling & Data Mining
 BDA 105  DAT 302  Data Programming I
 BDA 106  DAT 205  Data Science Capstone
 BDA 200  DAT 100  Foundations of Computer Programming 
 BDA 201  DAT 101  Stats for Data Analytics
 BDA 202  DAT 102  Working with Databases
 BDA 203  DAT 103  Business Intelligence & Data Analytics
 BDA 204  DAT 104  Data Analysis and Visualization
 BDA 205  DAT 200  Statistical Analysis for Data Science
 BDA 206  DAT 204  Data Analytics Tools
 BDA 207  DAT 105  Artificial Intelligence (AI) for Business: An Introduction

Core Courses (required; 15 units)

  1. Data Analytics & Modelling
  2. Big Data Analytics
  3. Data Management
  4. Predictive Modelling & Data Mining
  5. Big Data Programming

Capstone Course (required; 3 units)

  1. Big Data Analytics Capstone Project Course

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.

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.

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