Certificate in Advanced Data Science and Predictive Analytics

This course provides an introduction to data analytics and how it enables and drives value creation for organizations across industry sectors. A high-level overview of the end-to-end data and analytics solution process is provided. Various data sources will be discussed and a framework to turn raw data into value will be presented. In this course students will carry out data analyses using various methods.

4 Classes

36 Hours

Schedule

May 25 to July 19, 2026

Classes Bi-Weekly Wed 6:45 PM-9:45 PM (03 Jun 2026 to 15 Jul 2026); Wed 6:45 PM-9:45 PM (27 May 2026); Thu 6:45 PM-9:45 PM (02 Jul 2026)

Students will learn to access common data provisioning technologies to support their analytics requirements. The course presents classic data approaches rooted in variations of data warehouses and data marts. Extensions to these concepts that support big data processing are then introduced. Students will learn why data storage technologies have evolved to support big data analytic workloads. Students will learn to access relational databases using SQL and NoSQL document and graph databases using vendor specific query languages. Data governance concepts are introduced to help students understand the concepts of “data assets” and “data lifecycle management.” Metadata will be described in the course from both conceptual and practical perspectives.

4 Classes

36 Hours

Schedule

September 14 to November 08, 2026

Classes Bi-Weekly Wed 6:45 PM-9:45 PM (23 Sep 2026 to 04 Nov 2026); Wed 6:45 PM-9:45 PM (16 Sep 2026)

As organizations gather more and more data from their business processes, traditional computer systems and tools cannot handle the massive volume of data. Distributive computing platforms and tools are needed to digest, store and retrieve data. This course looks at distributive tools for data analysis, machine learning, data digestion and visualization. These tools are employed in real world examples to find trends and make informed business decisions.

4 Classes

36 Hours

Schedule

May 24 to July 18, 2027

Classes Bi-Weekly Wed 6:45 PM-9:45 PM (26 May 2027 to 07 Jul 2027)

Exercise the skills acquired in the previous courses as you work end-to-end on an analytics project. Students have an opportunity to select to either work: a) with a business on a sponsored project, or b) independently on their own project. This hands-on course requires defining the business problem, scoping out the project, review and preparing of collected data, as well as integrating, modelling and analyzing the data. Students are expected to also derive insights and offer recommendations based on their analysis.

4 Classes

36 Hours

Schedule

September 13 to November 07, 2027

Classes Bi-Weekly Wed 6:45 PM-9:45 PM (15 Sep 2027 to 27 Oct 2027)

You will learn the building blocks and tools that will empower you to take raw data sets and extract valuable insights and data visualizations that bring the data to life. You will be introduced to R programming and learn to manipulate data using various R libraries while working in RStudio and Weka. You will also be introduced to common algorithms and techniques for analyzing data and be introduced to the concepts, practices and tools that are used to implement machine learning. The course is designed to be practical and allow students to quickly apply their new skills to real world problems, with real datasets and using the CRISP-DM methodology.

4 Classes

36 Hours

Schedule

January 17 to March 13, 2026

Classes Sat,Sun 10:00 AM-5:00 PM (17 Jan 2026 to 18 Jan 2026); Fri 10:00 AM-5:00 PM (13 Mar 2026)

Having an understanding of the concepts of machine learning, you will examine and apply the fundamentals of working with extremely large data sets. First, you will parallelize and perform MapReduce operations using Hadoop and Spark. Second, you will take a look at the fundamentals of deep learning and working with TensorFlow and Keras. You will focus on image and voice data and build deep neural network models. At the end of this module, you should be ready to take on a large-scale real-world project challenge. * successful completion of ML1010 is required to begin ML1020.

4 Classes

36 Hours

Schedule

May 09 to July 03, 2026

Classes Sat,Sun 10:00 AM-5:00 PM (09 May 2026 to 10 May 2026); Fri 10:00 AM-5:00 PM (03 Jul 2026)

You will learn the building blocks and tools that will empower you to take raw data sets and extract valuable insights and data visualizations that bring the data to life. You will be introduced to R programming and learn to manipulate data using various R libraries while working in RStudio and Weka. You will also be introduced to common algorithms and techniques for analyzing data and be introduced to the concepts, practices and tools that are used to implement machine learning. The course is designed to be practical and allow students to quickly apply their new skills to real world problems, with real datasets and using the CRISP-DM methodology.

3 Classes

36 Hours

Schedule

September 19 to November 13, 2026

Classes Sat,Sun 10:00 AM-5:00 PM (19 Sep 2026 to 20 Sep 2026); Fri 10:00 AM-5:00 PM (13 Nov 2026)

Having an understanding of the concepts of machine learning, you will examine and apply the fundamentals of working with extremely large data sets. First, you will parallelize and perform MapReduce operations using Hadoop and Spark. Second, you will take a look at the fundamentals of deep learning and working with TensorFlow and Keras. You will focus on image and voice data and build deep neural network models. At the end of this module, you should be ready to take on a large-scale real-world project challenge. * successful completion of ML1010 is required to begin ML1020.

3 Classes

36 Hours

Schedule

January 23 to March 19, 2027

Classes Sat,Sun 10:00 AM-5:00 PM (23 Jan 2027 to 24 Jan 2027); Fri 10:00 AM-5:00 PM (19 Mar 2027)

You will learn the building blocks and tools that will empower you to take raw data sets and extract valuable insights and data visualizations that bring the data to life. You will be introduced to R programming and learn to manipulate data using various R libraries while working in RStudio and Weka. You will also be introduced to common algorithms and techniques for analyzing data and be introduced to the concepts, practices and tools that are used to implement machine learning. The course is designed to be practical and allow students to quickly apply their new skills to real world problems, with real datasets and using the CRISP-DM methodology.

3 Classes

36 Hours

Schedule

January 16 to March 12, 2027

Classes Sat,Sun 10:00 AM-5:00 PM (16 Jan 2027 to 17 Jan 2027); Fri 10:00 AM-5:00 PM (12 Mar 2027)

Having an understanding of the concepts of machine learning, you will examine and apply the fundamentals of working with extremely large data sets. First, you will parallelize and perform MapReduce operations using Hadoop and Spark. Second, you will take a look at the fundamentals of deep learning and working with TensorFlow and Keras. You will focus on image and voice data and build deep neural network models. At the end of this module, you should be ready to take on a large-scale real-world project challenge. * successful completion of ML1010 is required to begin ML1020.

3 Classes

36 Hours

Schedule

May 08 to July 09, 2027

Classes Sat,Sun 10:00 AM-5:00 PM (08 May 2027 to 09 May 2027); Fri 10:00 AM-5:00 PM (09 Jul 2027)

As organizations gather more and more data from their business processes, traditional computer systems and tools cannot handle the massive volume of data. Distributive computing platforms and tools are needed to digest, store and retrieve data. This course looks at distributive tools for data analysis, machine learning, data digestion and visualization. These tools are employed in real world examples to find trends and make informed business decisions.

4 Classes

36 Hours

Schedule

September 21 to November 22, 2026

Classes Sat 9:30 AM-12:30 PM (26 Sep 2026); Bi-Weekly Sat 9:30 AM-12:30 PM (17 Oct 2026 to 14 Nov 2026)

Exercise the skills acquired in the previous courses as you work end-to-end on an analytics project. Students have an opportunity to select to either work: a) with a business on a sponsored project, or b) independently on their own project. This hands-on course requires defining the business problem, scoping out the project, review and preparing of collected data, as well as integrating, modelling and analyzing the data. Students are expected to also derive insights and offer recommendations based on their analysis.

4 Classes

36 Hours

Schedule

February 01 to March 28, 2027

Classes Bi-Weekly Sat 9:30 AM-12:30 PM (06 Feb 2027 to 20 Mar 2027)

As organizations gather more and more data from their business processes, traditional computer systems and tools cannot handle the massive volume of data. Distributive computing platforms and tools are needed to digest, store and retrieve data. This course looks at distributive tools for data analysis, machine learning, data digestion and visualization. These tools are employed in real world examples to find trends and make informed business decisions.

4 Classes

36 Hours

Schedule

May 24 to July 18, 2027

Classes Bi-Weekly Wed 6:45 PM-9:45 PM (26 May 2027 to 07 Jul 2027)

Exercise the skills acquired in the previous courses as you work end-to-end on an analytics project. Students have an opportunity to select to either work: a) with a business on a sponsored project, or b) independently on their own project. This hands-on course requires defining the business problem, scoping out the project, review and preparing of collected data, as well as integrating, modelling and analyzing the data. Students are expected to also derive insights and offer recommendations based on their analysis.

4 Classes

36 Hours

Schedule

September 13 to November 07, 2027

Classes Bi-Weekly Wed 6:45 PM-9:45 PM (15 Sep 2027 to 27 Oct 2027)