2. Certificate Program
Course Objectives
IBM Watson Analytics Lab
IBM Watson Lab and course project
Summary
Outline
3. Certificate Program
Certificate in Data Analytics, Big Data, and Predictive
Analytics
Chang School of continuing education , Ryerson University
4. Offered courses at Certificate Program
Industrial Engineering
Data Organization for Data Analysis (core)
Introduction to Big Data (core)
Data Analytics: Basic Methods (core)
Big Data Analytics Tools (core)
Data Analytics: Capstone Course (core)
Computer Science
Data Access and Management (optional elective)
Mathematics
Data Analytics: Advanced Methods (core)
5. History
Program started on Fall 2014
Introduction to big data course was offered in all
semesters
9 semesters
On average 50 students enrolled on this course each
semester (4 terms/ year)
100+ students enrolled in the current semester (Fall 2016)
500+ students exposed to IBM Watson products
6. Course Objectives
Give students overview of big data
State of the art practice in analytics
The role of the data scientist
Big data analytics in industry verticals
Analytics lifecycle as an end-to-end process
Focuses on key roles for a successful analytic project,
Main phases of the lifecycle
Developing core deliverables for stakeholders
Team work skills
Problem solving skills
7. IBM Watson Analytics
The system is used in two lab sessions
First session while introducing software tools to analyze data
Datasets and step by step instructions are provided for students to
interact with the system and explore the datasets
Last session while introducing visualization of data
Visualization techniques described in the lecture are tested on the
provided experiment
8. IBM Watson
Lecture on Natural Language Processing
Introduction to natural language processing
Basic text processing
Cognitive Computing
Question answering systems
Lab Session using IBM Watson
Each team of 3-4 students upload a predefined document, train
the system by adding question-answer pairs and test after the
corpus is created
This lab is considered as preparation for course project
9. IBM Watson
Course project
Group project with the team of 4-5 students
One specific topic is selected as the project
Food and Nutrition
Canadian Education Information
Canadian Tourism Attractions
Crisis Management
Etc.
Sub-topics are selected by each group
Foodbanks in Canada
Nutrition in Beverages
Nutrition in solid food
Etc.
10. IBM Watson
Each group prepares the documents based on the
instructions
Conference paper on how to train IBM Watson*
Students train the system by providing question-Answer
pairs
Corpus is then created
Testing phase and calibration
Project report and presentation
* Murtaza, Syed Shariyar, Paris Lak, Ayse Bener, and Armen Pischdotchian. "How to effectively train IBM Watson: Classroom
experience." In 2016 49th Hawaii International Conference on System Sciences (HICSS), pp. 1663-1670. IEEE, 2016.
11. Summary
10+ projects are defined
Each group prepares 40+ documents
Each group assigned 400+ Q-A pairs for their specific
subtopics
Average accuracy provided by the system during the past
semesters is 75%, recall is 100% and precision is 65%