The document outlines a certificate program in data analytics offered by Ryerson University, focusing on big data and predictive analytics through IBM Watson tools. The program includes core courses and lab sessions that train students in data analysis, visualization, and natural language processing, culminating in a group project. Over its history, the program has engaged hundreds of students, demonstrating the effectiveness of IBM Watson with various performance metrics in project deliverables.

1. IBM Watson Classroom Experience
Dr.Ayse Bener, Ryerson University, Toronto, Canada

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%

12. Watson Generated
Recommendations
User and
Context
related
Information
Natural
Language
Processing
Recommender
System
Analytic Intent
detector
Dataset
Analyzer
User Specific Information
User-System Interaction
Situation
Historical User Actions
Historical User Preferences
Personalized Recommendations
Situation-based Recommendations
Recommendations based on user
preferences profile
Suggestions for Improvement
Context-Aware Recommender System for IBM Watson Analytics

13. http://www.datasciencelab.ca