Teaching Big Data and Analytics to Undergraduate and Graduate Students

Transcription

1 Teaching Big Data and Analytics to Undergraduate and Graduate Students in Information Systems Engineering Mark Last, Lior Rokach, and Bracha Shapira Big Data and Analytics EdCon 2013, Las Vegas, Nevada November 2-3, 2013

2 About BGU Founded in 1969 by government decision Israel s fastest growing and most dynamic university Pioneering collaborative approach to get it done Ability to identify new trends Investing in excellence in both manpower and infrastructure 7

3 Students +20,000 #1 students on campuses in Beer-Sheva, Eilat and Sede Boqer choice of Israeli undergraduate students 35% of student body in advanced degree programs 50% from Center & North of the country with a growing international student body 8

4 Faculties Pinchas Sapir Faculty of Humanities and Social Sciences Faculty of Natural Sciences Faculty of Engineering Sciences Faculty of Health Sciences Guilford Glazer Faculty of Business and Management Jacob Blaustein Institutes for Desert Research 9

5 Information Systems Engineering Established in 2000 with eight faculty members Currently 18 tenure-track faculty, which makes it the largest ISE department in Israel academy Mission: to bridge the gap between theoretical Computer Science programs and managementoriented Information Systems programs Interdisciplinary: Information Systems, Information Technology, Computer Science, Software Engineering, Mathematics, Statistics and Management Science 10

6 ISE Positioning

7 Information Systems Engineering Students: 400 Undergraduate Students in Info. Sys. Eng. 320 Undergraduate Students in Software Eng. 70 Graduate Students: MSc (thesis track only) + PhD Funding and Cooperation: Attracting a significant amount of funding: 20M USD in the last five years Publications: 500 Journal and conference papers p in the last 5 years. IP: 15 Granted Patents + 40 Patent Applications in the last 5 years. 10

8 Research Domains Cyber Security Data Mining and BI Information Systems Artificial Intelligence Human-Computer Interaction Medical Informatics Information Technology Software Engineering חפשו אותנו גם כתובתנו באינטרנט: בפייסבוק:

9 Data ISE BSc. Tracks Regular AI DM & BI, Established in 2012 Cyber Security Analysis and ddesign of Information Systems MSc. Tracks Regular DM & BI, Established in 2013 Cyber Security, Established in 2012

10 Related Mandatory Undergraduate Courses CS Databases Intro to Programming Data Structures Advanced Programming Intro to AI Algorithms Computational Models Statistics Intro to Statistics and Probability Regression Hypothesis testing File Organization Database Design Advanced Databases Analytics Data Mining and Data Warehousing Information Retrieval IR and Digital i librariesi Management E-Commerce Decision Making

11 Data Mining and Data Warehousing - Syllabus Introduction Bayesian Learning Overview of DWH Methodology OLAP and BI The Role of Information Theory in Data Mining Decision Tree Learning Instance-Based Learning and SVM Discovery of Association Rules Cluster Analysis Data Preparation Info-Fuzzy Networks November 8, 2013 Lecture No. 1 11

12 BI Undergraduate Track Core Undergraduate Elective Courses (at least 2) Financial DM Recommender Systems Text Mining and Web Content Mining Machine Learning Other Undergraduate Elective Courses (at least 2) Data Warehousing and Big Data Visualization Social Networks Analysis ERP Intelligent Systems Fault Detection Related Graduate Elective Courses See Next Slides Final Project in DM (Mandatory)

13 Master of Science with Focus on Data Mining i and dbusiness Intelligence Goal: train researchers e s and professionals poesso aswith sto strong analytical skills in the areas of Data Mining, Data Science, Predictive Analytics, Big Data, and Business Intelligence. Program of Study: 36 credits including eight mandatory and elective courses of each and Master Thesis (12 credits). Target Candidates: Information Systems Engineering, Software Engineering, Computer Science, Industrial Engineering, g, Statistics.

14 Admission and Enrollment for 2014 (MSc in DM & BI)

15 Core Faculty Members Mark Last Data Mining, Text Mining, Software Quality Assurance, Cyber Intelligence Lior Rokach Machine Learning, Recommender Systems Bracha Shapira Information Retrieval, Recommender Systems, Data Mining, Personalization Guy Shani Recommender Systems, AI, Machine Learning, Decision Making Yuval Shahar Medical Informatics, Decision Making

16 Additional Related Faculty Members Yuval Elovici Cyber Security Ai Ariel lfelner AI, Search Kobi Gal Decision Making, Cognition Meir Kalech Anomaly Detection Rami Puzis Social Networks Analysis Armin Shmilovici Data Mining, Operation Research Asaf Shabtai - Anomaly Detection, Malware Detection Meirav Taieb-Maimon - Visualization

17 Courses (MSc in DM & BI) Mandatory Courses: Research Methods in IS Statistical Methods in Information Systems Core courses (at least 4 courses) Advanced methods in data mining and data warehousing Text mining and Web Content Mining Applied Machine Learning Mining large datasets Advanced information retrieval systems (Recommender Systems) Elective courses (up to 3 courses) Financial Data Mining Advanced databases Analysis of complex networks Decision support systems Search methods in artificial intelligence Decision support systems in medicine Planning and automated decision making Identifying Cyber Attacks

18 Advanced methods in Data Mining and Data Warehousing Syllabus Overview of Current Research Areas in Data Mining and Data Warehousing Data Warehouses, Data Integration, and Big Data Feature Selection Advanced Methods of Decision-Tree Induction Data Stream Mining Spatio-Temporal Data Mining Graph Mining Text Mining and Web Content Mining Soft Computing Methods in Data Mining Homeland Security Applications November 8, 2013 Lecture No. 1 18

19 Text mining and Web Content Mining Syllabus Introduction to Text Mining and Web Content Mining Text Representation Natural Language Processing Ontologies Co-Occurrence Analysis Information Extraction Document Clustering and Categorization Text Summarization Social Media Analysis Lecture No. 1 19

20 Databases Oracle Distributed DB SQL Server DB Big Data Lab 2 Clusters of Hadoop Teaching Labs The largest cluster (Supported by Intel): 5 Servers with Total Storage of 152 Terabyte 320 Gigabyte of Main Memory 10 CPUs of Intel Xeon E (each with 6 cores)

21 Software Analytics: Weka RapidMiner MOA Matlab R Mahout (Hadoop) Databases Oracle SQL Server Cassandra Hive

22 Collaborators and Employers 22

23 The trend Conclusions A growing gneed for experts in Big Data, Predictive Analytics, Business Intelligence, and Data Science Main challenge Rapid advance of the relevant technologies Teaching dilemma Algorithms vs. practical tools Future plans at BGU Establishing inter-departmental programs in big data analytics and business intelligence Attracting international students

24 Thank you! ANY QUESTIONS?