Lecture1 2.pdf - Sep 10th Big Data for Data Science MIE 1628 Lecture 1 Introduction into Big Data Lecture outline \u2022 \u2022 \u2022 \u2022 Introductions

Lecture1 2.pdf - Sep 10th Big Data for Data Science MIE...

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Sep 10 th Big Data for Data Science MIE 1628 Lecture 1 Introduction into Big Data
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Lecture outline Introductions, instructor’s experience Data science objectives Emergence of big data industry What is Big Data & Hadoop: History of Hadoop Course outline, marking scheme Final project discussion Technology for this course Important Hadoop concepts
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SUMMARY Computer Scientist with 18 years of experience in applying mathematical & statistical techniques to complex data-intensive problems in finance and academia Lead Data Scientist, TD Bank 2018 – Present NLP, Semantic Topic Modelling, Optical Character Recognition, Sentiment Modelling, Wealth Management Principal Data Scientist, Manager, Capital One UK/Canada 2015 – 2018 Fraud Modelling (first AI based application fraud model in the UK and first self-learning, self-deploying transactional fraud model in the world) TEACHING : University of Nottingham 2014-15 Industry Teaching Sessions 2015-20 McMaster, AI and Machine Learning 2020 University of Toronto 2020 EDUCATION University of Nottingham Doctor of Philosophy in Computer Science 2012-2015 Istanbul Technical University 2010 –2012 MSc Robotics / Computer Engineering Isfahan University 1998 – 2003 BSc Computer Engineering Instructor’s background Shahriar Asta
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Your point of contact for this course: Lecturer: Shahriar Asta: [email protected] Three TAs: Ta Jiun Ting (Jeff): [email protected] Gautam Dawar: [email protected] ca
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Survey for enrolled participants You will receive an email with a request to answer a few questions. Please fill out the survey by next class, it will help me adjust curriculum to fit your background and programming skills: What department are you affiliated with? (MEIE, others) Have you taken any data science courses (machine learning, statistics etc)? What programing languages and environments do you have experience with: Java, C++, Python, R, Matlab, SAS, SQL, other What big data solutions do you have experience with? None, Hive, Impala, Spark, Kafka, HDFS, Azure, Ambari, Zookeper What topics would you like to cover and focus on in the class?
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Business background and motivation for this course
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My experience with enterprise data science and big data Canadian retail US retail Wholesale Corporate Personal and commercial banking Wealth Insurance Canada: .~15M customers Revenue: $21B (2017) Personal & business banking Wealth management : U.S. ~8 million retail customers Revenue: $10.2B (2017) Capital markets Corporate and investment banking Revenue: $3.2B (2017) Revenue: $1.6B (2017) The Banking Business Key Product Groups: Personal banking: Personal deposits Consumer Lending Credit cards and Merchant solutions Auto finance Business Banking Commercial banking Small Business Banking Wealth: Direct Investing Asset Management Advice Insurance Includes trade data, planner/advisor information, securities, mutual funds, market data …
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Long term technology trends transforming enterprises Big data Blockchain Data science AI Mobile and Digital Banks becoming technology companies Process Automation Biometrics and adaptive security Cloud IofT
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