cis6930fa11_introduction_class

cis6930fa11_introduction_class - CIS4930/6930 Data Science:...

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1 CIS4930/6930 – Data Science: Large-scale Advanced Data Analysis Fall 2011 Daisy Zhe Wang CISE Department University of Florida
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2 Vital Information Instructor: Daisy Zhe Wang Office: E456 Class time: Tuesdays 3-5pm, Thursdays 4-5pm Office hours: right after the class (one hour) TA: none Course page: http://www.cise.ufl.edu/class/cis6930fa11lad/ (read announcements frequently!)
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3 Overview Trend : Bigger Data and Deeper Analysis Data Science : Uses advanced analysis over large-scale data to create data products
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4 Data Science – A Working Definition Data Science is the science which uses computer science, statistics and machine learning, visualization and human-computer interactions to collect, clean, integrate, analyze, visualize, interact with data to create data products .
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5 Course Goal In this course, we will have in-depth discussions of recent publications related to Data Science. I will put most emphasis on the systems, applications and algorithms for large-scale advanced data analysis.
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6 This Course will Give you exposure to research topics and existing work in Data Science. Ask you to critique the papers we are going to read. Strongly encourage you to explore new research problems, come up with better solutions and make contribution.
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7 This Course will NOT Teach you statistics, machine learning, database systems. Teach you programming. Teach you how to be an expert in map-reduce, statistical packages, parallel databases.
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8 Expectations Require Information and Database Systems I (CIS4301) Data structures and algorithms, Coding (C, Java) Good Maths and Statistics Background Knowledge Encourage Actively participate in discussions in the classroom Read Data Science literature in general Experience in Machine Learning, NLP, Data Mining Academic honesty
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9 Course Outline Data Analysis I – Systems and Frameworks Data Collection, Cleaning, and Integration Data Analysis II – Applications and Algorithms Interface Design and Data Visualization
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10 Text Books Not required, but recommended. Class notes + papers.
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11 Additional Reading Pointers Data Science Summit (Strata) ( http://www.datascientistsummit.com/ ) Kaggle Competitions ( http://www.kaggle.com/ ) Data Science course at Berkeley ( http://datascienc.es/ ) Conferences and Journals VLDB, ICDE, SIGMOD CIDR, KDD, ICDM …
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Grading Homework (25 %) Project (55 %) Presentations (20 %) Participation (5% bonus) Novelty in Project (5% bonus) Late submission: 20% per day for up to 5 days. How can I get an A ?
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cis6930fa11_introduction_class - CIS4930/6930 Data Science:...

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