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Introduction to Advance Analytics Course
Introduction to Advance Analytics Course
Introduction to Advance Analytics Course
Introduction to Advance Analytics Course
Introduction to Advance Analytics Course
Introduction to Advance Analytics Course
Introduction to Advance Analytics Course
Introduction to Advance Analytics Course
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Introduction to Advance Analytics Course

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Part of advanced analytics course.

Part of advanced analytics course.

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  • Facebook friend connections worldwide, a network diagram of the Enron email set, a comparison of similar gene sequences between humans, chimps, and macaques
  • HW, FW, MW, SW: Hardware Firmware Middleware Software
  • Transcript

    • 1. Advanced Data Analytics: Introduction Jeffrey Stanton School of Information Studies Syracuse University
    • 2. Kilo, Mega, Giga, Tera, Peta, Exa Zetta = 1021 bytes…An organization Over 95% of theemploying 1,000 digital universe isknowledge workers "unstructured data" –loses $5.7 million meaning its contentannually just in time cant be trulywasted having to represented by its fieldreformat information in a record, such asas they move among name, address, or dateapplications. Not of last transaction. Infinding information organizations,costs that same unstructured dataorganization an accounts for more thanadditional $5.3m a 80% of allyear. information.Source: IDC Source: IDC
    • 3. Major sources of data• Health-related services, e.g. benefits, medical analyses• Business: – Walmart: 20 million transactions/day, 10 terabyte database• Science: – NASA: 0.5+ terabytes per day per satellite• Society and everyone: news, digital cameras, YouTube• DOD and intelligence 4
    • 4. Analytics: Multiple Disciplines Database Technology Statistics Machine Visualization Learning Analytics Pattern Recognition Social Computer Science Science 5
    • 5. Analytics: Multiple Skills• Curiosity – Interest and intrinsic motivation to figure things out, ask why, and pursue solutions• Skepticism – Seek simplicity and distrust it, go below the surface explanation of things, question all assumptions• Writing – Communicate results, tell stories, convince others of the merits of your case• Visual Reasoning – Develop and present visualizations that support your conclusions• Statistics – Draw inferences from and summarize data to develop a case and a story• Programming – Manipulate software tools to create a chain of provenance for data and analysis 6
    • 6. Knowledge Development for Industry, Education, Government, Research Domain Experts Infrastructure Professionals Expertise in specific Information Rapid pace of subject areas Organization & IT development Visualization Limited opportunity to Limited expertise in master technology skills Information Data Solution domain areas Analysis Scientists IntegrationProliferation of big data & Specialized knowledge of new technology HW, FW, MW, SW Digital CurationNeed for knowledge and Communication information managers challenges Transforming Data Into Decisions
    • 7. Analytics: Key Steps• Learn the application domain• Locate or develop a data source or data set• Clean and preprocess data: May take 60% of effort!• Data reduction and transformation – Find useful pieces, squeeze out redundancies• Choose analytical approaches – summarize, visualize, organize, describe, explore, find patterns, predict, test, infer• Communicate the results and implications to data users• Deploy discovered knowledge in a system• Monitor and evaluate the effectiveness of the system 8

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