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Predictive Analytics & Automated Intelligent Outlier Detection

University of Colorado Boulder - Tuesday November 12, 2013 @ 6:30pm MST

NOTE: For folks unable to attend in person register and we will email you a livestream link 2 hours prior to event. 

Location: ATLAS -[masked]th St Bldg 223, Boulder, CO - Room 100 

Map: http://goo.gl/maps/XTJ9v

Agenda:

6:30 - 7:00 Schmooze - Food will be served in Lobby.

7:00 - 8:00 Predictive Analytics by Michael Walker

8:00 - 9:00 Automated Intelligent Outlier Detection by Andrew Weekley

9:00 - 10:00 Network at Old Chicago at 1102 Pearl St. (western end of Pearl Street pedestrian mall)

See: http://oldchicago.com/locations/boulder

Predictive Analytics - Abstract

Predictive analytics is a hot topic in the data science and business communities. This presentation will cover the the latest state-of-the-art techniques for data science practitioners and real case studies for decision makers in business who need to understand how predictive analytics can help achieve durable competitive advantage.

Predictive analytics turns data into valuable, actionable information to determine the probable future outcome of an event or a likelihood of a situation occurring - and encompasses a variety of techniques from machine learning, algorithms, data mining, statistics, modeling and game theory that analyze current and historical facts to make predictions about future events.

Predictive analytics utilizes the power of data. Computers can learn from data how to predict the probable future behavior of individuals. While perfect prediction is not possible, determining probable behavior for certain profiles and demographics is useful. It can also be dangerous if predictive limitations are not understood or if negligently practiced. It can also backfire if folks get spooked and feel privacy has been violated.

In business, predictive models exploit patterns found in historical and transactional data to identify risks and opportunities. Models capture relationships among many factors to allow assessment of risk or potential associated with a particular set of conditions, guiding decision making for candidate transactions.

Bio

Michael Walker is a managing partner at Rose Business Technologies, a professional technology services and systems integration firm. He leads the Data Science Professional Practice at Rose. Mr. Walker received his undergraduate degree from the University of Colorado and earned a doctorate from Syracuse University. He speaks and writes frequently about data science and is writing a book on Data Science Strategy for Business. Learn more about the Rose Data Science Professional Practice at http://bit.ly/10TgVHG

Automated Intelligent Outlier Detection - Abstract 

Bad data is common in the atmospheric sciences. Often instruments are operated in sever weather conditions and in remote locations with limited power and communication infrastructure. Furthermore, the devices themselves might be research grade instrumentation and need constant attention. Typically, data is collected during a field program and is analyzed after the end of the field campaign. The intelligent outlier detection algorithm was developed to quality control time-series data collected by anemometers located on the mountains near Juneau Alaska. The intent was to develop an algorithm that mimics the human ability to identify suspect data – regardless of a priori knowledge about a given time series. Essentially, the algorithm segments a time series using basic image processing techniques and auto-correlation. The details of the algorithm will be presented in the context of several example time series.

Bio

Andrew Weekley is currently an analyst in the Strategic Energy Analysis Center at the National Renewable Energy Lab (NREL). Currently; Mr. Weekley is working on synthetic solar time series data used in solar integration models. Prior to NREL, Mr. Weekley was a software engineer in the Research Applications Laboratory at the National Center for Atmospheric Research (NCAR) where he developed image-processing algorithms and participated in numerous research projects. Mr. Weekley received his BA in physics and mathematics from the CU Boulder.

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  • Nitin k.

    Thanks to Michael Walker. I liked the Data Science vs. Data Engineers presentation (on the meetup files folder) a good one. Helped to fathom the changes occurring in technologies

    November 17, 2013

    • Nitin k.

      Michael, thanks for the ppt deck. The slides now are very visible. Infact, I was able to read through every slide and as someone who is getting his hands dirty, the slides made a lot of sense.

      November 17, 2013

    • Nitin k.

      The streaming was not very clear but walking through the slides helped. Appreciate your efforts.

      November 17, 2013

  • Jeff S.

    Found the presentation very informative, and I appreciated the technical level and pace. Can the presentation slides be posted?

    November 13, 2013

  • Michael M.

    Video for Automated Intelligent Outlier Detection:
    http://www.youtube.com/watch?v=rB70j7Hb65U

    1 · November 13, 2013

    • n

      Sweet dude!

      November 13, 2013

  • n

    I was grateful to be able see it remotely

    November 13, 2013

  • Michael W.

    Predictive Analytics - Outlier Algorithm Livestream URL: http://ustre.am/16Uil Start at 7:00pm MST / 9:00pm EST / 6:00pm PST


    Tuesday November 12, 2013

    Embed code:

    <iframe width="480" height="302" src="http://www.ustream.tv/embed/16422705?v=3&amp;wmode=direct"; scrolling="no" frameborder="0" style="border: 0px none transparent;"> </iframe>
    <br /><a href="http://www.ustream.tv/"; style="padding: 2px 0px 4px; width: 400px; background: #ffffff; display: block; color: #000000; font-weight: normal; font-size: 10px; text-decoration: underline; text-align: center;" target="_blank">Live streaming video by Ustream</a>

    November 12, 2013

  • A former member
    A former member

    Oops, just saw the thread about sending the link 2 hours prior. Thanks.

    November 12, 2013

  • A former member
    A former member

    Was going to be there in person, will now have to watch remotely. Can you please send the link again? Thanks for providing this option.

    November 12, 2013

  • Jing Z.

    I would like to be watching remotely. Thanks for your time and effort!

    November 12, 2013

  • Nitin k.

    This is a very interesting topic. Do we have a recording for people like me who cannot make it in real-time.
    Thanks in advance

    November 12, 2013

  • Don Sumudu Saranga H.

    I will be watching remotely thanks

    November 12, 2013

  • n

    I will be watching remotely. Thanks for providing this option!

    November 11, 2013

  • Michael M.

    Directions from Atlas building to Old Chicago (Boulder):

    1. Exit the Euclid St. parking deck/underground garage by turning right onto Euclid St.

    2. Turn right to go north on Broadway.

    3. After driving through the middle of the Pearl St. pedestrian mall, turn left onto Spruce St.

    4. Halfway down the block, turn left into the Spruce St. parking garage ($).

    5. Old Chicago is at the western end of the Pearl St. pedestrian mall at the intersection of 11th & Pearl.

    November 11, 2013

  • Michael M.

    Directions from Denver:

    1. US-36 to Baseline exit (the exit after Foothills Parkway but before US-36 turns into 28th St.).

    2. At end of ramp, turn left to go west on Baseline

    3. Turn right to go north on Broadway

    4. Make second right onto 18th St. (comes after Regent but before Euclid)

    5. Curve around as you're forced onto Euclid St.

    6. Park in the large Visitor parking lot & underground garage on the right. ($4 to park)

    7. Continue walking in the same direction you were driving on 18th St. -- actually the portion of 18th St. that's closed to public traffic.

    8. The Atlas building is the third building on the left, with the Pekoe Sip House coffee shop on the ground floor.

    9. Auditorium #100 is immediately off the lobby of the Atlas building.

    November 11, 2013

  • Douglas H.

    I will be watching remotely.

    November 11, 2013

  • Nitin k.

    Pl. email the link. Shall not be able to attend.
    Thanks in advance.

    November 11, 2013

  • Michael W.

    For folks unable to attend in person, register and we will email you a link to watch via livestream video 2 hours prior to start.

    2 · November 11, 2013

    • Bill H.

      Here is a predictive analytics question - what is the likelihood that there won't be enough seating by 7pm? That would help some of us decide whether to drive in or watch the live stream. I'd say that the offer of food increases the likelihood but that not being a CU meetup decreases the likelihood. I'd try to reflect that in my priors (Bayesian).

      November 11, 2013

  • Denise D.

    I will watch remotely.

    November 11, 2013

  • A former member
    A former member

    Will watch remotely

    November 9, 2013

  • Kate T

    I want to help my business user community make sense of some of the seemingly random patterns in our data.

    November 8, 2013

  • John S.

    Sometimes outliers are a good thing ;)

    November 8, 2013

  • Aman A.

    Will watch remotely!

    November 7, 2013

  • Leela Krishna K

    Need web access please

    November 3, 2013

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