staying competitive in data analytics: analyze boulder 20140903

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How to Stay How to Stay Competitive i n Competitive in Data Analytics Data Analytics Data Detectives of Boulder Richard Hackathorn, Jennifer Brendle, Scott Oetting & Jay Brophy with contributions from Mike McUne, Karen Blakemore, Ali Ongun, Larry Rupp, Jon Bates, Sara Bates, Brian Feifarek https://www.linkedin.com/groups?

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Presentation to Analyze Boulder on Sept 3 2014 by the Data Detectives of Boulder (https://www.linkedin.com/groups?home=&gid=6525462). Sharing our experiences over the past 3 years with MOOCs, Kaggle, etc.

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Page 1: Staying Competitive in Data Analytics: Analyze Boulder 20140903

How to Stay How to Stay

Competitive in Competitive in

Data AnalyticsData Analytics

Data Detectives of BoulderRichard Hackathorn, Jennifer Brendle, Scott Oetting & Jay Brophywith contributions from Mike McUne, Karen Blakemore, Ali Ongun, Larry Rupp, Jon Bates, Sara Bates, Brian Feifarek

https://www.linkedin.com/groups?home=&gid=6525462

Page 2: Staying Competitive in Data Analytics: Analyze Boulder 20140903

Never Stop Learning…

• Massive open online courses (MOOCs) - learning at scale• Coursera, Udacity, edX…

• Certificate Programs• Johns Hopkins Specialization

• Bootcamps (hands-on labs)• Big Data Bootcamp, Denver Oct 17-19, $1,500!

http://globalbigdataconference.com

• University Degree Programs• Leeds M.S. in Business Analytics

From Richard Hackathorn

Exploding & D

iversifying

Exploding & D

iversifying

Page 3: Staying Competitive in Data Analytics: Analyze Boulder 20140903

Data Science Specialization

https://www.coursera.org/specialization/jhudatascience/

From Jennifer Brendle

Page 4: Staying Competitive in Data Analytics: Analyze Boulder 20140903

Note: The first two courses in the series are prerequisites for the others. The remaining courses may be taken in any order and can be taken in parallel.

Johns Hopkins Specialization1. The Data Scientist’s Toolbox

2. R Programming

3. Getting and Cleaning Data

4. Exploratory Data Analysis

5. Reproducible Research

6. Statistical Inference

7. Regression Models

8. Practical Machine Learning

9. Developing Data Products

plus… The Capstone Project

From Jennifer Brendle

Page 5: Staying Competitive in Data Analytics: Analyze Boulder 20140903

PROS CONS

Pre-defined group of related courses

No direct interaction with Instructors.

Somewhat self-paced Some strict due dates

Active discussion forums Focuses completely on R and heavily on Biostatistics

Volunteer Teaching Assistants Unknown value as a credential compared to MS or more traditional certification programs

Develops a portfolio of work Low completion rates. During its first five months, more than 800K enrolled, but only 266 students have completed all nine courses

Page 6: Staying Competitive in Data Analytics: Analyze Boulder 20140903

Learn Analytics, Then Do Analytics• Do Competitions• Kaggle (especially involvement in the forums)

• CrowdANALYTIX• HackerRank

• Do Mess with Real Data• KDnuggets dataset directory http://www.kdnuggets.com/datasets/

• Data Mining Competitions http://www.kdnuggets.com/competitions/ • Social & IoT Data Streams http://www.programmableweb.com/

• Do Know Your Industry’s Data (from Mike McUne)

• Do Appreciate The Classics (like iris, titanic, bird strikes…)

From Richard Hackathorn

Page 7: Staying Competitive in Data Analytics: Analyze Boulder 20140903

Depend upon Your Community• Professional Communities• IEEE Computer, ACM …• LinkedIn groups, Data Science Central, KDnuggets…• Reddit: machine learning, statistics, computer vision

• Technical Communities• R, Python, and hundreds of others• Tutorials, galleries, cheat sheets

• Local Communities• Data Detectives, Analyze Boulder, and

dozens of Denver/Boulder meetups

From Richard Hackathorn

Lots of le

arning tips

in Data Detective blog

Page 8: Staying Competitive in Data Analytics: Analyze Boulder 20140903

What’s In Your Toolbox?

• Lots to choose from…• 2014 KDnuggets Software Poll – 69 used in real projects*• “You need to be familiar with different tools, but you also

need to be able to use them. A lot of tools have tutorials available. You have to be able to use the tools to solve real problems. That being said, there is not enough time to become adept at everything. Choose carefully.”

• Balancing excellence with comprehensiveness • But still do something useful

• Compatibility with your team and company

From Mike McUne

*http://www.kdnuggets.com/2014/06/kdnuggets-annual-software-poll-rapidminer-continues-lead.html

Page 9: Staying Competitive in Data Analytics: Analyze Boulder 20140903

What Do Employers Value?• Still being decided…

• “Unfortunately, I do not know if there is an answer to this yet because (1) we are in the very early innings, (2) the technology is rapidly evolving, and (3) the business usage of analytics is exploding into all kinds of new areas.”

• Depth versus Breadth?• Specialist or generalist?• Small toolbox or large toolbox? • Deep industry smarts or cross-industry savvy?

• Degree, certificate or track record?• Document your abilities. Build your portfolio!

• “Pro-bono consulting groups, such as Code for Colorado”• Regardless… “Stick to a focused plan”

From Scott Oetting

Page 10: Staying Competitive in Data Analytics: Analyze Boulder 20140903

In Summary

• Lots of resources! Perhaps too many?• Learn and then do• Depend upon your community• Choose your toolbox wisely• Document your abilities for desired employers• Need to get serious & stay focused

Join the Data Detectives at BJ’s Boulder on Weds at 11:30 for group mentoring and discussion. Note our blog on LinkedIn group for current information.- https://www.linkedin.com/groups?home=&gid=6525462