Cross-posted from MarkTab Data Mining
By Mark Tabladillo
I will be presenting a one-day pre-conference and a regular breakout session at the inaugural PASS Business Analytics Conference April 10-12 in Chicago, IL. This large conference requires paid registration. This post has details about the one-day pre-conference on April 10 and the regular conference session. Also, I provide a promotional code for a US$200 registration discount. However, first I will provide a general case for business analytics learning.
This term “business analytics” is increasingly being used to emphasize the need for scientific modeling and differentiate with the more common (but still important term) “business intelligence.” I delivered the inaugural session for the PASS Business Analytics Virtual Chapter (online) with a presentation titled “A Case for Business Analytics Learning.” Thoughts I have in that slide deck support reasons why this conference contributes to your own and your organization’s learning about scientific modeling and analytics.
Pre-Conference Session: A Best Practices Cookbook for Data Mining
I am presenting this one-day pre-conference on April 10 with Artus Krohn-Grimberghe, who is a data mining consultant and faculty member living in Germany. Here is the abstract:
Data mining increasingly fascinates business people and information technology professionals alike, with the promise of finding meaningful patterns, relationships, and opportunities in our continuously growing volumes of data. There are tried and tested best practices you can follow to begin and improve your data mining efforts. You’re invited to a full-day data mining seminar with Mark Tabladillo and Artus Krohn-Grimberghe to see these best practices in action. Aimed at the beginning to intermediate data scientist, this pre-conference workshop builds on Mark and Artus’ experience in teaching university students and advising industry clients. Following a cookbook theme for their presentation, they will be explaining and demonstrating their best practices framework by cooking through a data science example from beginning to end, covering these topics:
- How to avoid mythology while establishing a data science investigation
- How to apply the best artistry in data cleansing and transformation (shaping)
- How to apply best practices for machine learning algorithms
- How to communicate your data mining story within and beyond your organization
The presenters have designed specific breaks during the workshop where you can discuss and interact with them and other attendees. Note that these best practices transcend Microsoft SQL Server Data Mining, applying equally to other software, such as Matlab, Octave, R, SAS, SPSS, and Weka. After this workshop, you and your data science team will have the knowledge and best practices to approach small to large data mining challenges with confidence.
Regular Breakout Session: Data Analysis with R and Julia
R is a free, open-source environment for statistical analysis and graphing. In its almost 20 years of existence, R has remained popular in both academic and business environments. The newer Julia is a high-level, high-performance dynamic programming language for technical computing, with syntax that is familiar to users of other technical computing environments. This session outlines functional and performance differences between these two software packages. You’ll see demonstrations of best tips for integrating this software with Windows and walk away with guidelines for working with commercial software.
The code BAC698MVP will enable you to receive a US$200 discount from the conference registration fee (attendees who’ve already registered cannot retroactively use the discount code). You can find more information about this conference at http://passbaconference.com/ and register here.
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