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Purdue University Team
The Three Marketers WIN
STAMINA4 IT/Analytics Case Competition
Congratulations to Team Members Aishwarya Ravindra Poojary, Cheitali Thakkar, Dhruvi Patwa, and Sara Hernandez who will be one of three teams advancing to the Purdue/IU Case Competition in November

Current Events

Workshop on Applied Machine Learning:
 Practical Industry Case Studies, Introduction to Cloud AI Tools and Hackathon

Date: Friday 25th Oct 2019

Krenicki Center for Business Analytics and Machine Learning, Purdue University has organized a one-day workshop on applying machine-learning (ML) to aid decision-making, introduction to cloud AI platform tools for ML, and a hackathon. The workshop will first introduce the role of AI and ML in the digital transformation of enterprises today, present case studies of a few real-world large-scale industry implementations of ML to aid decision-making, and then provide a practical hands-on tutorial on cloud AI tools for common machine learning tasks. Once the participants are familiar with the cloud ecosystem, they will participate in a hackathon where they will be asked to formulate an ML-approach to a provided business problem, and then implement this approach via an ML pipeline on the cloud. A group of 3-5 participants will work together and present their results at the end.

Tentative schedule

Session 1: 8:30 AM – 9:15 AM

Introduction to applying Machine Learning for decision making: case studies of large-scale real-world industry implementations.

This session will describe the role of AI and ML in the digital transformation of businesses, introduce a framework for approaching ML problems, and discuss technical as well as enterprise or business challenges in the adoption of this framework. Finally, a few case studies of real-world industry implementations will be presented.

Coffee Break: 9:15 AM – 9:30 AM

Session 2: 9:30 AM – 10:45 AM

            Tutorial: Introduction to Cloud AI tools, and setting up an ML pipeline on the cloud.

This will be a hands-on session introducing the tools available on Google Cloud AI Platform for machine learning applications. The tutorial will go through setting up an ML project on the cloud and go through several example configurations for various use-cases. For example, there will be discussions of feature engineering, data pipelines – real-time and offline processing systems, explainable AI, etc. This will set up the technical pre-requisites for the Hackathon.

Hackathon Introduction: 10:45 AM – 11:00 AM

            Introduction to the hackathon rules, the problem to be solved, and the available data-sets and resources.

Hackathon: 11:00 AM – 4:00 PM

                Daylong hackathon with a lunch break at noon. (lunch will be provided)

REGISTRATION IS REQUIRED AND MAY BE DONE AT https://webapps.krannert.purdue.edu/Pitch/Login

Past Events

  • 2018

    Esport Data Hackathon: 24 Hour Challenge

    Esport Data Hackathon Winning Team along with BIAC Director Karthik Kannan, BAIM Program Director Matthew Lanham, and SportsUnited Rep Oliver Wells

    The winning team: team TSM.
    Team members: Joshua Allen, Thomas Chen, Stefany go, Shubhankar Sinha and Patrick Tirtapraja.

    Nov 9th - 10th, 2018

    Purdue Krannert School of Management's Krenicki Center for Business Analytics & Machine Learning partnered with Discovery Park, Integrated Data Science Initiative (IDSI), and SportsUNITED to bring an exciting Esport Hackathon to the Mid-West. Centered on the Purdue University Campus, West Lafayette, IN teams competed in a 24-hour Challenge, using cutting-edge technology (Machine Learning, AI, AR/VR, NLP, Immersive Media, Machine Vision, Blockchain) to develop innovative data solutions that solve problems for Data Visualizations and Predictive Analytics to support Fan Experience,  eAthlete Training & Performance.

    The photo is of winning team along with Krenicki Center Director Karthik Kannan, BAIM Program Director Matthew Lanham, and SportsUnited Rep Oliver Wells.

    Go to the link for more information:
    https://takegiantleaps.com/purdue-esport-data-hackathon-and-the-winner-is/?fbclid=IwAR3Yn91rIgRcMs68MxEDRSvPr9xhgfnV7t7_H2x3Gh9ZCiNnFkqTTk1Q0qQ

    Purdue/IU Case Challenge Sponsored by Eli Lilly

    Nov 9th - 10th, 2018

    All participants of Purdue/IU Case Competition sponsored by Eli Lilly

    All participants of Purdue/IU Case challenge

    Winning team of Purdue/IU Case Challenge

    The winning team of Purdue/IU Case Challenge

    STAMINA4- IT/Analytics Case Challenge

    STAMINA4 ITAnalytics Case Challenge Winning Team

    The winning team along with our corporate judges and Krenicki center director

    Similar to real-world business projects, the STAMINA4 case competition is an intensive, experiential learning opportunity that allows students to showcase their critical thinking and analytical abilities, communicate their ideas, and demonstrate mental tenacity.  STAMINA4 participants only have four hours to analyze a case and create a presentation to share their recommendations.

    The STAMINA4 IT/Analytics Winning Teams: 
    1st place Team Analytica consisting of team members:  Juilee Bhosale, Kiran Samayam, Lorena Bustamante and Sonia Daryani.
    2nd place Team Savy consisting of team members:  Ananth Nath, Shubham Gupta, Vijaya Rani and Yuvraj Gupta.
    3rd place J.A.K.Z Consulting Group:  Alec Riedman, Joseph Sawicki, Kristen Scully and Zoe Jordan.
    4th place Winter is Coming:  Fandy Fauzi, Jung Choi, Priyanka Jain and Srijan Saurabh.

    The photo is the winning team along with our corporate judges and Krenicki center director Karthik Kannan; Clare Harshey with Crane Division, Susan Rowe with Ogilvy Neo, Dave Suwanski with Nielsen and Bhagat Kandula with Wabash National.

    Data Science for Business and Economics Conference

    Data Science for Business and Economics Conference

    May 24th - 26th, 2018

    The objective of this conference was to feature speakers from business, economics, statistics, computer science, engineering, and other areas as they explore the use of data science to solve real-world problems.

  • 2017

    2017 Dawn or Doom Data Dive

    Krannert School of Management's Krenicki Center for Business Analytics & Machine Learning and Information Technology at Purdue (ITaP) recently joined together to offer a unique opportunity to Purdue's full time undergraduate and Master's students to work on a real-world data analytics problem faced by CISCO, utilizing a dataset provided by the company. Interdisciplinary student teams analyzed the dataset to come up with recommendations. The top seven teams presented their findings to a panel of judges consisting of senior executives. The winning team was KIME Consulting comprised of Chung Tang from Krannert School of Management, Qi Meng from The College of Science, Ruoxuan Zhao from Krannert School of Management and Xin Wei from The college of Health and Human Sciences.

    Kime

    Winning team KIME Consulting being presented with their winning check by Krenicki center Chair Professor Prabuddha De and ITAP Vice President and System Chief Officer Gerry McCartney along with Corporate Judges Jeff Cristee with CISCO, Vinu Thomas with Presido; Dewand Neely with the State of Indiana and event organizer Associate Professor of Management Mohammad Rahman.  

    2017 Dawn or Doom Date Dive Presentations

    2017 STAMINA4 IT/Analytics

    2017 Stamina4 IT Analytics Winning Team

    2017 STAMINA4 IT/ANALYTICS winning team including two of our corporate judges; Dave Suwanski with Nielsen Company and Mike Courtney with Accenture Operations; 
    Winning team members were: 
    Christine Rasquinha 
    Siddhanth Rajagopolan 
    Mike Hansen (missing from the picture)  
    Randy Wang

  • 2016

    Krannert-Walmart Data Dive

    April 2-3, 2016

    Teams from Northwestern University and the Georgia Institute of Technology won the inaugural Krannert-Walmart Data Dive on the campus of Purdue University on April 2-3. Each team took home $5,000 in prize money.

    A total of 17 teams from 11 universities took part in the competition. Graduate and undergraduate students used Walmart trend data and were tasked with developing solutions in two areas—customer segmentation and predicting products for efficient shelf space usage.

    Several Walmart executives were on hand for the 24-hour competition, including Karenann Terrell, executive vice president and chief information officer.

    “You won’t find the kind of commitment these students displayed during the competition very often,” Terrell said. “I was incredibly impressed by the breadth and insight of the questions we were asked.”

    For Northwestern student Eric Lundquist, who was part of the winning team in customer segmentation, the competition provided real-world perspective to what he is learning in the classroom.

    “Statistics are only useful in the extent to which they provide business value and can be communicated clearly and understood fully. We spend a lot of time considering the business value and actionability of our approach, which is something that doesn’t always come up in some of our more technical academic coursework,” Lundquist said.

    Georgia Tech’s Brian Burns said his team found the large amount of information allowed them to drill deeper into the company’s issues. “I’ve had experience with data-driven decisions prior to this competition, but what Walmart’s data set highlighted is that strategic decisions can become more granular as the volume and variety of information becomes larger,” Burns said.

    In addition to the winning schools and the host team from Purdue, the competitors included entries from Indiana University, Michigan State University, Ohio State University, University of Illinois at Champaign-Urbana, University of Iowa, University of Maryland, University of Rochester and University of Texas-Dallas.

    The competition is believed to be the first data dive on a college campus. It was organized by Mohammad Rahman, associate professor of management at Purdue’s Krannert School of Management, and was the first major activity conducted by Purdue’s Krenicki Center for Business Analytics & Machine Learning.

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