News & Events

2020 OUC Meter Data Science Competition

Orlando Utilities Commission (OUC) and the Department of Statistics and Data Science, UCF are pleased to announce a Meter Data Science Competition. Participants will get the opportunity to leverage real utility data sets and collaborate with utility ‘subject matter experts’ in order to help solve actual utility problems. And of course, the winning teams will also receive cash prizes!!!

Eligibility:

The contest is open to current Full-time and Part-time UCF students. Students can submit as a team with at least 2 and up to 4 members. Employees, immediate family members of employees, and/or those living in the same household of employees of OUC are not eligible.

How to enter:

Each team must appoint one team member as lead member. The lead member should fill in the team information in this (registration form-closed) and submit via email to Dr Rui Xie.

All participants must be current students of UCF.

Important Dates

  • Registration Deadline: September 11, 2020
  • Contest start date: September 16, 2020
  • Submission deadline:  February 01, 2021

Background and additional information is available below:

 

 

2019 Addition Financial Analytics Competition

 

Addition Financial Credit Union (AFCU) and the Department of Statistics and Data Science, UCF are pleased to announce a Data Analytics Competition using customers information data to predict if a customer is about to churn.

HOW TO ENTER:

Please download registration form here (closed), fill it out and submit.

Once registered, a registration ID will be assigned to each contestant by the system. The contestant will receive an email with the registration ID and a link to download the datasets within 24 hours of registration. All the participants have to be current students of UCF.

( If you didn’t receive the data within 24 hours, please send email to ask )

Important Dates

Contest start date: September 3, 2019 (data files will be available on this date)

Registration Deadline: October 1, 2019

Important Dates:

  • Monday, Jan 20th , 2020– Written Submission deadline
  • Tuesday, Jan 21st  through  Friday, Jan 31st – Student presentation (oral/PowerPoint) weeks at AFCU, Lake Mary Office
  • Friday, Feb 21st – AFCU will make winner selection and notify UCF.
  • Wednesday, Feb 26th  – Data Symposium, winners announced

Schedule timeslots for presentations in this link by January 15th:

https://calendly.com/afcu/afcu-data-competition-presentation

Background information and contest rules are available below:

Please click on the links for Competition Description and Rules and Colloquium Presentation Slides.

Q&A from AFCU.

CFE 2018 Lending Analytics Competition

 

CFE Federal Credit Union and the Department of Statistics, University of Central Florida are pleased to announce a Data Analytics competition using real life (but disguised) data to reduce the risk and make better lending decisions.

HOW TO ENTER: The registration is CLOSED.


Once registered, a registration ID will be assigned to each contestant by the system. The contestant will receive an email with the registration ID and a link to download the datasets within 24 hours of registration. All the participants have to be current students of UCF.

Important Dates

Contest start date: September 14, 2018 (data files will be available on this date)

Registration Deadline: October 1, 2018

Final submissions due: February 4, 2019 at 5 PM (early submissions are encouraged)

Group presentations will be conducted during the weeks of Feb 4, 2019, and Feb 11, 2019

UCF analytics dept. notification of top three winners: March 9, 2019

 

Background information and contest rules are available below:

Please click on the links for Competition Description and Rules, and Colloquium Presentation Slides.

Q&A from CFE.

CFE 2017 Lending Analytics Competition Winners

UCF’s winning team: Yanmei Patella, Jingrong Dai, Mingming Zhou; facutly advisor: Daoji Li, Ph.D.; CFE’s team: Jason Mizrahi, Kristen Ward and Daniel Kenon

On March 22, UCF hosted its annual Data Analytics Symposium, featuring professors and industry professionals as keynote speakers. During the event, CFE announced the top three teams for CFE 2017 Lending Analytics Competition, and the first-place winners had the opportunity to present their findings. CFE awarded a total of $5,000 in scholarships to the winning teams. The top three teams are:

  • 1st place — Team 14: Mingming Zhou, Jingrong Dai, Yanmei Patell
  • 2nd place — Team 4: Christen Lucido, Leah Neagele, Sean Davis
  • 3rd place — Team 10: Md Shakil Zaman, Jie Xiong, Qing He, Michael Koller

See the link here for more details.

 

UCF team places 2nd in 2017 Analytics Shootout competition

Mingming Zhou, Jerry Oglesby (Senior Director, Global Academic Program at SAS), Daoji Li and Yanmei Patella

Faculty advisor Daoji Li, Ph.D., led a UCF team to a second place finish in the 2017 SAS Analytics Shootout competition which was announced in September in Washington D.C. in the front of over a thousand people. (Watch the announcement video here.) This annual competition pits teams from all over the world against each other to solve a real-world analytics problem. This year’s case study required teams to analyze wildfire events and examine the underlying factors that contribute to their formation–research that helps communities prepare for wildfire impacts and mitigate risks.

Historically, UCF teams have consistently placed in the top three spots in this competition, with top three finishes in 2011, 2012, 2016 and now 2017. The faculty advisors for previous years were Morgan Wang, Ph.D., and David Nickerson, Ph.D. This year’s team consisted of Yanmei Patella (team captain) and Mingming Zhou. Yanmei recently earned a master’s degree from UCF’s Data Mining Program and is working at Citibank now. Mingming is currently completing a SAS Data Mining Certificate. Both were led by Li, who is a new member of the statistics faculty. This was his first competition, but he felt prepared thanks to the mentorship of Wang.

“It is very fortunate for me to get mentoring from senior colleagues, especially from Dr. Morgan Wang,” Li said. “Dr. Wang shared his success story on SAS Analytics Shootout with me. Our department also encourages junior faculty to attend national big data competitions.”

Daoji Li, Yanmei Patella, Mingming Zhou, and Edgard Maboudou

Nearly 20 years ago, the department created the country’s first data mining program at UCF. Now, UCF is about to offer doctoral students an opportunity to get ahead of the data science curve with a Ph.D. degree in Big Data Analytics. Led by Dr. Shunpu Zhang, chair of the Department of Statistics, he and his colleagues created this new PhD program to spearhead UCF’s effort to meet the Big Data Challenge. The Ph.D. program has been approved by Board of Governors, State University System of Florida and is in high demand from industry professionals and major businesses. The inaugural class begins Fall 2018. The SAS Analytics Shootout competition provides national exposure and recognition for UCF’s big data focus.

The team captain and faculty advisor were awarded complimentary travel, accommodations and meals to attend the annual SAS Analytics Conference and, during the awards ceremony, to give a presentation on their project.  Along with national recognition, the team also earned a $3000 gift from SAS for UCF.

Edgard Maboudou, Ph.D., Associate Professor of UCF’s Statistics Department, attended the conference and worked a booth to connect with conference attendees, promote UCF’s master and Ph.D. programs, and recruit graduate students in data mining, statistics and big data.

Learn more about the UCF Big Data Analytics Ph.D. program.

CFE 2017 Lending Analytics Competition

 

CFE Federal Credit Union and the Department of Statistics, University of Central Florida are pleased to announce a Data Analytics competition using real life (but disguised) data to reduce the risk and make better lending decisions.

HOW TO ENTER: Click “Register Now” button and fill out the registration form at the following URL:


Once registered, a registration ID will be assigned to each contestant by the system. The contestant will receive an email with the registration ID and a link to download the datasets.

Important Dates
October 6, 2017 – Competition Start
January 26, 2018 – Submission deadline

Disney world Waiting Line Colloquium

AVOID LINES AT DISNEY WORLD BY PREDICTING HOW LONG YOU’LL WAIT”

Speaker: Len Testa, Computer Scientist TouringPlans.com

APRIL 21, 2017 1:30PM – 2:30PM
Seminar Room, Room 222, TC2

Abstract:

Millions of people visit Walt Disney World annually. “Long waits in line” is one of their most frequent complaints.
It’s possible to minimize these visitors’ waits, if you can predict how long the lines will be at any time of day.
In this talk you’ll hear how the team at TouringPlans.com uses statistics, machine learning, and computer science to predict wait times at every Walt Disney World ride, for up to 365 days in advance. You’ll also hear real-world examples of how TouringPlans deals with conflicting – andoften intentionally incorrect – source data when setting up their machine learning systems.

Siemens 2017 Wind Analytics Contest Winners

Siemens Wind Power Inc. announced winners for the Data Analytics Contest at the University of Central Florida Symposium 2017 held by the Department of Statistics.

First Place: Team 14

Taha Mokfi , Mahsa Almaeenejad, Md Jibanul Haque Jiban , Kanak Choudhury

Second Place: Team 12

Megan Dotson , Iryna Tyukina, Hashim Zakiullah

Third Place: Team 8

An Sun, Lonnie Lester, Matthew Keeran, Xianchao Xiang, Yanmei Patella

 

2017 Disney Contest

Touring Plans Co. and the Department of Statistics, University of Central Florida are pleased to announce a Big Data Challenge using real life data to predict wait times at Walt Disney World.

HOW TO ENTER: Click “Register Now” button and fill out the registration form at the following URL:

 

 

Taha Mokfi
Once registered, a registration ID will be assigned to each contestant by the system. The contestant will receive an email with the registration ID and a link to download the datasets.

Background information and contest rules are available below:

Dataset (You need to register first then we will send you the password for the dataset)

Problem Statement and Rules