RMDS Lab is happy to announce its new data science competition, Developing and Analytics Dashboard to Improve Restaurant Performance

Source: RMDS Lab
Big Data & Analytics

Digital technology and data-driven decision making have dramatically changed how restaurants stay competitive or to survive. As food delivery and online reservation become more and more popular, restaurants can hardly operate without using digital and data technologies. With this in mind, RMDS Lab is announcing its next data science competition, Developing an Analytics Dashboard to Improve Restaurant Performance.This quarter’s contestants will be challenged with collecting important data sets and then creating an analytics dashboard that will help restaurants to boost their businesses and improve their overall performance. A grant of $1,000 will be awarded to the grand prize winner, and $500 will go to the runner up.

Everyone who registers for the competition will receive FREE admission to IM Data Conference, and contestants who complete and submit their project will receive a FREE, one-month premium membership to GRMDS platform as well as a certificate of completion.

Registrations:



Competitors will be challenged with finding good data to combine with the datasets provided to produce insights for the restaurant business. Through the process of data research, analysis, feature extraction and modelling, contestants should reach some solid and meaningful conclusions about improving restaurant performance. Based on various analyses, our contestants are expected to develop an analytical dashboard to showcase the business insights discovered in a way that is easily understood by restaurant decision makers and simple for them to implement to improve performance improvement.

We will develop a dashboard as an example that our contestants could take advantage of and continue with their own improvements. More specifically, contestants’ systems could allow restaurant owners to plug in their own data in order to see actionable insights, perhaps in the form of recommendations/projections for their establishment based on the contestant’s analysis.

Examples of analytical questions for competitors to explore may include:

  • Which day of the week or which hours of the day will be the busiest?
  • What factors influence restaurant customers and their purchases?
  • What aspects (food, service, price, and environment) are most valued by customers in each area?
  • What are the most important factors that contribute to customers’ review ratings?
  • What type of restaurants would customers prefer? (pick-up/delivery, dine-in)
  • What type of restaurants are facing the most serious competition in each area?
  • What’s the acceptable price level for different types of restaurants per location?
  • What keeps customers coming back?
  • How does a restaurant’s location determine its performance?

This competition will challenge competitors with collecting important datasets and creating a dashboard that restaurants can use to boost their revenue. It is free to enter, and winners will also be considered for internship positions. 

Important Dates

Registration begins Friday, August 27.
Datasets will be available on Friday, September 17
Completed projects will be due on Sunday, October 10
Competition winners will be announced on Thursday, October 28

All perspectives are welcome! Show us your most valuable insights from your innovative data analytics that could benefit the restaurant industry.

The Problem
 
This data science competition seeks to collect data and then develop an analytical dashboard to improve restaurant performance. Contestants will be provided with the necessary data to begin with.

➤  Dataset Overview
 
Participants will need to start with the dataset provided below to perform their analysis. This dataset contains the restaurant information in Los Angeles County. This dataset is published solely for the use of competition, please do not use it for other purposes.

There is also a recommended page for contestants to get access to some possible related datasets. Participants are encouraged to research and to collect more additional data to make their analysis more sensible and innovative.

Resources
 
RMDS Lab offers our community a variety of educational resources focusing on data science applications and techniques. You may explore the RMDS learning portal containing various data science courses at learn.grmds.org.

Competitors may use the code “COMPETITION2021” to get complimentary access to our online course on Big Data and AI to Improve Competency and Employability.
Below are additional free resources:


➤  Submission Deliverables
 
  • Technical report in PDF with names of all team members and team name required
  • Datasets used in .zip folder required
  • Readme on how to run your code and requirements.txt on your development environment required (except for high-school students)
  • CSV of results (Optional)
  • A working prototype like map, web page, apps, Tableau, Excel (Required if no codes submitted)

➤  Evaluation
 
Impact: What useful business insights are acquired from the proposal? How does this submitted model benefit (or cost) businesses, and what actionable steps are recommended to improve their work?​

Methodology Validity: Document the methodology, mathematics, and economic principles behind the proposal and provide the references or reasoning for your approach. How is the prediction generated and how are the factors weighted sensible? Are the assumptions and limitations of the methodology clearly outlined with suggestions to improve the proposal? Are the quantitative steps of data ingestion, feature engineering, model architecture, and performance optimization valid? How robust is your model?

Reproducibility: Does the solution use coding best practices with workflows and documentation to reproduce one’s work? Are the data ingress and egress pipelines reproducible? Is there a clear presentation of data science work in the documentation?

Usability: Is the information presented in a way that is actionable? Would a member of the general public understand the model, what it means, and what actions to take?

Ability to Deploy: Is getting access to the data realistic? How long is the computation time? How well is the scalability of the system to accept new data sources? How often does it need to be maintained? Is it hard to maintain/update? How much manpower, time, resources are needed to be allocated to maintain the functionality?​

Fair and Ethical Use of Data: Does the solution consider biases in data? Is the data from open and trusted sources?

Innovation: Will the idea have a big impact? How innovative is the approach, selection and weighting of various factors, or how information is displayed and communicated?

Judging Committee
 
  • Dr. Joseph Lema (Professor and Chair - Food & Beverage and Event Management Department, UNLV)

  • Richard Fox (former VP of data science of Qdoba Restaurants)

  • Cervantes Lee (former executive assistant to the chairman of Panda Express, adjunct faculty at UNLV College of Hospitality)

  • Dr. Richard Tang (professor at Loyola Marymount University College of Business Administration)

  • Dr. Jane Zhang (former professor of Cal Poly Pomona College of Hospitality Management)

  • Dr. Alex Liu (former Chief Data Scientist at IBM).
 
➤  Guidelines
 
Stage 1:  Registration
 
Participants will register on GRMDS. We will send out a confirmation email to all participants upon successful registration. Once you form your team, one representative from your team must fill out the Team Registration Form. Please note that this competition is open to all participants globally. For any questions you may ask it on the Forum.
 
Stage 2:  Team work and submission
 
Submissions must include all deliverables and are due Sunday, October 10, 11:59 PDT. Please upload all deliverables to the GRMDS. Place the names of all team members and team name on the technical report. Submission by any individual group member will represent the whole team.
 
Stage 3:  Evaluation and Final Presentation
 
Our expert committee will evaluate all project deliverables and select the finalist teams at the Awards Ceremony.

Prizes

First Place
  • $1,000 + Certificate
  • Complimentary six month premium membership at RMDS Lab

Second Place
  • $500 + Certificate
  • Complimentary six month premium membership at RMDS Lab

High School Award
  • $500 + Certificate
  • Complimentary six-month premium membership at RMDS Lab

Rising Star Award
  • Considerations for internship positions at RMDS Lab + Certificate
  • Complimentary six-month premium membership at RMDS Lab

Winners will also be considered for publishing opportunities with our partners.
 
 ➤ Code of Conduct
 
The use of data will adhere to ethical use and protection of individual data privacy. Find the Code of Conduct here.

The Problem
 
This data science competition seeks to collect data and then develop an analytical dashboard to improve restaurant performance. Contestants will be provided with the necessary data to begin with.

➤  Dataset Overview
 
Participants will need to start with the dataset provided below to perform their analysis. This dataset contains the restaurant information in Los Angeles County. This dataset is published solely for the use of competition, please do not use it for other purposes.

There is also a recommended page for contestants to get access to some possible related datasets. Participants are encouraged to research and to collect more additional data to make their analysis more sensible and innovative.

Resources
 
RMDS Lab offers our community a variety of educational resources focusing on data science applications and techniques. You may explore the RMDS learning portal containing various data science courses at learn.grmds.org.

Competitors may use the code “COMPETITION2021” to get complimentary access to our online course on Big Data and AI to Improve Competency and Employability.
Below are additional free resources:


➤  Submission Deliverables
 
  • Technical report in PDF with names of all team members and team name required
  • Datasets used in .zip folder required
  • Readme on how to run your code and requirements.txt on your development environment required (except for high-school students)
  • CSV of results (Optional)
  • A working prototype like map, web page, apps, Tableau, Excel (Required if no codes submitted)

➤  Evaluation
 
Impact: What useful business insights are acquired from the proposal? How does this submitted model benefit (or cost) businesses, and what actionable steps are recommended to improve their work?​

Methodology Validity: Document the methodology, mathematics, and economic principles behind the proposal and provide the references or reasoning for your approach. How is the prediction generated and how are the factors weighted sensible? Are the assumptions and limitations of the methodology clearly outlined with suggestions to improve the proposal? Are the quantitative steps of data ingestion, feature engineering, model architecture, and performance optimization valid? How robust is your model?

Reproducibility: Does the solution use coding best practices with workflows and documentation to reproduce one’s work? Are the data ingress and egress pipelines reproducible? Is there a clear presentation of data science work in the documentation?

Usability: Is the information presented in a way that is actionable? Would a member of the general public understand the model, what it means, and what actions to take?

Ability to Deploy: Is getting access to the data realistic? How long is the computation time? How well is the scalability of the system to accept new data sources? How often does it need to be maintained? Is it hard to maintain/update? How much manpower, time, resources are needed to be allocated to maintain the functionality?​

Fair and Ethical Use of Data: Does the solution consider biases in data? Is the data from open and trusted sources?

Innovation: Will the idea have a big impact? How innovative is the approach, selection and weighting of various factors, or how information is displayed and communicated?

Judging Committee
 
  • Dr. Joseph Lema (Professor and Chair - Food & Beverage and Event Management Department, UNLV)

  • Richard Fox (former VP of data science of Qdoba Restaurants)

  • Cervantes Lee (former executive assistant to the chairman of Panda Express, adjunct faculty at UNLV College of Hospitality)

  • Dr. Richard Tang (professor at Loyola Marymount University College of Business Administration)

  • Dr. Jane Zhang (former professor of Cal Poly Pomona College of Hospitality Management)

  • Dr. Alex Liu (former Chief Data Scientist at IBM).
 
➤  Guidelines
 
Stage 1:  Registration
 
Participants will register on GRMDS. We will send out a confirmation email to all participants upon successful registration. Once you form your team, one representative from your team must fill out the Team Registration Form. Please note that this competition is open to all participants globally. For any questions you may ask it on the Forum.
 
Stage 2:  Team work and submission
 
Submissions must include all deliverables and are due Sunday, October 10, 11:59 PDT. Please upload all deliverables to the GRMDS. Place the names of all team members and team name on the technical report. Submission by any individual group member will represent the whole team.
 
Stage 3:  Evaluation and Final Presentation
 
Our expert committee will evaluate all project deliverables and select the finalist teams at the Awards Ceremony.

Prizes

First Place
  • $1,000 + Certificate
  • Complimentary six month premium membership at RMDS Lab

Second Place
  • $500 + Certificate
  • Complimentary six month premium membership at RMDS Lab

High School Award
  • $500 + Certificate
  • Complimentary six-month premium membership at RMDS Lab

Rising Star Award
  • Considerations for internship positions at RMDS Lab + Certificate
  • Complimentary six-month premium membership at RMDS Lab

Winners will also be considered for publishing opportunities with our partners.
 
 ➤ Code of Conduct
 
The use of data will adhere to ethical use and protection of individual data privacy. Find the Code of Conduct here.

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