Meet the Organizers of the COVID-19 Hackathon

Recently, we caught up with current MSDS student Sepi Dibay and recent MSDS alumna Deepthi Hegde on their successful COVID-19 Hackathon from summer 2020.

Bios:

Sepideh (Sepi) Dibay immigrated to the United States in 2009 and pursued her Master of Public Health and Ph.D. in Epidemiology. She did her postdoctoral research at Fred Hutchinson Cancer Research Center and currently is interning at Amazon as a Research Scientist. Sepi has extensive experience designing/conducting research and analyzing observational and experimental data. She is also pursuing a Master of Science in Data Science at UW to enhance proficiency, and expand domain versatility.

Deepthi Hegde is a data scientist who is passionate about building real-time products that are scalable. She is currently with Microsoft and is a recent graduate of the Master of Science in Data Science program at UW. While at UW, she did research internships at Google and Nike, focusing on deep learning applications in computer vision. Before that, she was a researcher at Carnegie Mellon University where she worked on various machine learning projects. In her free time, she loves to mentor students on interviewing and jobs in data science.

What motivated you to organize the COVID-19 Hackathon?

It was 2 months into the pandemic and the situation wasn’t getting any better. We were bored of staying home and were looking for meaningful ways to contribute towards the cause in whatever way we could. We tried different channels such as volunteering for the State of Washington Health Department but since everything was new and the spread of this deadly virus was happening quickly we could not find a meaningful way to contribute. As data scientists, we believed in the power of data in combating the situation. We thought that by coming together as a community and combining research efforts and sharing insights, we could create more impact than each of us could individually. That’s when we decided to organize an online hackathon.

How many participants were there in total?

100+ students joined the competition and 42 participants made a submission.

How many teams submitted projects?

We had 13 teams submit their projects. Each team had 3-5 members.

The event was virtual because of the COVID-10 pandemic. How did the fact that it was completely online impact the event?

This was a very new way of organizing a hackathon and required a lot of coordination and arrangements to spread the word and engage the participants. Even though in some sense the online format limited our power to collaborate in person, it definitely helped us get participation from around the world. We had several teams with members from different time-zones working around the clock. We were also able to bring in experts in the field of data science from different states to offer introductory workshops on the first day of the hackathon.

What platforms did you use to host the hackathon? Can you describe how participants and teams were able to participate virtually?

We used Slack extensively for offline communications with the participants before and during the hackathon. During the two days of the hackathon, all the workshops, events and check-ins were done via Zoom. For collaboration on the projects, we asked teams to use GitHub, which was also how we asked teams to make their submissions.

Who were the judges?

Tim Randolph, Associate Member at Fred Hutchinson Cancer Research Center
Anna Talman Rapp, Program Officer at the Bill & Melinda Gates Foundation
Duncan Wadsworth, Data Scientist at Microsoft
Ying Li, Chief Scientist at Giving Tech Labs

What workshops did you conduct?

We conducted 2 introductory workshops on Day 1:

Intro to NLP (natural language processing) by Grishma Jena, Data Scientist at IBM
Intro to Time Series by Stanislav Panev, Project Scientist at Carnegie Mellon University

What kinds of datasets did teams use?

We provided two datasets (OxCGRT: COVID Policy Tracker, NYTimes: COVID-19 Data) for teams to explore. However in the spirit of open ended research and creativity, participants also had the option of using any other public dataset they liked and we did see several teams take advantage of it.

Describe the awards categories.

Track I: Best Storytelling/Data-Science Process

Clear hypotheses and assumptions
Exploratory data analysis
Problem solving
Comprehensive take-aways
Reproducibility

Track II: Best Prediction Model

Problem setup and metric definition
Quality of features
Explanation of choice of model
Model evaluation
Explainability and model interpretation

Track III: Best Interactive Visualization/Dashboard

Simplicity and ease of navigation
Choice of encodings and colors
Ease of understanding
Impact and take-aways
Documentation

Describe the winning teams’ projects below.

The Unpredictables won the prediction model category. This group investigated the impact of governmental policies on rates of COVID-19 infections in three states with the highest number of cases at that time (California, New York, and Pennsylvania).

Curious Duo won the storytelling category. This group focused on two states, Washington and Florida, for their analysis. The objective was to identify and collect tweets from the states, and identify the sentiment trends for the state-specific user and how this impacted the spread of COVID-19.

Data visualization had two winners:

Java’s Just Coffee visualization allows the user to interact and explore COVID-related data on the number of cases/deaths and policies on which governments have focused to counteract this pandemic. This visualization also allows the user to interact with how people have responded to COVID in the United States.

JiaLiDun did a visualization to show the effectiveness of governments’ policy responses towards the COVID-19 pandemic in different countries. This group looked at three different major categories of policies: containment and closure policies, economic policies, and health system policies. Within each category, there are different levels of stringency that were also taken into consideration.

Student Profile: Florencia Marcaccio

Hi Florencia! Tell us a bit about yourself.

I’m originally from Argentina. Before coming to the U.S., I worked as a Big Data Analyst for Telefonica, a telecommunications company. In this position, I developed Machine Learning models and KPIs to improve the quality of their services.

I was awarded a Fulbright Scholarship to pursue an M.S. in Data Science in the U.S., and I chose to come to the UW. In addition to being a graduate student, I’m currently a Data Science Intern with the Academic Experience Design and Delivery team at the UW-IT Department. I have the opportunity to work with an incredible team on projects that impact student persistence and pathways, building Data Science solutions to improve their academic experience.

Why did you choose the M.S. in Data Science program at the UW?

I chose the MSDS program at the UW because the curriculum is interdisciplinary and covers the breadth of data science. The program offers classes in all the areas that I was interested in learning and improving. Additionally, I really liked how the program is industry-focused, which is enhanced by its location in Seattle.

You finished your first year in the program. What has it been like so far?

I have learned a lot in my classes, from the fundamentals to more advanced topics. Even for topics that I had been exposed to previously, I’m learning the more in-depth “why” that I was missing before the program.

Another thing that has impressed me are the many career events organized by the program, where we are given tools to better prepare for the next step in our professional lives. In particular, the Technical Interview Workshop was an extremely helpful resource when preparing for summer internship interviews, and now for full-time positions.

My first year in the program was impacted by the COVID-19 pandemic. Fortunately, both the professors and the program staff were able to quickly adapt to the situation and deliver a strong remote learning experience.

You were awarded a Merit & Opportunity Scholarship. How has this scholarship impacted you?

The scholarship has positively impacted my experience in the program. It has enabled me to dedicate myself full-time to my studies, as well as take advantages of the many social activities and career events organized by the program and the university.

What tips do you have for incoming students?

I recommend building relationships with your classmates and senior cohorts and taking advantage of the career development events organized by the MSDS program and the UW.

MSDS Admissions Insights: 2020-2021 Essays

Happy first day of fall! The MSDS program recently launched our 2020-2021 application, and we revised our essay prompts for this year. Because we recognize that writing essays is one of the most challenging aspects of applying to graduate school, we want to provide you with some direct insights into what we are looking for when we read your essays.

Essay 1: Why UW MSDS?

We are interested in learning why you would like to join our data science program. A strong essay will go beyond generic responses, applicable to any data science master’s program, and will instead provide a clear and personalized motivation for wanting to attend our program. Before you start writing your essay, we encourage you to brainstorm the specific qualities that attract you to our data science program, and to identify how well the MSDS program aligns with your aspirations. This essay is your chance to show us that you are good fit for the MSDS program, and vice versa.

Essay 2: Data Visualization

Note that this essay prompt has multiple questions. Be sure to answer them all! The 500-word count is brief, so you will have to use good judgment about what aspects of the given data science visualization you choose to discuss. Our favorite thing about this prompt is that there is no one right approach to writing this essay. We are expecting responses that are as diverse and compelling as our applicants. However, an excellent essay will provide strong, detailed analyses of a data visualization rather than general reflections or a summary. We hope to see evidence of strong communication and critical thinking skills, and we encourage you to put those skills on display in your response to this essay prompt.

Essay 3: Overcoming a Barrier

We are asking you to tell one specific, discrete story about a time you overcame a barrier in your academic or professional life. The 350-word limit is short, so you will have to decide what level of context to provide. We recommend offering us just enough situational detail to set the scene while leaving yourself space to reflect on how you overcame the obstacle and what you learned about yourself in the process. Writing about a time you overcome an obstacle gives you the opportunity to implicitly convey some of the character traits and strengths that define you as a person.

One final piece of advice: the essays are an opportunity to share with the admissions committee who you are behind your transcripts and your resume, so take your time. Think carefully about the content and the quality of your essays. Please respond thoroughly but concisely to the prompts. Applicants are required to adhere to the word count.

Good luck! We look forward to reading your essays in January.

Advice for Reapplicants

Admission to the MSDS program is very competitive. The fact of the matter is that we simply cannot admit all of the qualified applicants we receive in a given year. Many applicants who are not successful wonder if they can reapply to the MSDS program during the next application cycle. The short answer is yes! If you were denied or waitlisted last year, we welcome you to apply again this year. Having applied in a previous year is not a negative factor in your application. We appreciate sustained interested in our program. We also admire your persistence and resilience in reapplying. In fact, each year, we offer admissions to some reapplicants who present compelling applications.

Though not required, we strongly recommend that you submit an optional essay outlining how you have improved your candidacy since your last application, as the admissions committee will be looking for changes in your qualifications. You can use this opportunity to highlight any additional coursework, professional experience, technical projects, or areas of personal growth that you have undertaken since you last applied to the MSDS program.

Additionally, we encourage you to approach your application with a fresh outlook and consider how you might better represent your strengths and accomplishments to the admissions committee this time around. You may want to revise your resume or reconsider your choice of recommenders. If possible, request a letter of recommendation from someone who can speak to your growth over the period since you last applied to the MSDS program.

Beyond your resume and recommendations, the essay prompts offer you the opportunity to distinguish yourself from the rest of the applicant pool. After reading more than a thousand applications this past year, we can tell you that thoughtful, well-written essays often make the difference between being admitted and being waitlisted. While essays can be one of the most challenging aspects of applying to graduate school, they can also be one of the best ways to make a compelling case for your admission.

In sum, reapplicants should make sure they enhance their application, rather than just submitting the same application. We cannot guarantee that reapplicants will be admitted to the program. However, in the past, several of our best students were admitted to the MSDS program after applying more than once. We look forward to reading a new application from you, and we are excited to see how you have grown since you last applied to our program. We invite you to review our 2020-2021 application requirements. As always, if you have questions about the MSDS program or the application requirements, please feel welcome reach out to uwmsds@uw.edu.

Student Profile: Vikrant Bhosale

Hi Vikrant! Tell us a bit about yourself.

I was raised in Mumbai which is the financial hub for India. It is also a densely-populated city with almost 19 million people. I completed my bachelor’s degree in electronics in 2000. Since then, I have been working as a software engineer. I moved to the U.S. in 2010 to work at Microsoft. During my 10 years at Microsoft, I found myself gravitating towards problems that involved analyzing and processing huge amounts of data. After working at Microsoft for a decade, I transitioned to working at startups, including one that focused on natural language processing and voice recognition. I currently work at a company called Sift. Sift uses machine learning and data science to provide digital trust and safety for businesses.

Why did you choose the M.S. in Data Science program at the University of Washington?

Apart from the fact that the University of Washington is renowned, I liked the fact that a lot of the curriculum’s focus is on honing the basic fundamentals of data science. This makes learning advanced techniques easier. I also liked the fact that it is a cohort-based program. The diversity of professional backgrounds in my cohort means that I get to learn from my fellow students and make lasting connections across industries. I also like the flexibility of the program. The part-time program enables me to earn my degree while working at Sift.

You recently finished your first year in the program. What was your favorite class? Why?

It is difficult to pick one course. I loved Statistical Machine Learning for Data Scientists. I liked the fact that the professor chose to teach us the basics and help us understand the fundamentals behind several machine learning techniques. The course empowered me and gave me the skills to teach myself any advanced technique in machine learning in the future. I also liked the way the professor designed the homework. Our assignments required us to explore concepts on our own.

How has the M.S. in Data Science program shaped your career outlook so far?

I find myself proposing more innovative solutions and new ways to present my ideas to my colleagues at Sift. The MSDS program has been a great career booster, and I have already used what I learned in to the program to take on bigger projects and opportunities.

What tips do you have for future students?

Make sure you understand the “why” behind your decision to earn your master’s degree in data science. This motivation will inspire you. If you have a family, talk to them and make sure you have their support. It would be impossible to work full-time and be successful in the program without the support of my family. If you are working full-time, make sure you schedule time for studying and completing homework assignments. Take advantage of the cohort experience. Your classmates will go on to work at very influential companies in the future. This is the best time to form a lasting connection with them.

Is there anything else you’d like to share about your experience in the program?

My experience in the program has been great. I like the fact that several guest speakers from industry come to our classes every quarter. The fact that they find it worthwhile to meet MSDS students speaks to the reputation of the program.

I also like the fact that the curriculum is rigorous and our professors have very high standards for students. This ensures a high-quality experience.

Another thing to note is that the cohort is extremely diverse with respect to demographics and professional experience. This makes it a very well-balanced program where you learn from different perspectives.

Incoming Student Profile: Meet Alison Gale

Hi Alison! Tell us about yourself.

I grew up in Virginia in a suburb of DC. I majored in Computer Science at Brown University, but outside of my major, I was very interested in Math and Economics. During my final semester, I took an Intro to Data Science class which helped spark an interest in the field. Outside of school and work, I really enjoy being outdoors so I can often be found hiking, running, or playing soccer.

Tell us about your professional background to date.

After graduating in 2014, I moved to Seattle to work as a software engineer at Google. On my first team, I worked on a product that was then called DoubleClick Search, but has since been rebranded to Google Marketing Platform. I focused on developing a suite of chart building tools that enabled customers to create reports detailing key metrics of their ad campaigns. This was my first professional exposure to the power of analyzing and visualizing large sets of data, and I really enjoyed learning about what features our customers needed to better understand their datasets.

Currently I’m working on Google Cloud, focusing on frontend development. Initially I worked on the user interface, but more recently I’ve been focusing on front end infrastructure. This includes things like how our app initializes, handling navigation between pages, and supporting migrations to the latest technologies. The main goal of my work is to improve the reliability and performance of the application.

What made you decide to become a data scientist?

Throughout my professional career, I kept finding myself drawn towards projects that involve analyzing and visualizing data. On my current team, I’ve driven many efforts to analyze the performance of our application. Identifying the slow parts of the application will allow us to focus our efforts to improve performance. Becoming a data scientist will provide me with more tools to analyze and improve performance for our end users.

Outside of work, I’m a big fan of women’s soccer. While there is extensive analysis of the men’s game, there is a lack of coverage and analysis of women’s soccer data. It would be awesome to apply data science techniques to analyze and visualize women’s soccer data.

What attracted you to the Master of Science in Data Science program at the University of Washington?

I was attracted to the fact that the program has a part-time option while still being an in-person program. I’m really looking forward to taking what I learn in classes and applying it to problems I’m facing at work. Additionally, the program puts a lot of work into keeping the content and curriculum relevant for work in industry. The special topics classes and capstone project sound like great ways to engage with industry professionals and learn practical skills.

What aspects of the program are you most looking forward to this fall?

It has been a while since I’ve been in school so I’m looking forward to being in an academic environment again. I loved math in college but I haven’t worked with it much in the last six years so I’m looking forward to diving back into topics like probability and statistics.

Student Profile: Matthew Rhodes

Name: Matthew Rhodes

Undergraduate Institution: Michigan State University

Undergraduate Major: Computer Science

Tell us a little bit about yourself.

Born and raised in Detroit, I come from humble beginnings. I discovered my love for computers early in life by playing video games on my mom’s PC and my Nintendo 64. After graduating from high school, I went on to pursue a bachelor’s degree in computer science from Michigan State University. GO GREEN! I interned at Clinc during my junior year of college and fell in love with machine learning. I decided that I wanted to learn more about data science so I made the choice to attend the MSDS program at the University of Washington. I make music in my free time and I like to meditate every chance I get.

Why did you choose the M.S. in Data Science program at the UW?

I knew I wanted to pursue a career using machine learning to solve business problems. The career switch to data science was a no brainer. I also knew that the University of Washington had a very rigorous program that would prepare me for an industry career. All of the companies in the area that partner with UW were the icing on the cake.

You are halfway through your first year in the program. What has it been like so far?

The coursework is simultaneously engaging and challenging. In addition to my classes, I’m doing research with Professor Bill Howe which takes up a lot of my time. The first two statistics courses are the toughest classes so far. My favorite courses so far are Data Visualization, Data Reproducibility and Data Management for Data Scientists.

You interned at Amazon last summer. Can you tell us a little bit about that experience?

My experience at Amazon was amazing. I interned with the AWS Ground Truth team. I learned so much about how research is conducted in industry. I also had the opportunity to apply different concepts, such as Bayesian statistics and convolutional neural networks, to industry problems. My team members always made me feel like I had a strong group of individuals I could reach out to for help. I was also lucky to have a great manager.

What tips do you have for incoming students?

I would give three tips to incoming students:

Save every assignment. A lot of the concepts we cover in class come up during interviews and on the job.
Find out what helps you with deal with stress. No matter your background there will be concepts introduced to you that you have never encountered before. Try your best. When things get tough, you should do something that helps relieve stress.
Find a friend in the program. It always helps to have someone you can talk to about lectures, concepts, interviews or just to hang out with.

MSDS Career Services: Q&A with Tori Gottlieb

Tori Gottlieb leads career services for MSDS students and alumni. She recently sat down for a Q&A about her experience running career services for the MSDS program.

Hi Tori! Can you tell us a little bit about yourself?

I’m originally from San Francisco and have been working in higher education since 2006. I started my career as an academic advisor, working with students in the social sciences, humanities, and arts at UC Merced. I’ve also advised students in Materials Science & Engineering at Stanford, and before coming to MSDS, I advised the Informatics program at UW.

I live in Lake City with two VERY opinionated little girls, two very grumpy cats, and one very enthusiastic Pit Bull mix. In my spare time, I love reading, writing, watching baseball, and singing along (poorly) to Beyonce and Taylor Swift.

How do you promote career readiness for MSDS students?

So much of finding internships and jobs, especially in the tech industry, is networking. Students need to have opportunities to meet employers, and also need to be prepared when those opportunities arise. This is where having a polished resume, a solid elevator pitch, and a professional outfit come in handy! My goal is to hold events or promote other resources around campus so students are as prepared as possible going into meetings and interviews with employers.

What are some current career events and initiatives the MSDS program offers that prospective students should know about?

This year, we had resume reviews, a technical interview workshop, information sessions with companies like Amazon and Zillow, and networking events like Dinner & Data Science, where students can connect with industry professionals and alumni. I’m looking forward to holding more events in spring, and ramping up during the big hiring season in autumn quarter so that we have a full slate of events to help our incoming class with their job hunt.

What is special about the career services that the MSDS program offers it students and alumni?

The opportunity to network across cohorts is a huge benefit for our program. Our alumni are engaged and want to give back to the program and the students in it, which is rare, especially at the graduate level. Additionally, we have strong industry connections from organizations like Microsoft, Facebook, and Seattle Children’s Hospital who are highly invested in the success of the MSDS program. The fact that students can come into a program and immediately network with industry professionals who want to see them succeed helps them to feel connected to the industry and supported from day one.

The MSDS program is lucky to call Seattle home. What advantages does Seattle provide students and alumni?

Seattle is a hub of technology, much of which relies on data science, and thus our students and alumni. Data scientists are in high demand here, whether they opt for private industry or the public sector. There’s never a shortage of opportunities here, which allows professionals to advance their careers without having to uproot their lives. Additionally, Seattle is a city with great, young energy. There’s a lot of cultural activities and events, fabulous food, and beautiful natural scenery. It’s a wonderful place to live and work.

What advice would you offer incoming students?

You’ll have to hit the ground running when you get here in September, so show up to events, and use your resources! There are career events and resources specifically for MSDS, but there are also a ton of events going on around campus through the UW Career Center, Career Center @ Engineering, and the eScience Institute that can help you level up your career. Try to do as much as you can – there’s no telling where your next opportunity will come from.

It’s Almost Time to Apply!

Believe it or not – 2020 is almost here! This means that the January 6^th deadline to apply to the M.S. in Data Science program is just around the corner. Many of you will spend the next month working on your applications. Because we know that writing admissions essays is not the most exciting way to begin the New Year, we have some tips to help make that task a little easier.

Make a list. Start by creating a list of everything you need to do between now and January 6^th. Do you need to finish updating your resume? Have you uploaded your transcripts to your application? Write down everything you need to do and then prioritize your tasks.

Create a schedule. After you’ve written down a list of everything you need to do and prioritized the most important tasks, you should try creating a schedule to ensure that you’re able to complete everything by the application deadline. If possible, try to schedule some time to work on your application during a part of the day or week when you’re not exhausted from the rest of your schedule. You want to be able to work on your application with a fresh mind.

Crank out an essay. You’ve set aside time to work on your application, but when you sit down at your computer, you can’t seem to find the right words to write your admissions essays. Does this sound familiar? You’re not alone. Applicants often tell us that writing essays is the most difficult part of the application process. There’s a lot of pressure to put the best of yourself on display in your application, and the desire to write something “perfect” can hold you back from actually writing. The first step you can take to get the writing process started is to write down whatever comes to mind. You can then edit and tweak your writing until your essays better represent what you want to say to the admissions committee.

Take some time for yourself. If you start to feel overwhelmed, you should give yourself permission to put your application aside for the day and do something that brings you joy, such as watching a movie or spending time with your friends. Carving out time for yourself will help you recharge and come back to your application with renewed focus.

Ask for help. Don’t be afraid to reach out to your colleagues, classmates, professors or your university’s writing center to ask for help with your application. Whether it’s help reviewing your resume or editing an admissions essay, don’t hesitate to ask for help if you need it.

If you have questions about the application process, be sure to contact us at uwmsds@uw.edu. We hope you all have a Happy New Year, and we look forward to reading your applications in January!