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Most graduate school applicants will agree – writing application essays is the most difficult and stressful part. While writing your essays may seem daunting, this is your chance to differentiate yourself from other applicants with comparable qualifications. It is likely that dozens of applicants, if not more, have academic records and professional backgrounds that are similar to yours, but strong essays may put you ahead of the competition.

Before you write:

• Read the questions. Before you begin writing, take the time to carefully read the questions in each essay prompt. Make you sure you understand everything that is being asked of you.
• Reflect on the purpose. Ask yourself: What purpose does this essay serve? For example, Essay 1: Why UW MSDS? should give the admissions committee insight into your short-term and long-term goals, why you want to attend our program specifically, and what kind of career you envision after graduation.
• Create an outline. Drafting an outline will help you organize your thoughts and identify the building blocks of your essay. An outline will also help you better understand what you are trying to communicate to the admissions committee.

Writing your essays:

• Write a compelling introduction. Do not start with “My name is…” or “I am applying because…”. Your name or the fact that you are applying are not the most interesting things about you! You should choose to begin your essay, for example, with an anecdote, a question, or an attention-grabbing statement, but make sure it is directly relevant to your essay.
• Don’t repeat your resume. If you do talk about your work experience, do so briefly and only to make a larger point.
• Show, don’t tell. Offer examples, stories, or descriptions when writing your essays. For example, rather than just stating that you want to work as a data scientist in the healthcare sector after graduation, describe what you find the most compelling about the industry or job role.

Revising your essays:

• Edit, edit, edit! Give yourself enough time to write multiple drafts of your essays. One of the most common mistakes applicants make is to leave too little time for writing their essays.
• Proofread. Proofread your essays carefully and try reading your essays out loud to help catch any awkward phrasing. Ask your classmates, instructors, or colleagues to proofread your essays. Make sure your work is free of spelling mistakes and grammatical errors.
• Stick to the word limit. It might be tempting to ignore the word limit, but we want to see that you can get your point across clearly and concisely.

Last, but not least, make sure you do not duplicate the content of your essays. Each essay has its own purpose. There is no reason to repeat the same ideas in your writings.

Your resume is your best chance to highlight your major academic and professional accomplishments in one place. To help you showcase your strengths, consider these tips on what to include in your resume and how to organize it:

• Your resume should be focused. The admissions committee reviews hundreds of applications each year. The resumes that grab their attention are concise and convey an applicant’s academic and professional history immediately. This means that you should avoid being overly descriptive. Bullet points are more effective than long sentences.
• Follow the one-page rule: Unless you have 10+ years of professional experience, your resume should not be more than one page. Ask yourself: What are the most important things I want the admissions committee to know about me? Put the most important content on one page. Everything else is extraneous. You should choose to include professional experience that is directly related to data science but consider leaving out activities or experiences that are not indicative of your potential to succeed as a data scientist.
• Your resume should be well-organized.Your resume should include clearly defined sections with headings. The most important and relevant sections should be at the top, while the least important should be at the bottom. Some common resume sections include: education, employment, research, publications, extracurriculars, and technical skills. Please note that you do not need to have all these sections in your resume. For example, if you do not have any publications (and most of our applicants do not have publications), you do not need to have a publications section. We recommend arranging the entries in reverse chronological order.
• Your resume should look professional. Use an easily readable font, stick to black and white, and avoid including pictures and images. We recommend submitting a .pdf resume so the format is not altered when we download your application.
• Your resume should be free of errors. Your resume should be free of spelling mistakes and grammatical errors. Ask faculty, classmates, or colleagues to review your resume.

We get asked all the time – “Why are there so many prerequisite courses?” The short answer is that students need to have a strong quantitative and technical foundation to succeed in the M.S. in Data Science program. Our curriculum is rigorous, and the fast-paced classes assume you have a strong math background which includes calculus I-III and linear algebra. Our classes also involve substantial computer programming assignments which assume you have mastered the concepts covered in two introductory programming courses. The admissions committee also looks at the grades you earned in your prerequisite courses to evaluate your potential for graduate studies in data science. Strong grades are one indication that you have the preparation and motivation to excel in the program.

Another question we get asked all the time – “What if some of my prerequisite grades are not so good?” If this situation applies to you, do not despair! There are measures you may take to offset this obstacle:

• Write an optional essay. If your prerequisite grades were affected by serious circumstances outside your control, such as an illness or a death in the family, you may wish to take advantage of the optional essay to explain the situation to the admissions committee.
• Repeat a class. Another way to offset a poor grade in a prerequisite course is to take it again and earn a higher grade the second time around.
• Take an extra class. What if you did poorly on your Intro to Computer Science II midterm and it dragged your final grade down to a C? You can take a more advanced course in computer science and demonstrate your drive to succeed. While a grade awarded in a more advanced course will not affect your previous grade, it will reveal your initiative and show that your prior performance is not indicative of your capabilities.
• Show improvement over time. What if you earned a disappointing grade in calculus I and went on to earn higher grades in calculus II and III? An upward trend in your grades is an indication that you are capable of growth and improvement over time.

As a final note, prerequisites must be completed before the application deadline. The M.S. in Data Science program receives a high volume of applications, and we cannot offer one of the limited seats to someone who may not have the background necessary to thrive in the program.

Countdown to the Application Deadline

The January 18, 2019 deadline to submit your application to the M.S. in Data Science program is quickly approaching. As the deadline gets closer, the admissions staff will post advice on steps you can take to strengthen your application.

Application Advice Part 1: Letters of Recommendation

We pride ourselves on the intimate cohort experience we offer students. However, because we are a selective program, we must turn away hundreds of qualified applicants each year. Strong letters of recommendation are often the key to admission as they can provide valuable insight into an applicant’s intellectual abilities and personal qualities.

Choosing your recommenders. Two letters of recommendation are required; three are preferred. For applicants with professional experience, we recommend at least one academic reference and one professional reference. It is usually most helpful to submit academic letters of recommendation from instructors or research advisors in quantitative or technical disciplines who know you well and can speak to your ability to succeed at the graduate level. Do not seek out letter writers with prestigious titles only because you think their status will impress us. We want to see sincere letters from faculty or professionals with whom you have worked directly.

Guiding your recommenders. To help guide your references, you should educate them about the M.S. in Data Science program; let them know why you are applying to our program specifically; and show them your resume and essay responses. By taking these steps, you will position your references to write well-informed letters of recommendation that complement the other components of your application and strengthen your application overall.

Our main goal is to identify applicants who are extremely well prepared for academic and professional success in data science. To that end, we use a holistic review process to evaluate factors that we know have a bearing on success, including academic excellence, intellectual curiosity, technical and quantitative abilities, leadership, communication skills, creativity, and critical thinking. Our evaluation of these factors is based on: 1) your academic record, 2) your professional experience, 3) your motivations and preparation for graduate studies in data science, and 4) your personal qualities that will enable you to succeed as a data scientist.

1. The academic portion of our review is comprised of the following:

• Transcripts. We look at your field of study, overall GPA, major GPA, grades in prerequisites, grade trends over time, and other courses you completed.
• Academic letters of recommendation. The most informative letters come from instructors or research advisors in quantitative or technical disciplines who have evaluated you in more than one course and can provide specific examples that speak to your ability to succeed at the graduate level.

This portion of review is more important for students who just graduated from another program because they are less likely to have significant professional experience.

2. The professional portion of our review is comprised of the following:

• Resume. Your resume must concisely outline your education, work history, internships, publications, and extracurricular activities. Your resume should enable us to identify your unique strengths and experiences.
• Professional letters of reference. The most informative letters are from supervisors or colleagues who know you through direct involvement and can speak about the impact of your work, as well as about your key abilities and strengths.

3. Your motivations and preparation for graduate study are reflected through the following:

• Essay1: Why UW MSDS? The best responses describe short-time and long-term plans, how an M.S. in Data Science will help you achieve your goals, and why the UW MSDS program is a good fit for you.
• Transcripts. Strong grades in prerequisite courses are an indication that an applicant is prepared for graduate study in data science.

4. The final area of consideration is your personal qualities. We look for traits that are highly sought by industry employers, including communication skills, critical thinking, leadership, and creativity.

• Essay 2: Data Visualization. The best responses offer insight into your ability to critically and creatively think about and discuss data science visualization.
• Essay 3: Leadership. The best responses are authentic and original and provide detailed insight into your unique leadership style.
• Letters of recommendation. We look for insight into your personal qualities by evaluating what your letter writers say about your unique attributes and skills.

An application does not have to be perfect to be successful. We understand that you may have faced adverse circumstances in the past. This is why we offer you the opportunity to write an optional essay that may explain any gaps in your application or that may contextualize your academic background or professional history.

Due to the high volume of applications we receive, it is not possible to provide feedback on application materials, and reviewers cannot provide feedback on why an application was rejected. All admissions decisions are final.

As the January 18, 2019 application deadline gets closer, the admissions staff will post advice on our blog on steps you can take to improve your application.