Data Science

 

 

CAREER TRACKS

Data Science

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Whether you are just starting to explore, or have committed to a career in data science, the Data Science Career Track offers tips, tools, and action steps to help you move forward.

The first step tis to know yourself well by identifying your interests, skills, strengths, and values. This information will give direction to your career search activity. We can help you reflect on your story and create career exploration goals.

 

Interests: Knowing your interests can help you identify possible organizations and positions/titles that might be a good match for you.

Values: Knowing your values will help you to identify potential work environments and organizations that match your passion(s) and core belief(s).

Skills & Strengths: Knowing your skills and strengths can help you identify roles and tasks that you might be well-suited for you.

The University Career Center offers these tools to help you learn about your interests, values, skills and strengths, and we’re happy to help you use these tools to identify career ideas for you.

Want some coaching on where to begin? Not sure which Career Track is right for you?  Make an appointment on Handshake for “Exploring Options.” We’ll be happy to help you explore your interests and skills and think about career ideas and options.

Before your appointment, we strongly encourage you to:

 Explore Your Track

What is data science?

Uses automated methods to analyze massive amounts of data and to extract knowledge from them (thereby transforming data into information and converting information into knowledge)

Interdisciplinary field for extracting knowledge or insights from data in various forms

Discipline that is emerging at the intersections of social science, math, statistics, computer science, and information design

Why is data science important and popular?

Data is increasingly cheap and ubiquitous and employers are busy digitizing their old content while also creating new content (e.g., web logs, mobile devices, sensors, instruments, transactions, etc.).

Employers are accumulating new data faster than they know what to do with it. One big question for employers is how to use their data effectively — not just their own data, but all data that's available and relevant to their industry, business, and customers.

IBM estimates 90% of data in the world today has been created in just the past two years.

National Geospatial Intelligence Agency says it collects more data in one day than is found in the entire Netflix library.

New technologies are emerging to organize and make sense of all this data.

Check out this video with several vignettes by researchers at U-M about how they're working with big data: Video

Is data science a job FUNCTION or an INDUSTRY?

Data science is primarily consider a job function -- because it describes WHAT you do at an organization. (A job industry is WHERE you do the work, at what kind of organization.)

Sometimes, data science is also considered an industry -- because there are organizations that only do data science, typically for clients in other industries.

Data science is actually a fairly new term for employers. Most employers have always had some kind of data to analyze and employees whose role is to sort, sift, categorize, analyze and understand what the data means and how it can be used to impact the organization and its customers. So this function has had many names or labels over the years and is only more recently called data science. This means when you explore the data science function or when you search for jobs and internships in the data science field, you may need to search for opportunities with a variety of labels, including data science, data scientist, data analyst, business analyst, statistician, quantitative analyst, etc.

And because there is so much more data available in the world today and new tools are required to make sense of this proliferation of data, this field is growing very fast!

How is data science used or applied across industries and businesses?

  • Prediction (predict a value based on inputs)
  • Recommendations (e.g., Amazon and Netflix recommendations)
  • Classification (e.g., spam or not spam)
  • Pattern detection and grouping (e.g., classification without known classes)
  • Anomaly detection (e.g., fraud detection)
  • Recognition (image, text, audio, video, facial, …)
  • Actionable insights (via dashboards, reports, visualizations, …)
  • Automated processes and decision-making (e.g., credit card approval)
  • Scoring and ranking (e.g., FICO score)
  • Segmentation (e.g., demographic-based marketing)
  • Optimization (e.g., risk management)
  • Forecasts (e.g., sales and revenue)

Find several different examples of how U-M researchers are using data science in this Video.

What is the general “process” of data science? What does data science involve?

  1. Acquire or collect raw data and store it somehow/somewhere (data can come from multiple sources -- public or private -- and the appropriate data for the task at hand must be identified and gathered)
  2. Clean and integrate the data (data from multiple sources needs to be extracted, moved, transformed, integrated, and stored in a way that’s optimized for decision making)
  3. Investigate the data to determine which tools, models and algorithms should be used in order to provide accurate, reliable, significant results (garbage in equals garbage out)
  4. Apply models and algorithms, e.g., machine learning, statistical modeling, artificial intelligence, etc.
  5. Communicate results (to colleagues in your department or other departments, and/or to business decision makers in your organization or experts outside your organization)
  6. Make decisions based on results
  7. Repeat to solve a new problem. 
  • Computer skills, statistics and/or math is a great place to start.
  • Technical skills are increasingly important given the vast amount of data available today and the pace at which more data is being generate. Technical skills might include: R, SAS, Python, MatLab, SPSS, SQL, Hive, Pig, Spark, Java, Ruby, Hadoop, Excel, Tableau.
  • Analytic thinking ability and a focus on details will help you solve numerical problems, organize numbers, and categorize concepts. If you are curious about how stats affect the lives of people, understand spatial relationships, and can base your decisions on sound logic, as opposed to emotion, you may have the internal bearing for a career in numbers.
  • Communication skills are critical since data science is a team sport! Data scientists must be able to communicate with other data scientists to bring all kinds of expertise to bear to solve tough problems. And they must also be able to communicate with colleagues or experts in other functions to fully understand the business challenges and customer needs. And finally, they must often be required to communicate complex issues and methods to non-data and non-technical team members.  
  • Intellectual versatility is essential, since data scientists often collaborate with experts in varied fields such as business, law, medicine, finance, urban planning, etc. Data scientists must be flexible and innovative and continually look at situations from different angles to devise the best solutions.

Employers tell us these are the most important competencies a student should have -- skills that you may have gained in all kinds of experiences in academics, extracurricular activities, or at work -- that you can transfer to this field.

Given data science is a new label and a growing and changing field, there is a wide variety of titles that are being used across industries for entry-level roles. You might see or search for these, among others: data scientist, data science analyst, business analyst, statistician, researcher, developer, quantitative analyst, quantitative researcher, machine learning engineer, database engineer, data architect, data infrastructure engineer, etc.

What’s most important, is not the title, but the actual description of the position. Read it carefully to understand what the position requires and check the “Required Qualifications” and “Preferred Qualifications” that most postings include to help determine if you want to apply.

It’s important to understand current industry trends as well as hot topics in the field. At the exploratory stage, understanding where the industry is going, employment outlook, geographical nuances, etc., will help you determine whether the current and developing status of the industry is one that truly interests you and/or is feasible to pursue based on your specific circumstances.

Also, if you are actively job searching, it will be crucial to understand who the key players in the industry are, what challenges are faced by large and small organizations, which problems these organizations are trying to solve, and more.

So make sure to add a few of these media sources to your regular reading list:

Where can I learn more?

Campus Resources

Student Organizations

General Online Resources

Armed with some background information and ideas, connecting with professionals can offer next-level insights and answer more specific questions.

Get Help

Want some coaching around navigating your Career Track? Interested in talking with a career coach about your interest in this field?

3,2,1

This short exercise will help you clarify your question(s) and identify strategies to answer your career exploration questions.

3  - What are three take-aways from your exploration of this Career Track?

2  - What are two questions that you have or what are you questioning now?

1  - What is one specific action step you plan to take in order to answer your two questions?

Was this helpful?

 Launch Your Job or Internship Search

Every company/organization does their recruiting and hiring in different ways. You should consider these factors when launching your search:

  • Handshake is where companies post opportunities specifically for U-M students, so every student use Handshake!
  • Many companies recruit in early fall -- September and October -- for internships and full-time positions. They may or may not come to campus. Be sure to check Handshake frequently for positions.
  • If you don’t find what you’re looking for in the fall, there are still many companies that are still recruiting in late fall (November) and early winter (January, February, March) and sometimes even later. So keep looking!
  • It can be expensive for companies to send their employees to campus. Just because a company isn’t visiting campus, they are likely still very interested in hiring U-M students -- so keep checking Handshake, or reach out to an alum you find in UCAN (these alums have volunteered to help you!) or on LinkedIn.
  • You should also make a habit of checking the websites for companies that interest you. That is the best way to know when those companies are hiring.

The U.S. Bureau of Labor Statistics indicates that the vast majority of positions (70-80%) are found through talking with friends, family, colleagues, peers, and acquaintances -- NOT by searching job sites. Networking is super important! You can use these tools to search, find, and contact alums and others who can share their insights and expertise as you search for positions. Through talking with people who are doing things that interest you at organizations that interest you, you will get your name out there and you will potentially be remembered and contacted when the right position opens up!

Job/Internship posting sites are a not a primary recruiting tool for companies/organizations, particularly those looking for recent grads, and therefore should be only a small part of your job search efforts. In fact, data reveals that only a small percentage of positions are secured through job boards.

A much larger percentage of positions are uncovered and eventually secured through personal outreach to, and active engagement with, various professionals in the field, through what many people generically refer to as “networking.” This means you need to: (1) identify a list of companies that interest you, (2) do your research about those companies, and (3) connecting with real professionals in those companies.

Does this mean that you should not bother looking at job boards?  Absolutely not! Job boards can be helpful to get a sense for the most active companies/organizations, variety of positions, job descriptions, salary ranges, etc. (step #1 and 2 above). However, mining job boards should not be the only strategy in your search for a position. You are likely to be more successful if you take a multi-pronged approach: use Handshake, do some networking, and check job boards like these:

For the basics of a great resume, check out our resume resources page. And you can find information about cover letters on our University Career Center website too.

Here are some example data science-related resumes. And even more examples.

And check out these additional tips:

How to prepare for your interviews:

Research: Research the company, its products/services, its clients, current big projects, etc. You can find this on the company website. Also Google the company the night before your interview so you know if any big news has been released about the organization or its leadership or employees.

Reflect: Reflect on your skills and experiences and how they apply to this specific position and company/organization. The more you know about the organization and position you’re applying for, the easier it will be for you to connect your experiences and skills to the employer’s needs, thus improving your chances of getting hired.

Rehearse: Practice makes perfect! Search the web for “typical data science interview questions,” or “standard technical interview questions,” and then ask your friends and family to pose some of those questions to you so you can practice your answers. And/or schedule a mock interview the University Career Center.

U-M Career Center Interview Resources

Get Help

Want some coaching around navigating your Career Track? Interested in talking with a career coach about your interest in this field?

3,2,1

This short exercise will help you clarify your question(s) and identify strategies to answer your career exploration questions.

3 - What are three take-aways from your exploration of this Career Track?

2 - What are two questions that you have or what are you questioning now?

1 - What is one specific action step you plan to take in order to answer your two questions?

Was this helpful?

 

 

Photo Credit: Tiago Gerken