Careers Archives - University of Wisconsin Data Science Degree

Data Science Webinar Highlights the Current Job Market and Future of the Field

March 11, 2022 By Brandon Arbuckle Leave a Comment

The University of Wisconsin Data Science program hosted a career webinar breaking down the future of the field. The discussion, which was facilitated by UW Data Science Academic Program Manager, Jennifer Cox, and Senior Success Coach, Michael Paul, examined the current job market and career outlook for the data science industry.

The panel featured experts representing different areas of data science, all of whom were either UW Data Science graduates or Advisory Board members for the program:

Missy Wittmann, Data Solution Engineer at American Family Insurance
Brent Ferrier, Principal Data Scientist at Oshkosh Corporation
Rebecca Wells, Data and Analytics Catalyst at Trane Technologies
Carolyn Challoner, Information Technology Instructor at Fox Valley Technical College

The four panelists shared career knowledge on a wide range of topics, including what skills you need to succeed in the field and the importance of companies cultivating an inclusive workplace.

What Employers Expect From Today’s Data Science Professionals

To work in the data science industry, possessing business knowledge is just as important as having a strong understanding of programming languages and visualization tools. With soft skills like strong communication and collaboration, data scientists can clearly and effectively explain projects to other departments in their organization.

“As a data scientist, being able to straddle that line between business and IT is expected,” Ferrier said. “That’s why communication is so key, because you have to live in two worlds.”

Companies are searching for workers who can bring best practices to the current business through real-time analytics and personalization. In fields such as consulting and manufacturing, Ferrier said there has been a sudden rise in companies seeing the value in advanced analytics.

“There are tons of jobs out there for the jack of all trades, the generalist who can go end-to-end on a full project,” he said.

The industry is also beginning to see a specialization of roles in larger companies. Wells predicts a job market where teams consist of “unicorn” employees, or workers who are hard to find but have unique and specialized skills that can greatly improve an organization.

“Instead of expecting the world out of one person, companies are starting to recognize that it does behoove them to specialize across their roles and build unicorn teams instead of that magical single unicorn,” she said.

Creating An Inclusive Workplace for a Better Tomorrow

The panel also discussed the importance of diversity and equity in the workplace. Many companies have made an effort toward inclusivity to ensure everyone has a voice.

Wittmann said American Family Insurance in particular has an Inclusive Excellence department, along with employee resource groups to support women in technology, different ethnicities, and veterans.

“The more diverse your teams are, the stronger the teams will be because you have different perspectives and ways of looking at things,” Wittmann said.

Companies like Trane Technologies have begun to implement anonymous hiring by removing candidates’ names from their applications. This allows hiring managers to make decisions based on an applicant’s skills while avoiding gender bias and other forms of hiring discrimination.

Remote positions have also created more job opportunities for graduates and working professionals across the country. In the case of Oshkosh Corporation, Ferrier said the company hired remote workers in Arizona who otherwise wouldn’t have been able to join the Wisconsin-based organization. The company has promoted tuition assistance as well for workers in other departments who want to acquire the core knowledge necessary for a data scientist role.

Getting Your Foot in the Door

The job market for data science has become an employee’s market in recent years, which makes the career outlook for the field a promising one.

For people looking to transition into the field or are searching for their first job after graduation, a solid résumé and proper networking can go a long way to set you apart from other applicants. The panel suggested using online job boards such as LinkedIn, which is how Challoner found her current job at Fox Valley Technical College.

“Because of my background, résumé, and data science degree, my inbox blew up,” she said.

The panel recommended broadening your search when looking for data science openings, since job titles can vary from company to company.

“Organizations use many different terms,” Wittmann said. “You may not necessarily see the words ‘data science.’ They may be looking for some type of data engineer or whatnot.”

When searching for jobs, shadowing other employees within your organization or reaching out to a company’s hiring manager for an informational interview can help you find the right fit.

“How [hiring managers] describe what they want from their employee versus what they put in a job description are often very different things,” Wells said.

Creating an online portfolio is another option to demonstrate your data science knowledge and skill set. With the UW Data Science program, students can tailor their final capstone project to showcase a collection of work with potential employers.

Furthering Your Career With a Data Science Degree

The emergence of online degree programs has given a wider range of students more access to an education. This includes Ferrier’s wife, who went back to school online while she was raising their children as a stay-at-home mother.

“I find that a lot with our data science students,” Cox said. “They’re working a job or several jobs, have kids, and are fitting this in with their regular life.”

With the field of data science constantly evolving, the panel stressed the value of being a lifelong learner.

“Getting that data literacy can help you in any career, not just one that is a traditional data science career path,” Challoner said.

While online courses on LinkedIn Learning and Coursera are useful for supplemental learning, a master’s degree program will allow you to establish credibility and fully develop the technical skills needed in data science. The 100 percent online UW Master of Science in Data Science is a suitable option for those looking to advance their careers while having the flexibility to support any lifestyle.

Want to learn more about the UW Data Science program? Talk with an enrollment adviser by emailing learn@uwex.wisconsin.edu or calling 608-800-6762.

The full webinar is available below:

About the Panel

Missy Wittmann | Data Solution Engineer at American Family Insurance

Over the past 36 years, Missy has worked several positions within the data space at American Family. Her passion for data led her to the Wisconsin Data Management Association (DAMA), where she was elected president and held board positions at the international level. Missy joined the UW Data Science Advisory Board to discuss the needs of the industry and ensure the program is aligned with the workforce.

Brent Ferrier | Principal Data Scientist at Oshkosh Corporation

A graduate of the UW Data Science program as well as an Advisory Board member, Brent is responsible for the strategy and implementation of data science initiatives at the Oshkosh Defense business unit at Oshkosh Corporation. A lifelong learner, Brent also has an MS in Cybersecurity from Georgia Tech and an MBA from UW-Parkside. His educational background in supply chain management and data regulations has further complemented his data science career.

Rebecca Wells | Data and Analytics Catalyst at Trane Technologies

Rebecca develops the data and analytics vision at Trane Technologies, where she has worked for 20 years. This includes how the organization is structured as well as the process of turning analytics into data science. A graduate of the UW Data Science program, Rebecca was drawn to the field after developing an interest in visualization storytelling. She has since been able to combine her statistics and programming skills with her pre-existing business background.

Carolyn Challoner | Information Technology Instructor at Fox Valley Technical College

A 2018 graduate of the Data Science program, Carolyn is working to train the next generation of data scientists in Fox Valley Tech’s newly formed Data Specialist degree program. She has previously worked at IT and global marketing jobs throughout the Fox Valley region of Wisconsin, including Plexus, Miller Electric, and Kimberly-Clark.

Michael Paul | Senior Success Coach at UW Extended Campus

Michael has supported UW Data Science students since 2013, with a career in higher education spanning 15 years. As a success coach, Michael helps students improve their connectedness to the program, their home campus, and online resources.

Jennifer Cox | Academic Program Manager at UW Extended Campus

Jennifer has worked in research, project management, and information science at the University of Wisconsin System since 2007. She became the Academic Program Manager for the UW Data Science program after completing the MS in Data Science degree program in 2018. Jennifer’s greatest data science interest relates to the use of data in business strategy and decision making.

Women in Data Science: Why They’re Critical to the Data Science Workforce

February 1, 2021 By Kaitlynn Martin Leave a Comment

According to a 2020 National Center for Women and Information Technology (NCWIT) report, women make up 57 percent of the overall workforce, but only 26 percent of computing and math occupations. This is the first post in a new series from the University of Wisconsin Master of Science in Data Science to highlight the importance and need for women in the data science industry. The Women in Data Science series will feature the crucial roles women take on within the computing field. Additionally, the series will highlight students, faculty, and program leaders of the UW Master of Science in Data Science program who share their experiences as women in the data science industry, and offer advice for breaking into the male-dominated profession.

Though there is a lot of work yet to be done for women’s inclusivity in data science, there are a few statistics that are worth celebrating. In this first blog post, we take a look at hiring trends, strengths women bring to data science roles, and how women can stay competitive in the industry.

Positive job growth for women in data science

First, let’s break down the percentage of women in computing occupations. A 2022 Burtch Works study found that the number of women in data scientist roles has reached 24 percent, an increase from 2018 where just 15 percent of data scientists were women. The same study also found that the largest percentage of women in data science roles are in the entry-level individual contributor category.

These numbers indicate a possible increased presence of women in the field, along with the beginning of women-led leadership. However, there is still a great need for growth in the computing field for women and other minority groups, especially in the mid-to-senior level leadership roles historically held by men.

Diversity reduces bias and improves work quality in organizations

The 2020 NCWIT report also found that women in the computing workforce are more racially and ethnically diverse than men in the field. As shown in the graphic below, there is a higher percentage of women with African American, Black, and Asian descent who hold computing roles than men.

Graphic from the 2020 National Center for Women and Information Technology (NCWIT) report showing the racial composition between women and men with computer and information sciences (CIS) bachelor degrees in 2019. At all degree levels, the women earning CIS degrees are more racially and ethnically diverse when compared to men.

Workplace diversity plays a large role in reducing bias, which is especially critical in the computing field. According to a Boston Consulting Group (BCG) study, “Interpreting causal relationships and correlations in large data sets requires subtlety, and both humans and machine learning algorithms can occasionally ‘see’ patterns that lead to spurious, biased, or even downright dangerous conclusions.” A diverse team—whether by gender, race, ethnicity, sexuality, age, disability, and more—allows for various opinions and experiences to be considered when tackling projects.

Workplace diversity also improves the quality of work. A study done by Columbia University researchers Bo Cowgill and Fabrizio Dell’Acqua found that prediction errors were correlated within demographic groups, especially by gender and ethnicity. More diverse teams will reduce the chance for compounding biases, resulting in fewer errors.

While diversity reduces bias, it also leads to higher innovation revenue. Because people with different backgrounds and experiences often work through problems in different ways to come up with a variety of solutions, the odds that one of the solutions will be a financial success increases.

A chance to make a difference in the world

There are a number of ways that data science impacts the world, and according to the BCG study, this is important to women. The study found that 73 percent of women entering the data science and machine learning field prioritize tangible impact in their career choice, compared to 50 percent of men.

Graphic from the BCG study comparing the percentage of women and men STEM students who have a preference for working on applied problems with tangible impact. On average, the results show that women, including data science majors, place a higher emphasis on applied, impact-driven work than men do.

However, many women do not see data science roles as fulfilling their career goals. According to the same study, half of women instead see the data science field as “theoretical and abstract, focused on manipulating code and data with low impact and, by implication, low purpose.” Although there are probably many factors that contribute to this negative perception, the study cites the largest is the companies themselves. It is argued that companies are not communicating the important and meaningful role that data scientists play within their business, deterring applicants.

Data science is utilized in all industries, ranging from renewable energy, to healthcare, to public safety. Within these industries, the work of data scientists often has a tangible impact on the technology that will shape our future. In order to recruit women to data science roles, companies should showcase how data science is at the heart of effective decision making by highlighting the specific problems that data scientists solve in their organization. Bottom line, all industries stand to benefit from the experiences, perspectives, and skills that women in the field have to offer, and they must work toward better communicating with women in data science.

How to stand out in a competitive industry

If you have an interest in data science and want to stay competitive, enrolling in a data science master’s degree program may be the right next move for your career. You can start by asking yourself a few important questions before taking the leap, including which program is the right fit. Diversity in faculty expertise and perspectives is a critical component to consider before applying to a program. It’s important that you can relate to and feel supported by program faculty and staff.

UW Data Science strives to be inclusive

The gender gap within data science roles and leadership positions continues to be a pressing issue. Every organization can benefit from prioritizing diversity, equity, and inclusion initiatives within their internal culture and hiring processes. This requires conversations at all employment levels, from institutions granting degrees to CEOs discussing promotions to leadership positions.

The UW Master of Science in Data Science program aims to create an inclusive environment for all. If you’d like to learn about graduates of the program, read about Venmathi Shanmugam, a modeling and simulations engineer working at the Veterans Affairs office, and Halee Mason, who is a lead data scientist at Cloud9 Esports, Inc. You can also learn about our diverse faculty members.

Have questions about the courses, tuition, or how to apply? Talk with an enrollment adviser by emailing learn@uwex.wisconsin.edu or calling 608-800-6762.

Data Science vs. Data Analytics: The Differences Explained

October 21, 2020 By Kaitlynn Martin Leave a Comment

If you’ve ever scanned job websites for roles in data, you’ve probably seen listings for data scientists and data analysts accompanied by job descriptions that sound quite similar. While the two fields are interconnected, data science and data analytics vary in scope, responsibilities, and goals.

One overarching similarity is that professionals in both roles use big data to solve problems and create improvements in an organization. The biggest difference, however, is how they interact with data.

Data scientists often work with vast stores of raw data, working as investigators to create ways to analyze and model that data using statistical analysis and heavy coding. The goal of their work is to uncover the questions the data can answer. Data science often lays the foundation for further investigation.

Data analysts leverage the modeling of the data scientist to create actionable and practical insights using a variety of tools. The work of data analytics involves using organized data to apply findings immediately.

Skills and Tools You’ll Need in Data Science and Data Analytics

There are some distinct differences between skills needed for data science and data analytics careers. However, there is also some overlap.

data science vs data analytics skills comparison in venn diagram — *A Venn diagram highlighting the similarities and differences between the skills needed for data science and data analytics careers.*

A Deep Dive into Jobs and Responsibilities

When looking at job opportunities, it’s important to not only look at the job title, but also the responsibilities, as the titles can overlap between data science and data analytics.

Some common job titles in the data science field are:

Data scientist
Data analyst
Data architect
Data mining engineer
Machine learning engineer
Hadoop engineer
Predictive modeler

Data science responsibilities often include: identifying opportunities for investigation, collecting data, predicting trends, cleaning and validating data, and communicating. To learn more about data science careers, read here.

Some common job titles in the data analytics field are:

Data analyst
Business analyst
Database analyst
Market research analyst
Operations analyst
Business intelligence analyst
Tableau developer

Here are some common data analytics responsibilities: exploratory data analysis, data cleansing, statistical analysis, and developing visualizations.

Salary Expectations

According to the U.S. Bureau of Labor Statistics, the median annual wage of a data scientist is $100,910. The 2022 Burtch Works Salary Report found that data scientists make on average between $90,000 and $275,000 annually, depending on experience level and managerial responsibilities. According to the same report, analytics professionals (referred to as artificial intelligence professionals in the report) can make between $105,000 and $275,000 on average, also depending on experience level and managerial responsibilities. For a more in-depth look at salary information, visit here.

Educational Advantage

One way to increase your salary is to earn an advanced degree, which is common in both fields. The Burtch Works study found that 93 percent of the data scientists and analytics professionals surveyed held an advanced degree.

A graph from the Burtch Works study showing the educational levels—including bachelor's, master's, and Ph.D.— of predictive analytics professionals and data scientists. — A graph from the Burtch Works study showing the educational levels—including bachelor’s, master’s, and PhD—of data scientists and analytics professionals (referred to as AI professionals in the report).

The 100 percent online UW Masters in Data Science prepares students for both data science and data analytics roles. Students not only learn technical skills they need to succeed, but they also gain knowledge in effective project management, leadership, and communication.

Want to focus on data analytics? UW Extended Campus offers a virtual Data Analytics Bootcamp that can be completed in just 24 weeks. You will graduate from the program ready to apply your knowledge in the professional world.

Discover

The University of Wisconsin offers an online, 36-credit Master of Science in Data Science degree program. This data science master’s program will teach you how to harness the power of big data using the latest tools and analytical methods. Start your journey today.

Explore

Curious about what you’d learn in UW Data Science courses? See the curriculum.

Ask

Have questions about the UW Data Science program? Contact an adviser at 608-800-6762 or learn@uwex.wisconsin.edu.

Should Data Scientists Take a Machine Learning Course? Absolutely, and Here Are the Best Options

March 8, 2018 By UW Data Science Blog Team Leave a Comment

Machine learning, a subfield of artificial intelligence, is huge right now. The web search for this topic has exploded in recent years, and interest in it continues to rise today. According to Adnovum, machine learning has the potential to positively impact society and is “now being used in several sectors, including but not limited to customer service, logistics, healthcare, travel, retail, financial services, manufacturing, etc.”

So, what does this mean for data scientists? If you haven’t taken a machine learning course, should you now? Our answer: Yes. Today, machine learning is considered a prominent component of data science. Taking a machine learning course is going to help you stay at the forefront of your field as well as gain greater insight and efficiency as a data scientist.

The Basics of Machine Learning

Just in case, here’s a quick refresher on machine learning. At a high level, the goal of machine learning is to enable computers to learn on their own and predict outcomes through algorithms, such as neural networks, that mimic our own decision-making abilities.

How does machine learning actually work? A computer engineer or data scientist will develop an algorithm that allows a computer to spot patterns in large data sets. For example, if you wanted a machine to be able to identify an apple from a banana, you might gather data on color and density of different pieces of fruit and feed that information into the machine. Based on this data set, the computer will be able to accurately identify—on its own—whether a fruit is an apple or a banana. And as it repeats a task, the computer will continually learn and optimize its performance.

The power of machine learning is that it allows computers to make predictions, extract insights, or take actions without the need for human intervention.

Why Should Data Scientists Learn Machine Learning Now?

A data scientist gathers data from multiple sources and applies machine learning, predictive analytics, and sentiment analysis to extract critical information from the collected data sets.

It’s true that machine learning is only one piece of data science, but it’s an important one. According to Forbes, machine learning is the field of AI which today is showing the most promise at providing tools that industry and society can use to drive change.

data science skills_machine learning course — Image Source: drewconway.com

“Machine learning is a constantly growing and changing field,” says Dr. Abra Brisbin, assistant professor of mathematics at UW-Eau Claire and a faculty member for the UW Data Science program. “Understanding machine learning lets data scientists stay at the forefront of their industry, because they can adapt to those changes and use machine learning techniques as tools rather than black boxes.”

This cutting-edge technology enables data scientists to do things that were not possible five years ago. Because of this, many companies and organizations are rushing to hire talented professionals who can employ machine learning to gain valuable information, make processes more efficient, and develop new products.

Some real-world machine learning examples:

Netflix uses machine learning algorithms to recommend movies to customers.
Facebook employs machine learning to create audience interests for ad targeting.
Autonomous vehicles have machine learning technologies that integrate data points from sensors to operate the vehicle.
Twitter uses it to crop photos so that the most interesting part of the photo is displayed.
Ticketmaster has machine learning-generated dynamic pricing, which is based on how well concert tickets are selling at any given moment.
Retail websites use machine learning models to predict which products to recommend and when to give out discounts.
Cybersecurity organizations can use machine learning to detect fraud, prevent, phishing, and defend against cyberattacks.

Oh, and one more reason to have specialized knowledge in machine learning: In the United States, it pays well.

Online Machine Learning Courses

Two ways to learn machine learning are:

An online machine learning course
A data science degree program

Which is best for you? Base your decision on your learning preferences, career goals, and on how much you already know about programming and statistics.

Single Online Machine Learning Courses

If you have a solid foundation in both programming and statistics—and a casual interest in the topic—an online machine learning course could provide sufficient preparation for getting started implementing algorithms and building models.

Data science blogs, including KDNuggets, almost always recommend one course for machine learning newcomers: Andrew Ng’s machine learning course on Coursera. Coursera also offers a number of other beginner courses about AI, deep learning, and other similar topics. For a data science-specific course, Udemy offers an “Introduction to Machine Learning for Data Science.”

These are great resources for those who want a broad introduction to machine learning, data mining, and statistical pattern recognition. The downside of these courses? They are broad introductions. These single, online courses lack the structured, in-depth instruction and guidance of a degree program.

Data Science Degree Program

This brings us to a data science degree. This is your best choice if you want to become the machine learning specialist at work. Through in-depth training and instruction, a degree program will solidify your understanding of statistics and the theoretical underpinnings of machine learning. It will teach you practical implementation of algorithms and model-building in popular programming languages, such as Python and R.

However, be careful when selecting a program. Not all data science degree programs have coursework that teaches machine learning.

The online, 12-course University of Wisconsin Master of Science in Data Science is different. Machine learning and predictive analytics were woven into many of the courses when the program was created, because the faculty and industry advisory board recognized them as eventual must-have skills for data scientists.

For example, the first course, DS 700: Foundations of Data Science, introduces students to machine learning. After that, DS 740: Data Mining covers machine learning concepts and practical skills in detail; DS 745: Visualization and Unstructured Data Analysis gives students in-depth understanding of machine learning for text analysis; and DS 775: Prescriptive Analytics emphasizes applications of machine learning to inform the business decision-making process.

UW graduate Christian Acosta learned machine learning in the Data Science program. He said this:

“I’m finding that the specific projects I chose are applying to my current work in marketing analytics with Kohl’s. Specifically, I have used topic modeling and various supervised machine learning to analyze past performance. In the future, I hope to use our data to build shopping propensity models for our consumers.”

Also, don’t forget that to be great at machine learning, you need strong strategic thinking and business intelligence skills. UW Data Science helps students hone these kinds of soft skills.

A master’s degree is the more expensive way to learn machine learning, but it’s also the most worthwhile. If you have a Master of Science in Data Science on your resume, it will hold more weight than a single course. Plus, many of our students receive employer reimbursement, so we recommend checking if this is an option for you.

Take the Next Step

Have questions about the UW Data Science capstone, curriculum, tuition, admissions, and more? An enrollment adviser would be happy to discuss these things with you!

Call: 608-800-6762
Email: learn@uwex.wisconsin.edu

Or, to explore the Master of Science in Data Science program, start here.

Business Analyst vs. Data Analyst: Which Role Is Right for You?

October 24, 2017 By UW Data Science Team Leave a Comment

As a data lover, you’re at a professional crossroads. Do you stay in your current—and familiar—role, or do you shift and move in an entirely new direction? More specifically, how do you choose between becoming a business analyst vs. data analyst?

Both roles would allow you to capitalize on your love of “all things data,” and both would appeal to your affinity for problem solving. Both positions would also pair well with an in-depth knowledge of data science. However, the roles of business analyst vs. data analyst require different skillsets and focuses, making it necessary to choose your path carefully.

What Is a Business Analyst?

A business analyst identifies technology solutions to solve oftentimes amorphous business problems. They work in a variety of industries including healthcare, transportation, manufacturing, finance, banking, software services, and telecommunications. The International Institute of Business Analysis defines a business analyst as an “agent of change,” who identifies and executes new opportunities for businesses to capitalize on technology. Business analysts often specialize in one of the following roles: business systems analyst, systems analyst, functional analyst, service request analyst, or agile analyst, depending on one’s area of interest. For example, a functional analyst helps organizations use and integrate their technology with other systems. A service request analyst handles user inquiries and system enhancements.

Successful business analysts possess strong foundational data science skills as well as an ability to develop strategic business and project plans, identify key performance indicators, create use-case scenarios, and engage and communicate with stakeholders at all levels of the organization. They must be able to take a holistic view of a business problem or challenge and work with various individuals to get the information necessary to drive IT changes. Those transitioning into a business analyst role may have previously worked as software developers or project managers.

A business analyst’s daily responsibilities may include reviewing data about current work habits, interviewing users to identify technology challenges, preparing documents that outline detailed functional requirements needed to address those challenges, creating flowcharts for programmers to follow, designing and executing test scripts or scenarios, and managing change requests related to the project.

What Is a Data Analyst?

Data analysts, on the other hand, use specialized analysis techniques and tools to determine how businesses can use data to make more informed decisions. This may sound very similar to the role of business analyst; however, data analysts work more directly with the data itself. They’re responsible for identifying important business questions, applying the appropriate statistical techniques to harness structured and unstructured data, and performing complex data analysis to extract useful information and develop conclusions. Data analysts are also responsible for protecting an organization’s data and ensuring that all data repositories produce consistent and reusable data. Data analysts and business analysts work in many of the same industries and particularly those that rely on technology. Data analysts and data scientists are also increasingly employed in other industries such as agriculture, travel, food, oil, and auto insurance—each of which has only begun to tap into the power of big data.

Successful data analysts are those who can extract and analyze big data as well as present results to executive management or departmental managers. This requires a unique balance of technical data knowledge and business acumen—a skillset professionals can gain or hone in a data science degree program. Those transitioning to a data analyst role may have previously worked in fields such as accounting, healthcare information management, database administration, computer science, or business.

A data analyst’s daily responsibilities may include culling data using advanced computerized models, removing erroneous data, performing analyses to assess data quality, extrapolating data patterns, and preparing reports (including graphs, charts, and dashboards) to present to management.

Business Analyst vs. Data Analyst: 4 Main Differences

Although business analysts and data analysts have much in common, they differ in four main ways:

Overall responsibilities. Business analysts provide the functional specifications that inform IT system design. Data analysts extract meaning from the data those systems produce and collect. Data scientists can often automate the business analyst’s tasks and may be able to provide some of the business insights as well.
Salary. Data analysts earn an average salary of $72,691, according to Indeed. Business analysts earn a higher average annual salary of $86,206. Although business analysts tend to make more, professionals in both positions are poised to transition to the role of “data scientist” and earn a data science salary—$100,910 on average.
Skillsets. Business analysts require data science knowledge as well as skills related to communication, analytical thinking, negotiation, and management. Data analysts require similar skills with a more in-depth focus on technical data manipulation.
User interaction. As project facilitators and managers, business analysts often have more direct interaction with systems users, customers, system developers, and others than data analysts do. That’s because business analysts may frequently interview people to learn more about how technology can be improved to help business processes. They work collaboratively with others throughout the duration of a single project. Although data analysts may consult initially with internal subject matter experts to identify important data sets, the bulk of their work is done independently.

How to Prepare for a Business Analyst or Data Analyst Role

If you’re thinking about transitioning to a business analyst or data analyst position, consider earning a Master of Science in Data Science online from the University of Wisconsin. The 12-course curriculum focuses on building both technical data science skills and “power” skills such as leadership, communication, and project management—skills that are beneficial in either position. Plus, a data science master’s will help you advance to a high-paying, in-demand data science role. To learn more, see our page “What Do Data Scientists Do?”

UW Data Science Students Stories

How to Make Your Data Science Resume Stand Out

A Modern History of Data Science

March 22, 2017 By UW Data Science Team Leave a Comment

If you’re a data scientist, you probably recognize the names DJ Patil and Jeff Hammerbacher. Not only are both often credited with popularizing the term “data science,” but they also exemplify the modern data scientist—that is, one who applies his or her data-savvy expertise in any setting that demands it, including healthcare, e-commerce, social media, and journalism—just to name a few.

Patil, the chief data scientist at the United States Office of Science and Technology Policy, boasts an extensive resume that includes stints at LinkedIn, Greylock Partners, Skype, PayPal, and eBay. [Read more…]

How to Make Your Data Science Resume Stand Out

February 16, 2017 By UW Data Science Team Leave a Comment

When it comes to landing the data science job of your dreams, you only get one opportunity to make a first impression. You need to make it count.

How can you do that? It’s simple: A succinct and targeted data science resume and cover letter. When written effectively, both documents set you apart from other candidates and convey exactly what you can offer and why.

Polish your data science resume and cover letter using these tips so you stand out from your competitors.

6 Sections to Include in Your Data Science Resume

Writing a resume can be a daunting task, especially if you don’t know where to begin. That’s why we compiled these six components that every data science resume should include:

1. Contact information

You know to include your full name, credentials, address, phone number, and email address. If you have a professional presence on LinkedIn, Twitter, GitHub, or another social media site, consider providing links to those as well. Make it as easy as possible for hiring managers to get in touch with you.

2. Professional summary

Your professional summary should be no more than three or four sentences stating the following:

Years of experience
Primary areas of data science expertise (e.g., data mining, data warehousing, or data visualization)
Type of position you seek (e.g., data analyst, data scientist, or data engineer)
Industry in which you hope to apply your data science knowledge (e.g., healthcare, retail, government, or finance)

The professional summary should tell your story—where you’ve been and where you’re headed next. Think of it as your elevator speech—that is, the way in which you’d describe yourself and your abilities in 30 seconds or less. You’ll likely have different versions of this speech depending on the type of data science position for which you’re applying as well as the industry. This is your chance to grab a hiring manager’s attention and encourage him or her to keep reading, so make it worthwhile.

3. Core competencies

This section should include a bullet-point list of your data science strengths (e.g., statistical analysis, data interpretation, and communication) and explain generals tasks you’ve completed to achieve each competency. For example, with data communication, perhaps you could include these tasks: Write, format, and present technical prose; facilitate data-informed discussions; and help non-technical professionals act on data findings.

Refer to data science course outlines to help build this portion of your resume. That’s because most data science course descriptions also include core competencies and learning objectives that you can use as a foundation.

4. Education

List each degree and institution as well as the date of graduation. A Master of Science in Data Science proves to employers that you have the wide range of knowledge and skills needed to do any data science job. Consider listing any courses that are particularly relevant to the job for which you’re applying. For example, if you’re hoping to land a data scientist job in biomedical research, consider listing any data science ethics courses in which you applied various analyses using an ethical framework. Tie your education experience as much as possible to the job you seek.

5. Technical expertise and certifications

This section should include a bullet-point list of specific data science skills you’ve honed as well as tools with which you’ve worked. If you earned a data science master’s degree, list that you have hands-on experience with data science tools such as SQL Server and Tableau and languages such as R and Python. Don’t list tools for which you only have a theoretical knowledge. Hiring managers want to know that you have experience working with these tools and will likely ask you for examples during an in-person interview.

6. Work experience

Don’t worry about keeping your resume to one page—that’s an outdated notion. If you’re a mid-level or above professional, you’ll likely need two pages to list all work experience. And never skip significant job experiences, even if they’re unrelated to the job to which you are applying. A gap of time between jobs is a red flag to hiring managers. For unrelated job experiences, don’t include as much detail on your resume as you would for other jobs.

Tailor each job description to the one you’re applying for. For example, if the position you hope to land requires an in-depth knowledge of SQL, tailor your previous job descriptions to highlight your use of SQL.

Also focus on key accomplishments in each previous position by listing a select group of three to five projects and briefly describing your role in each project’s success. Use action words (e.g., created, managed, coordinated, or led) to describe your participation.

Make Your Data Science Resume Keyword-Friendly for Hiring Managers

A strong data science resume also incorporates the keywords for which hiring managers are searching. This will vary from job to job, and a good rule of thumb is to incorporate as many of the same terms and phrases used in the job posting directly into your resume.

You’ll also want to incorporate as many general data science keywords as possible. For example, Towards Data Science lists these must-have resume skills and keywords for data science that you could reference if it makes sense to do so. Don’t force yourself to include these terms. Use this list as a point of reference, and be cognizant of it as you construct your skills, competencies, and previous job descriptions.

4 Things to Consider When Writing Your Data Science Cover Letter

Follow these four tips when constructing your cover letter:

Personalize your love for data science and why you want to work in this field. Tell a brief story about why you love data and what you hope to accomplish in the data science role that you seek.
Explain why you want to work for that specific company. Look at the company’s “about us” page, and respond directly to the values the company promotes and why these values are important to you as well.
Expand on projects listed in your resume. What skills did you hone, and what did you learn that you can apply going forward in a future data science position?
Remember the unrelated job experiences discussed above? The cover letter is your chance to highlight those experiences and explain how skills you gained in a different field will transfer to this job. This is what will make you truly stand out as a candidate.

3 Bonus Tips to Boost Your Application

Following are three other tips to consider:

Keep the layout simple. Use a straightforward layout and font for your resume and cover letter. Let the content do the talking. And never go below an 11.5 font size.
Proofread for grammar and punctuation. Data science is all about the details, and you don’t want to ruin your chances of landing an interview by misspelling a word or forgetting a crucial punctuation mark.
Invite feedback from a friend or colleague. Are there areas for improvement? Is the resume clear and succinct? Don’t hesitate to look at other data science professionals’ resumes as well. This includes LinkedIn profiles—especially those profiles of individuals who already work for the company to which you’re applying. What language do they use, and what skills do they promote? Use this as a guide for developing your own stellar and unique resume and cover letter.

Considering a Master of Science in Data Science? University of Wisconsin offers this degree online through six campuses. Learn more about the program and which UW campuses offer the degree here.

Data Scientist Jobs: What a Search on LinkedIn Reveals

December 15, 2016 By UW Data Science Team Leave a Comment

A quick search on LinkedIn reveals more than 312,000 data scientist jobs nationwide. More than 220,000 of these jobs were posted in the last month. More than 19,000 were posted in the last 24 hours alone, suggesting that those who pursue a data science degree can literally (and figuratively) bank on job security.

Data scientists in demand

Data scientists work in virtually every industry, including healthcare, computer science, information technology, retail, marketing, manufacturing, transportation, communication, education, insurance, finance, science, security, government, nonprofit, and law enforcement just to name a few. Companies such as Facebook, Amazon, IBM, Kayak, Capital One, The New York Times, and others continue to clamor for those with data science training who can drive business intelligence using voluminous and complex data.

What exactly are these and other companies looking for? Following are just a few snippets taken verbatim from data science job openings posted recently on LinkedIn:

Use big data tools (e.g., Hadoop, Spark, H2O, and AWS) to conduct the analysis of billions of customer transaction records.
Establish scalable, efficient, and automated processes for large-scale data analyses that will tie into production systems.
Use predictive analytics and machine learning to create new products or drive business decisions.
Apply expertise in quantitative analysis, data mining, and the presentation of data to see beyond the numbers and understand how our users interact with both our consumer and business products.
Interpret, document, and present/communicate analytical results to multiple business disciplines, providing conclusions and recommendations based on customer-centric data.

Finding the right data scientist training

Employers want to hire data scientists who don’t just know the technology but who know what to do with it; agile communicators who are well-rounded can remain flexible as new challenges and business needs arise. If you’ve thought about pursuing a career in data science, look for a program that provides not only a solid foundation rooted in statistics and computer programming but also one with instruction in economics, business management and communication.

Aligning your education with the top 12 most in-demand data science skills

The interdisciplinary nature of the University of Wisconsin Data Science program caters directly to the types of skills that employers seek. Many employers require a master’s degree in data science or a comparable discipline (e.g., statistics, computer science, or mathematics) as well as the ability to perform these 12 most-common tasks:

Create and manage simple databases in Access and SQL Server.
Write and execute SQL statements to retrieve and manage data.
Analyze data to solve basic analytics problems using Excel and R.

You’ll learn all of this and more, including how to use other well-known data science tools (e.g., PowerBI and Access) in the course, Foundations of Data Science.

Use Python and R to analyze real-world data.
Use an application programming interface (API) to collect real-world data from social media.
Clean and format data for analysis.

You’ll learn all of this and more in the course, Programming for Data Science.

Use tools and software such as Hadoop, Pig, Hive, and Python to compare large data-processing tasks using cloud-computing services.

You’ll learn this and more about big data analysis in the course, Big Data: High-Performance Computing.

Help non-technical professionals visualize, explore, and act on data science findings.

You’ll master technical, informational, and persuasive communication in the course, Communicating about Data.

Create effective visuals to maximize readability, comprehension, and understanding of complex datasets.

You’ll learn this and more in the course, Visualization and Unstructured Data Analysis.

Use data and predictive analytics to inform the decision-making process.

You’ll learn this and more, including optimization, decision analysis, game theory, and simulation in the course, Prescriptive Analytics.

Transform findings from data resources into actionable business strategies.
Explain how data assets can be used to develop competitive advantage.

You’ll learn this and more about how to obtain decision-making value from an organization’s data assets in the course, Data Science and Strategic Decision Making.

In addition, the University of Wisconsin partners with an industry advisory board dedicated to helping students bridge the gap between classroom learning and real-life data science challenges. Their continual input helps ensure course content meets today’s employer needs in this rapidly evolving field. This makes for a smooth transition into the workforce.

Giving employers what they need and want

Upon completion of the University of Wisconsin master’s in data science program, individuals are prepared to:

Identify organizational questions for which data science can provide answers.
Collect and manage data to devise solutions.
Select, apply, and evaluate models to solve data science tasks.
Interpret data science analysis outcomes.
Effectively communicate data science-related information in various formats and for various audiences.
Value and safeguard the ethical use of data.
Transform findings from data resources into actionable business strategies.

Increase your value to employers seeking savvy, well-rounded data scientists by earning a University of Wisconsin master’s degree in data science. To learn more, download a program guide at https://datasciencedegree.wisconsin.edu/.