UW Data Science Team, Author at University of Wisconsin Data Science Degree

UW Grad Becomes Manager of Data Analytics at Overstock.com

June 29, 2018 By UW Data Science Team Leave a Comment

manager of data analytics Patrick Anthony is excited. As the new data analytics manager at Overstock.com, he’s responsible for building machine learning and deep learning solutions to further the company’s marketing capabilities. He is also hiring and training a team of advanced analytics professionals who can contribute to this effort.

“I am excited most about the prospect of using the latest techniques and technologies in machine learning and deep learning to contribute original work to the field of marketing. At our company, my team and I have a plethora of resources to do cutting-edge work in the field.”

To make the most of his role, he has applied all the data science knowledge he gained from several years of consulting and a master’s degree in Data Science from the University of Wisconsin.

His journey into data science

It all started as a hobby really. Early on, Patrick was interested in games—from puzzle games to action-adventure video games to sports. That initial interest evolved into more complex pursuits in college.

As an undergraduate student, he filled his schedule with upper-level math, statistics and probability, and computer programming courses. He took a specific interest in game theory, agent-based modeling, and evolutionary economics. He wanted to move beyond the traditional paradigm of classical economics and contribute to the evolutionary perspective of choice, systems, and organizations.

After graduating with his bachelor’s degree, he became a data analytics consultant for Seattle-based companies such as Microsoft, Amazon, and Tableau Software. He was quickly hooked. “Solving quantitative problems in that fast-paced consulting world energized me,” Patrick says.

Why a master’s degree?

While consulting, Patrick enrolled as an online UW Master of Science in Data Science student. “As a data analyst, I identified specific business needs. But I wanted to go deeper in the data science field to gain additional skills in machine learning and prescriptive analytics for marketing.”

Eventually, he decided to leave consulting to take a full-time position with Tableau, a data visualization company. He helped develop data science strategy and solutions to improve the impact of marketing, sales, and customer success programs. “I used machine learning and prescriptive analytics to identify value for the business.” This high-impact work earned him official recognition by Tableau and a company award honoring his efforts.

Putting new data science skills to work

Patrick graduated from the UW Data Science program in 2017. He says the rigorous curriculum grounded in computer science, math and statistics, management, and communication prepared him to become a full-fledged data scientist and leader.

Since he started at Overstock.com in March 2018, Patrick has used his knowledge of statistics, machine learning, artificial intelligence, data mining, and natural language processing daily.

He credits these data science courses as having the most immediate impact on his career:

Big Data: High-Performance Computing
Data Mining
Prescriptive Analytics
Visualization and Unstructured Data Analysis (e.g. natural language processing)
Data Science and Strategic Decision-Making

In particular, the program helped teach him to distill vast stores of complex, unstructured data into actionable insights and improved decision-making. These skills are critical for a data scientist at Overstock.com—and are sure to help him continue to lead and innovate in the future.

What’s next?

Start exploring the UW Data Science program. Have questions about the courses, tuition, or how to apply? Talk with an enrollment adviser by emailing learn@uwex.edu or calling 608-262-2011.

Take a Look at a UW Data Science Course: Foundations of Data Science

May 25, 2018 By UW Data Science Team Leave a Comment

“What are the UW Data Science master’s program courses like?”

We often receive this question from prospective students. So, we created this post to give you an inside look at the first data science course in the curriculum, “DS 700: Foundations of Data Science”—and to answer some of the questions that may be on your mind.

What do I learn in the course?

“DS 700: Foundations of Data Science” introduces you to data science and its importance in business decision-making. The course provides an overview of commonly used data science tools, such as R, and SQL Server, along with assignments on descriptive and predictive analytics. There is a focus on statistics, programming, ethical perspectives, and strategic deployment of analytics to lay the foundation for data science applications.

At the end of this course, you’ll be able to:

Comprehend fundamental concepts in Data Science and Analytics.
Understand fundamentals of SQL and apply them to Relational Databases.
Use R Studio for programming, using packages, debugging and knitting .Rmd files.
Effectively utilize packages from Tidyverse for R.
Read/Write data of various formats using R.
Create effective visualizations using ggformula.
Perform linear and logistic regression, interpret and graph the model.
Perform exploratory data analysis with dplyr package in R. / Perform data cleaning, management and manipulation with R.
Use conditional statements, loops and control flow logic in R.
Interpret p-values

This course features lessons that consist of lectures, readings and other media, assignments, discussion prompts, quizzes, and a final project.

Introduction to fundamental concepts in Data Science and Analytics
Introduction to SQL and relational databases
SQL Join and conditional flow statements
Advanced SQL Server operations (Transact SQL)
Intro to R Studio for programming, using packages, and knitting .Rmd files
Intro to debugging in R studio, linear and logistic regression, and graphs
Implement graphs in R, perform exploratory data analysis with dplyr
Implement joins in R, use control flow logic, manipulate datasets, and implement imputation
Implement functions in R, advanced debugging, good coding practice
Write and debug code involving control flow logic
Interpret p-values, solve complex problems using R
Demonstrate ability to synthesize knowledge and skills

What are the lectures like?

After you enroll in “Foundations of Data Science,” you can log in to the learning management system to access all course content. The lectures are hosted in Storybook+ media player and contain rich media content—slides, animations, videos, and instructor narration. You can listen and replay lectures as many times as you wish.

Some lessons have video content featuring interviews with real-life data scientists about their work and the current industry landscape. Other videos show the professor demonstrating vital skills that data scientists use on the job, including how to perform a K-means cluster analysis or work in SQL server.

What type of assignments do I complete?

There is a mini project and a final project that encourage you to practice and demonstrate the concepts you learned in the course. You might be asked to create SQL queries for various scenarios using the sample database. You will use your R programming skills honed during the course to perform analytical operations and derive inferences.

Here’s an example of the mini project:

What else do I do in the course?

Learning material. The professor for this course picks selections from textbooks, articles, or videos for students to watch. Sometimes, you are asked to reflect on this material in an assignment or discussion prompt.

Discussion posts. A lesson may include a graded discussion prompt, which gives you an opportunity to learn from your peers and contribute your own ideas to the group. For these, you craft an initial post and reply to other students’ posts.

Quizzes. Most lessons include a quiz of 25 to 30 questions covering that week’s course content, readings, and videos.

What technology do I use in this data science course?

You use MS SQL Server, R Studio, and Microsoft Office (Access, Excel, Word, PowerPoint). All of these software applications are on the virtual desktop platform, which you have access to as a student in the UW Data Science program.

Who developed the course?

Dr. Rajeev Bukralia, a former instructor at UW-Green Bay, originally developed the content for “DS 700: Foundations of Data Science.” The course was revised by instructors Praneet Tiwari and Abra Brisbin. The course is facilitated by instructor Jae Hoon Choi. Faculty work with an instructional design team and industry advisory board to ensure that the course remains cutting-edge and aligned with employer needs.

Do students in the course interact?

Yes. Students interact and share ideas through graded discussions in the learning management system. Also, data science students can collaborate, ask questions, and have general, non-assessed discussions through Piazza, a web-based forum.

How much do I do in one week?

Generally, you need to complete one lesson per week. You have seven days to complete readings and other learning materials, lectures, assignments, and discussion posts. (If you take the course during summer term, the timeline is accelerated, and you may complete multiple lessons per week.)

This data science master’s curriculum is as intensive as any University of Wisconsin program—on campus or otherwise. Some students put 20 hours of work into one course each week. But that number varies widely depending on how much experience they bring to the program. Although the program requires a serious time commitment, the flexible, online format allows you to study early in the morning, late at night, or whenever works best for your schedule, making it ideal for those who work full time.

Have questions about “DS 700: Foundations of Data Science,” the rest of the curriculum, how to apply, home campuses, and more? Our enrollment advisers can help.

Call 608-262-2011 or email learn@uwex.edu.

Why Your Smart Summer Checklist Should Include a UW Data Science Degree

March 13, 2018 By UW Data Science Team Leave a Comment

The first day of spring is approaching fast, and you know what that means. It’s time to start thinking about summer.

Your smart summer checklist should include plenty of SPF and water. But here’s another item to add to your list: An online University of Wisconsin Data Science master’s degree.

We know what you’re thinking, whoa, whoa, whoa. That’s a big leap. But summer is a great time to enroll in your first course. By starting your education this summer, you’ll be able to ease into your online learning experience and sign up for a course that has no prerequisites, such as “Data Warehousing” or “Ethics for Data Science.”

And by taking advantage of summer semesters, you can cut six months off of your total time to your degree. In this program, you must complete 36 credits to graduate—summer is a valuable time to blast through more credits and start benefiting from your master’s degree sooner!

Summer term starts May 29. Registration is open now.

Summer courses run: May 29 – August 17
Preview week (when enrolled students can explore the online learning management system) starts: May 22

Please see the UW Data Science Course Schedule page for information about specific courses.

Next step: Apply now

If you are not already enrolled, your first step will be applying to the UW Master of Science in Data Science. Click here to learn more about the application process. If you have questions, please contact a UW enrollment adviser at 608-262-2011 or learn@uwex.edu. They can walk you through the process.

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 $70,246, according to Indeed.com. Business analysts earn a slightly higher average annual salary of $75,575. Business analysts tend to make more, but professionals in both positions are poised to transition to the role of “data scientist” and earn a data science salary—$113,436 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

5 Big Data Industries You Should Be Watching Closely

September 11, 2017 By UW Data Science Team Leave a Comment

Big data is making big waves in many industries. For example, the airline you flew last month probably analyzed data to ensure your safety. Your favorite chain restaurant looked at big data to create that new specialty item on the menu.

Industries of all shapes and sizes are starting to tap into the power of big data. This bodes well for savvy data scientists who can turn this data into actionable insights. Here are five industries that are being transformed by big data.

Agriculture

Data science is the “next revolution in sustainable agriculture,” according to an article recently published in Ag Professional. That’s because it plays a critical role in helping farmers increase crop yields using the same number of acres, enabling them to meet the growing population’s agricultural needs. By measuring variables such as temperature, wind speed, and rainfall, data scientists help farmers predict potential outcomes as well as make decisions that reduce the impact of field variability and improve yield.

Farmers are also using data analytics to proactively manage dairy production. For a real-world example of this, see the following video, which is viewed and discussed by UW Data Science students in the course DS 745: Visualization and Unstructured Data Analysis.

Travel

“It would be almost impossible to overstate the transformative potential of big data to the travel industry,” writes Thomas H. Davenport in a report titled “At the Big Data Crossroads: Turning Towards a Smarter Travel Experience.” To inform his research, Davenport interviewed 21 different travel companies to learn more about how they use big data to their advantage. For example, KAYAK uses data analytics to enable flight price forecasting that predicts whether the price of a flight will go up or down in the next seven days. It also provides a statistical confidence level behind these predictions. Marriott Hotels uses data analytics to predict the optimal price at which to fill its rooms.

Airbnb uses machine learning to detect host preferences and compute the likelihood that relevant hosts will want to accommodate a guest’s request. Then, Airbnb serves up likely matches more prominently in the search results.

Mark Ferguson, a management professor at the University of South Carolina, told CIO.com that cruise lines usually collect a substantial amount of customer-related data, because they track onboard spending habits over the entire trip. By offering a discounted base price to customers who frequently spend extra money aboard the ship, these companies likely see higher profits overall.

Airlines have also begun to use data science for a variety of purposes. For example, by analyzing data created by jet engines and sensors that monitor the environment (e.g., temperature, humidity, and air pressure), airlines can predict when various parts of a plane are likely to fail so they can take preventative maintenance actions. Following are several other ways in which airlines are starting to use big data:

Identify profitable new routes by analyzing customer flying patterns
Avoid accidents and delays by analyzing the geo-location of storms and other severe weather conditions
Enhance customer service by analyzing a variety of customer data, including travel itineraries, social media, and frequent flyer status to improve front desk and on-board services. Southwest, United Airlines, and Delta are also using data analytics to improve the customer experience, according to the Fortune article “For the Airline Industry, Big Data is Cleared for Take-off.”

Food and Dining

Restaurants are also starting to tap into the power of big data to drive menu changes, improve services, identify new location opportunities, and more. In a report titled, “Big Data and Restaurants: Something to Chew On,” the National Restaurant Association encourages restaurants to take advantage of data analytics. The association says the data from a variety of systems, including point-of-service (POS) (e.g., sales by time, size of party, and menu items), accounting (e.g., payroll expenses, credit card sales versus cash, and gas or electric bills), and employee scheduling is a “vein of gold just waiting to be mined.” Other sources of data to mine include OpenTable, Facebook, Twitter, Yelp, TripAdvisor, Foursquare, Urbanspoon, or Instagram.

Some restaurants are using technology with their POS systems to gather information about customers (e.g., whether someone is a new or repeat customer, what they ordered, what they tipped, and how long they were at the table) and create profiles that include favorite drinks and food so they can target promotions and loyalty programs accordingly.

The association report also features various restaurants that have already taken the leap into the world of big data. For example, Chicago-based Levy’s restaurants use big data to understand the correlation between sporting events and food and beverage purchases. Panera tracks guest purchases and habits through loyalty cards and then leverages this data with primary marketing research and third-party data to guide its brand strategy, drive new customer acquisition, retain existing customers, and assist in real estate planning.

Oil

The rising cost of extraction and the turbulent state of international politics are two of the reasons why the oil industry has begun to rely on data analytics to drive business decisions, according to an article published on Forbes. In the article, contributor Bernard Marr writes that Royal Dutch Shell, one of the largest oil and gas companies, has developed a data-driven oilfield that reduces the cost of drilling for oil. The technique requires the ability to monitor low frequency seismic waves beneath the earth’s surface to determine whether they’re distorted as they pass through oil or gas.

“Data from any prospective oil field can then be compared alongside that from thousands of others around the world to enable geologists to make more accurate recommendations about where to drill,” writes Marr.

In addition, oil companies are using big data to monitor and improve machine performance as well as streamline the transport, refinement, and distribution of oil and gas.

Auto Insurance

According to the McKinsey article “Unleashing the Value of Advanced Analytics in Insurance,” auto insurers already analyze a variety of information, including real-time data about driving habits, to set premium rates and discounts. They also factor behavior-based credit scores from credit bureaus into their analyses, because there is empirical evidence suggesting that people who pay their bills on time are also safer drivers.

Allstate’s Arity unit, an independent subsidiary focused on connected-car data, uses data from telematics initiatives to develop models and products for auto insurance as well as ridesharing and roadside assistance, according to Digital Insurance.

Looking Ahead

These five big data industries are just a handful of those starting to realize the power of data analytics. (Here are several more.) As we look ahead, the power of big data will become even bigger. Companies both large and small will continually look for ways to capture and analyze customer data to improve profits and drive business intelligence.

What’s Next?

Data Science Careers Outlook

Data Science Student Stories

UW Data Science Course List

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, Data Science Central lists these top 27 data science keywords 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 8,000 data scientist jobs nationwide. More than 5,000 of these jobs were posted in the last month. More than 300 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/.