Back to Blog

data science design IDEO
Data Science

4 Ways Data Science and Design Work Together

5 minute read | November 13, 2020
Kindra Cooper

Written by:
Kindra Cooper

Ready to launch your career?

While they might sound like polar opposites, data science and design represent an interdisciplinary approach to designing intuitive, user-friendly products made expressly for humans. At this year’s Springboard’s Rise 2020 virtual conference, designers Takashi Wickes and Lisa Nash from IDEO, a global design consultancy, explained the concept of human-centered data science, which results from interdisciplinary teams incorporating design thinking into their approach. 

Here’s a good example. When a startup called Rise Science approached IDEO, they presented a data platform for college and professional athletes to track their sleep and adjust their behavior so they could play at peak performance. But users had trouble understanding the charts and graphs to decide what they should do next. What they needed was not easier-to-read data, but a different perspective on their data based on actionable recommendations they could follow, such as “Go to sleep earlier.” The problem was not a data visualization problem but a user experience one. 

During their session at Rise, Wickes and Nash discussed IDEO’s four-step design process and how they’ve fostered collaboration between data scientists and designers throughout the design process. 

Inspiration

The initial phase of any design process starts with searching for inspiration in unlikely places. Instead of performing a standard-issue competitor analysis or studying the market, IDEO designers look to other industries that are analogous in terms of human factors to the industry for which they’re trying to solve a problem. Recently, the design consultancy was tasked with designing a holistic patient experience solution for Nemours, a pediatric healthcare system that operates hospitals and clinics in Delaware, Florida, New Jersey, and Pennsylvania. 

The team needed a method to combat emergency room overcrowding and reduce patient admission time. An ER is a high-density environment where doctors and nurses are focused on diagnosing issues and fixing those problems while under severe time pressure. For inspiration, the team looked to the race track—specifically, to the pit crews that check and fix car parts during races.

“They’re a team of highly specialized individuals who diagnose problems, find solutions and treatments to alleviate those problems, and execute those solutions in a high-stress environment,” explained Wickes. 

The designers took a similar approach to the emergency room redesign: patients are greeted, sorted by medical condition, and immediately checked into rooms. 

Using data science during this process allowed the designers to explore analogous spaces and blindspots. Proprietary data from the client or publicly available data sources like social media are a great place to start. Wickes says looking at hashtags and posts related to the potential product idea allows design teams to find those first sparks of inspiration—perhaps by discovering that people use a certain product in unexpected ways or perceive a particular brand differently from its positioning. 

Research and synthesis

From a human-centered design standpoint, research involves stepping into the prospective user’s shoes and building empathy for them to understand their pain points, each one of which represents a design opportunity. Lisa Nash, a data scientist and designer at IDEO, couches it in simpler terms: “Understanding what people say, do, think and feel.” 

Most qualitative research centers around surveys and one-on-one interviews, but what people say and what they actually do often diverge. For example, when IDEO was approached by a pharmaceutical company to redesign the packaging for a brand of prescription medication, the team interviewed people who took prescription meds on a daily basis. One interviewee was an 80-year-old woman, whose hands shook from arthritis. She told the researcher she had no trouble opening her pill bottle. Curious, the researcher asked her to demonstrate how she did it. 

“To their surprise, she pulled out a deli slicer to cut the top off the prescription bottle,” said Nash. “Then it was pretty obvious that she had created a workaround and wasn’t opening the bottle the way it was intended.” 

Exploring the gap between people’s stated intentions and their actual behavior can be very enlightening. Applying a data science approach to this phase of the design process helps researchers gain an objective view of qualitative data. For example, using statistical analysis to rank the relative importance of competing pain points or understanding how much time someone loses from using a subpar solution. 

Get To Know Other Data Science Students

Jonathan Orr

Jonathan Orr

Data Scientist at Carlisle & Company

Read Story

Pizon Shetu

Pizon Shetu

Data Scientist at Whiterock AI

Read Story

Melanie Hanna

Melanie Hanna

Data Scientist at Farmer's Fridge

Read Story

Prototyping

Once designers have done sufficient research to understand their target audience’s pain points and brainstorm potential solutions, the next step is to design a tangible product, test it in the field and iterate in response to user feedback. Prototypes can be low-tech; IDEO designers often defer to arts and crafts supplies commonly found in preschool classrooms, such as pipe cleaners, modeling clay, and paste. 

Wickes displayed a photo of a nurse at an IDEO workshop who proposed an idea for a bedside medical device that she could operate one-handed so she could hold her patient’s hand with her free hand and keep them calm during the procedure. She’d prototyped it using yellow Play-Doh. “No one expects that a lump of Play-Doh is going to be the final design,” explained Wickes, “so they’re able to look at the inherent essence of the idea that the nurse is trying to communicate.” 

Visuals are an ideal medium for data scientists and designers to communicate their ideas in a universal language, whether it’s a sketch of a potential interface or a graphical representation of data analysis.

Communication

The final phase of the ‘design x data science’ process involves tightening up the concept and presenting the final idea to the client. Communication isn’t limited to charts, reports, and PowerPoint presentations. It could involve having the client try a hi-fidelity prototype of a mobile app by assuming the role of a particular user or creating an experiential pop-up installation to show how a reimagined retail space or in-patient treatment center would look and feel.

“We like to bring the vision to life for our stakeholders,” said Nash. “We communicate the design in a way that our stakeholders not only understand our design but also feel it.” 

Using data science techniques in communication allows designers to ground the prototype in a real-life situation by using real or simulated data. For example, in one recent project, IDEO redesigned the patient experience at a hospital. They conducted qualitative interviews with patients and analyzed the data of patient flows to discover opportunities for improving the experience, such as reducing wait times. 

Data scientists used real data from the hospital to simulate different scenarios and how each one would affect patient flow. 

“The simulation helped the team test different possibilities,” Nash said. “The client remarked they could feel the emotions the patients might feel during different parts of the process.”

For more Rise 2020 coverage, check out posts on how data science can be leveraged for social good and tips on transforming your career in a post-pandemic world.

Companies are no longer just collecting data. They’re seeking to use it to outpace competitors, especially with the rise of AI and advanced analytics techniques. Between organizations and these techniques are the data scientists – the experts who crunch numbers and translate them into actionable strategies. The future, it seems, belongs to those who can decipher the story hidden within the data, making the role of data scientists more important than ever.

In this article, we’ll look at 13 careers in data science, analyzing the roles and responsibilities and how to land that specific job in the best way. Whether you’re more drawn out to the creative side or interested in the strategy planning part of data architecture, there’s a niche for you. 

Is Data Science A Good Career?

Yes. Besides being a field that comes with competitive salaries, the demand for data scientists continues to increase as they have an enormous impact on their organizations. It’s an interdisciplinary field that keeps the work varied and interesting.

10 Data Science Careers To Consider

Whether you want to change careers or land your first job in the field, here are 13 of the most lucrative data science careers to consider.

Data Scientist

Data scientists represent the foundation of the data science department. At the core of their role is the ability to analyze and interpret complex digital data, such as usage statistics, sales figures, logistics, or market research – all depending on the field they operate in.

They combine their computer science, statistics, and mathematics expertise to process and model data, then interpret the outcomes to create actionable plans for companies. 

General Requirements

A data scientist’s career starts with a solid mathematical foundation, whether it’s interpreting the results of an A/B test or optimizing a marketing campaign. Data scientists should have programming expertise (primarily in Python and R) and strong data manipulation skills. 

Although a university degree is not always required beyond their on-the-job experience, data scientists need a bunch of data science courses and certifications that demonstrate their expertise and willingness to learn.

Average Salary

The average salary of a data scientist in the US is $156,363 per year.

Data Analyst

A data analyst explores the nitty-gritty of data to uncover patterns, trends, and insights that are not always immediately apparent. They collect, process, and perform statistical analysis on large datasets and translate numbers and data to inform business decisions.

A typical day in their life can involve using tools like Excel or SQL and more advanced reporting tools like Power BI or Tableau to create dashboards and reports or visualize data for stakeholders. With that in mind, they have a unique skill set that allows them to act as a bridge between an organization’s technical and business sides.

General Requirements

To become a data analyst, you should have basic programming skills and proficiency in several data analysis tools. A lot of data analysts turn to specialized courses or data science bootcamps to acquire these skills. 

For example, Coursera offers courses like Google’s Data Analytics Professional Certificate or IBM’s Data Analyst Professional Certificate, which are well-regarded in the industry. A bachelor’s degree in fields like computer science, statistics, or economics is standard, but many data analysts also come from diverse backgrounds like business, finance, or even social sciences.

Average Salary

The average base salary of a data analyst is $76,892 per year.

Business Analyst

Business analysts often have an essential role in an organization, driving change and improvement. That’s because their main role is to understand business challenges and needs and translate them into solutions through data analysis, process improvement, or resource allocation. 

A typical day as a business analyst involves conducting market analysis, assessing business processes, or developing strategies to address areas of improvement. They use a variety of tools and methodologies, like SWOT analysis, to evaluate business models and their integration with technology.

General Requirements

Business analysts often have related degrees, such as BAs in Business Administration, Computer Science, or IT. Some roles might require or favor a master’s degree, especially in more complex industries or corporate environments.

Employers also value a business analyst’s knowledge of project management principles like Agile or Scrum and the ability to think critically and make well-informed decisions.

Average Salary

A business analyst can earn an average of $84,435 per year.

Database Administrator

The role of a database administrator is multifaceted. Their responsibilities include managing an organization’s database servers and application tools. 

A DBA manages, backs up, and secures the data, making sure the database is available to all the necessary users and is performing correctly. They are also responsible for setting up user accounts and regulating access to the database. DBAs need to stay updated with the latest trends in database management and seek ways to improve database performance and capacity. As such, they collaborate closely with IT and database programmers.

General Requirements

Becoming a database administrator typically requires a solid educational foundation, such as a BA degree in data science-related fields. Nonetheless, it’s not all about the degree because real-world skills matter a lot. Aspiring database administrators should learn database languages, with SQL being the key player. They should also get their hands dirty with popular database systems like Oracle and Microsoft SQL Server. 

Average Salary

Database administrators earn an average salary of $77,391 annually.

Data Engineer

Successful data engineers construct and maintain the infrastructure that allows the data to flow seamlessly. Besides understanding data ecosystems on the day-to-day, they build and oversee the pipelines that gather data from various sources so as to make data more accessible for those who need to analyze it (e.g., data analysts).

General Requirements

Data engineering is a role that demands not just technical expertise in tools like SQL, Python, and Hadoop but also a creative problem-solving approach to tackle the complex challenges of managing massive amounts of data efficiently. 

Usually, employers look for credentials like university degrees or advanced data science courses and bootcamps.

Average Salary

Data engineers earn a whooping average salary of $125,180 per year.

Database Architect

A database architect’s main responsibility involves designing the entire blueprint of a data management system, much like an architect who sketches the plan for a building. They lay down the groundwork for an efficient and scalable data infrastructure. 

Their day-to-day work is a fascinating mix of big-picture thinking and intricate detail management. They decide how to store, consume, integrate, and manage data by different business systems.

General Requirements

If you’re aiming to excel as a database architect but don’t necessarily want to pursue a degree, you could start honing your technical skills. Become proficient in database systems like MySQL or Oracle, and learn data modeling tools like ERwin. Don’t forget programming languages – SQL, Python, or Java. 

If you want to take it one step further, pursue a credential like the Certified Data Management Professional (CDMP) or the Data Science Bootcamp by Springboard.

Average Salary

Data architecture is a very lucrative career. A database architect can earn an average of $165,383 per year.

Machine Learning Engineer

A machine learning engineer experiments with various machine learning models and algorithms, fine-tuning them for specific tasks like image recognition, natural language processing, or predictive analytics. Machine learning engineers also collaborate closely with data scientists and analysts to understand the requirements and limitations of data and translate these insights into solutions. 

General Requirements

As a rule of thumb, machine learning engineers must be proficient in programming languages like Python or Java, and be familiar with machine learning frameworks like TensorFlow or PyTorch. To successfully pursue this career, you can either choose to undergo a degree or enroll in courses and follow a self-study approach.

Average Salary

Depending heavily on the company’s size, machine learning engineers can earn between $125K and $187K per year, one of the highest-paying AI careers.

Quantitative Analyst

Qualitative analysts are essential for financial institutions, where they apply mathematical and statistical methods to analyze financial markets and assess risks. They are the brains behind complex models that predict market trends, evaluate investment strategies, and assist in making informed financial decisions. 

They often deal with derivatives pricing, algorithmic trading, and risk management strategies, requiring a deep understanding of both finance and mathematics.

General Requirements

This data science role demands strong analytical skills, proficiency in mathematics and statistics, and a good grasp of financial theory. It always helps if you come from a finance-related background. 

Average Salary

A quantitative analyst earns an average of $173,307 per year.

Data Mining Specialist

A data mining specialist uses their statistics and machine learning expertise to reveal patterns and insights that can solve problems. They swift through huge amounts of data, applying algorithms and data mining techniques to identify correlations and anomalies. In addition to these, data mining specialists are also essential for organizations to predict future trends and behaviors.

General Requirements

If you want to land a career in data mining, you should possess a degree or have a solid background in computer science, statistics, or a related field. 

Average Salary

Data mining specialists earn $109,023 per year.

Data Visualisation Engineer

Data visualisation engineers specialize in transforming data into visually appealing graphical representations, much like a data storyteller. A big part of their day involves working with data analysts and business teams to understand the data’s context. 

General Requirements

Data visualization engineers need a strong foundation in data analysis and be proficient in programming languages often used in data visualization, such as JavaScript, Python, or R. A valuable addition to their already-existing experience is a bit of expertise in design principles to allow them to create visualizations.

Average Salary

The average annual pay of a data visualization engineer is $103,031.

Resources To Find Data Science Jobs

The key to finding a good data science job is knowing where to look without procrastinating. To make sure you leverage the right platforms, read on.

Job Boards

When hunting for data science jobs, both niche job boards and general ones can be treasure troves of opportunity. 

Niche boards are created specifically for data science and related fields, offering listings that cut through the noise of broader job markets. Meanwhile, general job boards can have hidden gems and opportunities.

Online Communities

Spend time on platforms like Slack, Discord, GitHub, or IndieHackers, as they are a space to share knowledge, collaborate on projects, and find job openings posted by community members.

Network And LinkedIn

Don’t forget about socials like LinkedIn or Twitter. The LinkedIn Jobs section, in particular, is a useful resource, offering a wide range of opportunities and the ability to directly reach out to hiring managers or apply for positions. Just make sure not to apply through the “Easy Apply” options, as you’ll be competing with thousands of applicants who bring nothing unique to the table.

FAQs about Data Science Careers

We answer your most frequently asked questions.

Do I Need A Degree For Data Science?

A degree is not a set-in-stone requirement to become a data scientist. It’s true many data scientists hold a BA’s or MA’s degree, but these just provide foundational knowledge. It’s up to you to pursue further education through courses or bootcamps or work on projects that enhance your expertise. What matters most is your ability to demonstrate proficiency in data science concepts and tools.

Does Data Science Need Coding?

Yes. Coding is essential for data manipulation and analysis, especially knowledge of programming languages like Python and R.

Is Data Science A Lot Of Math?

It depends on the career you want to pursue. Data science involves quite a lot of math, particularly in areas like statistics, probability, and linear algebra.

What Skills Do You Need To Land an Entry-Level Data Science Position?

To land an entry-level job in data science, you should be proficient in several areas. As mentioned above, knowledge of programming languages is essential, and you should also have a good understanding of statistical analysis and machine learning. Soft skills are equally valuable, so make sure you’re acing problem-solving, critical thinking, and effective communication.

Since you’re here…Are you interested in this career track? Investigate with our free guide to what a data professional actually does. When you’re ready to build a CV that will make hiring managers melt, join our Data Science Bootcamp which will help you land a job or your tuition back!

About Kindra Cooper

Kindra Cooper is a content writer at Springboard. She has worked as a journalist and content marketer in the US and Indonesia, covering everything from business and architecture to politics and the arts.