12 Types of People Who Should Study Data Science - Data Science Degree Programs Guide

Reasons Why Professionals Choose Degrees in Data Science

Intrinsic Intellectual Curiosity
Interest in Machine Learning
Seeking Job Stability
Priority on Career Flexibility
Startup and Business Goals
Developing Data-Driven Marketing
Able to Prioritize Tasks and Take Initiative
An Effective Collaborator
A Desire to Cultivate Business Expertise
Interest and Ability in Coding
Analytical Skills and Expertise in Mathematics
Communication Skills

“Data scientist” is now one of the highest demand roles in the corporate world, but it remains a poorly understood job title amongst the general public. It’s natural for someone who has recently learned of this career path to wonder who should study data science. What are the unique characteristics that set apart successful data scientists who can command the top salaries that global corporations are willing to pay? What skill set, exactly, are hiring managers looking for when they onboard new data science professionals?

It’s easy to imagine data scientists as glorified number crunchers or specialized programmers, but there are actually many different kinds of people that study this field. Data science has groundbreaking applications in almost every industry. It has reshaped the way that business leaders analyze and evaluate their key metrics by providing better quantity and quality of information. While there are plenty of great career opportunities for actual data scientists, the knowledge and skills related to the profession are useful for achieving other goals as well.

Let’s take a look at 12 of the characteristics that could potentially define a person who should study data science:

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1. Intrinsic Intellectual Curiosity

A data scientist is a person who can harness the vast potential hidden in data and then use it in conjunction with related technologies to empower their organizations for improving profitability and efficiency. One common problem is that average stakeholders in many organizations are so used to the status quo that they would have no easy way of knowing which problems could be solved using data. Therefore, the typical data scientist joining their organization would have to be able to not only solve the organization’s existing problems; the data scientist is likely to also have to be proactive about figuring out which problems the company’s data could realistically solve.

These monumental goals can only be accomplished by a person who has intrinsic intellectual curiosity. Curiosity is characterized by a desire to ask questions, seek the answers and acquire an underlying knowledge of why things happen in a particular way. It’s a mindset of observing and wondering: “the data suggests a course correction in our current warehousing procedures would be beneficial, so what would the outcome be if we did x instead of y?”

2. Interest in Machine Learning

Machine learning and artificial intelligence are among the many emerging technologies that have captured the imagination of both the public and scientific communities. Machine learning could potentially be one of the most useful technologies for data science professionals to implement at work, but it’s surprisingly under-utilized. It’s relatively rare to find a working data scientist who has achieved expert-level proficiency with all the relevant aspects of this technology. This lack of machine learning expertise can become a hindrance to success on the job; frequently, it’s machine learning strategies that hold the keys to helping data science professionals design models that could accurately forecast what the likeliest outcomes will be in various business situations.

This skills gap results in massive opportunities for opportunistic students who have the capability to study machine learning concepts as part of their university degree program. Key skills to focus on cultivating include the following:

• Creating supervised machine learning models; understanding how to do regression analysis, testing, classification, training, scoring and cross-validation; understanding how to utilize accuracy assessment methods
• Creating unsupervised machine learning models; understanding clustering concepts and analysis of principal components
• Reinforcement learning
• Adversarial learning
• Natural language processing
• Decision trees
• Logistic regressions
• Modeling procedures relevant to data mining and extraction
• Proper use of outlier detection techniques to weed out irrelevant data

3. Seeking Job Stability

For many students, the potential for a strong salary and stable employment are key considerations when selecting a degree. This is one of the key reasons driving current interest in data science degrees, as demand for qualified professionals has been increasing at a rapid rate in recent years. In fact, many major corporations in the United States have grown their number of data workers exponentially over the last decade, according to Forbes.

4. Priority on Career Flexibility

Another defining feature of the data science skill-set is the incredible versatility it can offer to practitioners. People who want a large degree of freedom in choosing the location or type of job can often satisfy these objectives in data science professions. Large corporations, government agencies, educational institutions and many other kinds of organizations across the country need data scientists to help leverage their increasing stockpile of information.

5. Startup and Business Goals

The abilities and knowledge gained through education and experience in data science can be invaluable for entrepreneurs. People who are interested in starting a business, especially those that rely on the internet, have many opportunities to apply these skills. Learning how to collect, curate and analyze data to provide useful and actionable information can make the difference between success and failure for founders of a startup.

6. Developing Data-Driven Marketing

There are plenty of different applications for data science in the business world, but marketing professionals are among those with the most to gain upfront. People who want to quantify and predict consumer behavior can leverage data science to their advantage. This makes it an appealing, and perhaps a necessary choice for people who want to add a modern edge to their marketing arsenal.

Choosing a profession isn’t just about the career potential, prestige or opportunity for development, although all of these things are certainly important. Students should also consider how their degree program will help them achieve their personal ambitions and life goals. With this in mind, it’s easy to see how so many different types of people pursue studies in data science as part of their academic plan.

7. Able to Prioritize Tasks and Take Initiative

New business technologies are being developed and implemented at a dizzying rate of speed; as a result, data scientists have to be able to keep ahead of many ever-changing processes in a relentlessly fast-paced working environment.

The typical data science job isn’t suitable for someone who needs constant hand-holding, oversight and step-by-step guidance. This role is best suited for a person who is able to observe a situation, assess what needs to be done, prioritize which actions should be taken first and then take action to actually accomplish the desired results. When things go awry, the data scientist then needs to be able to assess how to course correct to get each aspect of the project back on track for successful completion.

8. An Effective Collaborator

Data scientists have to be able to work independently at times, but there are also many situations where they will need to collaborate with colleagues. It isn’t unrealistic to expect that they would need to be able to interact with various stakeholders from every other department within their organization:

• They’ll have to work with the company’s top executives to understand the most important objectives to prioritize in their work.
• They’ll have to interact with software developers to formalize data pipelines for empowering reliable business insights.
• They’ll have to work with the accounting team to understand how data can help the organization to minimize fraud and maximize their cash flow.
• They’ll have to work with the marketing department to implement data-driven strategies for optimizing the company’s marketing campaigns.
• They’ll have to help the sales team determine data-driven ways to make the company’s sales funnel more engaging and effective.
• They’ll have to help the logistics team understand how data could be utilized to minimize expenses and maximize working efficiency.
• They’ll have to brainstorm data-suggested strategies with product managers; the data can then be used for refining existing products and possibly even for introducing better products.
• They might even be trusted to collaborate with customers to identify ways of better meeting their needs using strategies based on data analysis.

To empower these collaborations, it’s ideal if the aspiring data scientist would make an effort to pick up some of the lingo used by professionals in each of these departments. It couldn’t hurt for the data science major to take at least one elective in related business subjects.

9. A Desire to Cultivate Business Expertise

It’s ideal for data scientists to have expansive in-depth background knowledge of the industry they’ll be working in. In particular, it’s useful to understand that industry’s most compelling problems and the methods various companies within that industry are currently using to solve them. Different solutions will each have a corresponding impact on the businesses utilizing them. In each case, it’s crucial for a data scientist to be able to predict what the likeliest outcome resulting from each potential solution would be. The data scientist’s primary professional role is to improve on the industry’s existing solutions to arrive at a more profitable way of doing things.

Previous work experience is useful for success as a working data science professional, but much of this knowledge can be accumulated simply by reading industry publications and blogs – and by giving conscious thought to the absorbed information. For university students who are studying data science, internships are invaluable for getting on the fast track to obtaining necessary business expertise.

10. Interest and Ability in Coding

According to Harvard Business Review, 80 percent of an average data scientist’s workday is typically spent on two tasks: discovering data and analyzing it. Both coding and mathematical ability will be essential for achieving success with these two objectives. Data science professionals typically use some combination of the following coding languages to get their work done:

• R programming language
• Python
• SQL
• Java
• Javascript
• Perl
• C and C++
• Scala
• Julia
• Unix shell/awk
• Lisp

11. Analytical Skills and Expertise in Mathematics

Analytical skills are a critical component of a data scientist’s job. At a minimum, aspiring data scientists will need to gain expertise in the following mathematical concepts:

• How to use statistics and probability theory; how to work with random variables, use Bayes’ theorem, and calculate standard errors
• How to use set theory; how to construct and present Venn diagrams
• How to do algebra with inequalities
• How to graph and describe functions on the Cartesian plane
• How to use slope and distance formulas
• How to use exponents and logarithms

Beyond these capabilities, further knowledge of advanced mathematics will also be useful. Ideally, students who have a track record of successfully completing high school mathematics and programming courses are the students who should study data science at the undergraduate or graduate level.

12. Communication Skills

Successful data scientists are able to take complex mathematical and technical insights, translate them into plain English, and communicate them to stakeholders on the team who work in other departments like marketing, sales and human resources.

“Data storytelling” ability is a particularly desirable characteristic in data scientists. This is loosely defined as being the ability to assess which narrative best describes the company’s data. It furthermore encompasses the capability of using that narrative to effectively communicate related implications to other stakeholders in the business.

Data storytelling is such a top-priority skill amongst employers that the role of “Data storyteller” is evolving into its own distinctive job title. Some companies are now employing “data storytellers” in addition to their data scientists.

While it’s ideal if prospective data scientists already possess these skills and interests, it isn’t a deal breaker if some of them are lacking. Many of the above skills can be cultivated and further developed. The most straightforward way to accomplish this is choosing the right degree program. Education can give an individual a jump start on acquiring the needed communication skills, collaborative abilities, coding knowledge and mathematical expertise. University offers students the chance to learn quickly from instructors who have already developed the needed skill set.

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