Data Scientist: Definition and Qualifications

Data scientists are big data wranglers, gathering and analyzing large sets of structured and unstructured data. A data scientist’s role combines computer science, statistics, and mathematics. They analyze, process, and model data then interpret the results to create actionable plans for companies and other organizations.

Data scientists are analytical experts who utilize their skills in both technology and social science to find trends and manage data. They use industry knowledge, contextual understanding, skepticism of existing assumptions – to uncover solutions to business challenges.

A data scientist’s work typically involves making sense of messy, unstructured data, from sources such as smart devices, social media feeds, and emails that don’t neatly fit into a database.

You will need at least a bachelor’s degree in data science or computer-related field to get your foot in the door as an entry level data scientist, although most data science careers will require a master’s degree.

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What do data scientists do?

Data scientists have several duties that allow them to use mathematical, science and computer skills on a daily basis. Their specific duties depend on their level of experience and their particular position. For example, an entry-level data scientist may spend much of their day cleaning data, while a high-level employee may create new products or structure big data projects.

Heres a look at some of a data scientists general duties:

Who are data scientists?

Data scientists refer to the analytical experts who collect and interpret large amounts of structured and unstructured data. Using their knowledge of computer science, mathematics and science, they help create actionable plans and solutions for companies based on their findings.

While data science and data analytics both use data, data science focuses on broader insights that determine which questions should be asked, while data analytics highlights the importance of finding answers to the questions that are asked. Since data science has many applications, its a versatile career found in many industries.

For example, you can find data scientists working in health care, banking, e-commerce or for the federal government. No matter where they work, they often collaborate with other teams within an organization such as a companys operations or marketing department to solve complex problems a business faces.

How is data science used?

Data science has several purposes across the workforce. Understanding the importance of data science can help you determine if you see yourself working in this field. Here are some of the uses of data science:

Advertising and marketing

Data science uses existing data and combines it with other data points to gather insight to better understand a companys customer, audience and campaigns. Using this insight, companies can create more strategic marketing plans to engage their audience, sell products and therefore, increase their profit margin.

Rather than relying on their intuition, companies can better achieve their advertising goals using research. Also, while data science can target a brands audience, it can help these companies control their brands story.

Personalized products and services

Thanks to the insight that data science provides, companies can tailor their products, services and experiences to their customers in the hopes of achieving greater profits. For example, when a consumer visits a retail website, their movements on the website may be tracked.

Companies can use a consumers website activity to present them with product suggestions the next time they visit their website. This personalized experience helps customers feel seen and understood by the company theyre shopping with.

Security and fraud detection

One of the main benefits of data analytics and data science is the ability to handle massive amounts of data. Not only does it help determine what is normal within a set of information, but it also spots irregularities. When data scientists spot these anomalies, they can expose weaknesses in a security system.

While you could find these anomalies without the use of technology, data science lets you track trends and potential problems infinitely quicker. In terms of fraud, data science and analytics helps the Internal Revenue Service detect discrepancies on tax forms.

Data scientist salary and job outlook

Not only does data science have many uses, but its also a lucrative career. The salary you earn in this profession depends on your experience level and knowledge. For the most up-to-date salary information from Indeed, click on each salary link below:

Junior data scientist

Lead data scientist

Senior data scientist

Unlike other IT sectors that are heavily saturated, data science offers excellent job prospects for qualified individuals. According to the Bureau of Labor and Statistics (BLS), the job outlook for computer and information research scientists is projected to see a growth of 15 percent by 2029. Overall, theres a large demand for this profession and an abundance of positions available.

Data scientist qualifications

If youre interested in becoming a data scientist, you need to have the knowledge and passion to excel in this field. This involves having the proper education, skills and other qualifications. Here are some the requirements for becoming a data scientist:

Education

To become a data scientist, you need at least an undergraduate degree in data science or a related field such as statistics, computer science, computer engineering or information systems. While a degree in computer science teaches you about computing systems, math, programming languages and database management, computer engineering focuses on systems engineering, cybersecurity and overall network architecture.

If you plan to make a career switch into data science, you may be able to bypass a related degree if you develop the technical skills required for the position through online classes. Besides, while a masters degree isnt necessarily required for this profession, some jobs may require some type of advanced degree in a qualitative discipline. Its worth noting that most data scientists have a masters degree and Ph.D., so having an advanced degree in this field can help you compete with other job applicants.

While you work on your data scientist education, you can also consider a specialization such as artificial intelligence, research, database management or machine learning. Not only does this increase your knowledge within one area of data science, but it may also increase your earning potential if hiring managers want to hire data scientists with specialized skills.

Technical skills

Data scientists need to have several technical skills to help them perform their duties efficiently. Their technical skill set allows them to gather insight that can help them solve complex problems. Here are some of the technical skills you need as a data scientist:

Soft skills

In addition to technical skills, data scientists need a strong set of soft skills that help them interact with their colleagues, solve problems and manage their work. Soft skills that data scientists need include the following:

FAQ

What qualifications do you need for a data scientist?

To work as a data scientist, you must have an undergraduate or a postgraduate degree in a relevant discipline, such as Business information systems, Computer science, Economics, Information Management, Mathematics and Statistics. At different levels, the course eligibility differs.

How do I become a data scientist?

There are three general steps to becoming a data scientist:
  1. Earn a bachelor’s degree in IT, computer science, math, business, or another related field;
  2. Earn a master’s degree in data or related field;
  3. Gain experience in the field you intend to work in (ex: healthcare, physics, business).

What majors do you need to be a data scientist?

The in-demand graduate degrees for data science include the exact same specifications for an undergraduate degree: data science (if available), computer science, information technology, math, and statistics. However, many companies also accept STEM degrees such as biotechnology, engineering, and physics (among others).

Is it hard to become a data scientist?

Because of the often technical requirements for Data Science jobs, it can be more challenging to learn than other fields in technology. Getting a firm handle on such a wide variety of languages and applications does present a rather steep learning curve.

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