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How to Become a Data Scientist

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What is a Data Scientist?
Career Outlook
Data Scientist Salary
Becoming a Data Scientist
Education Course
Day in the Life

What is a Data Scientist?

In the 21st century, computers have provided ways to collect massive amounts of information. The information can be structured, such as lists and records or unstructured such as random events and observations. Large amounts of data can also be the primary business asset of many modern industries. The task of understanding these masses of information is a big and important task. This is the role of the data scientist.Data science is a complex area of work and study; while it is a single field, it involves a wide array of skills, knowledge, and experience. The wide variety makes it difficult to state a single simple definition. A straightforward way to describe data scientist is that they are professionals that gather data and analyze it with the goal of reaching sound conclusions that make the data more useful.

There are many everyday uses of data science in our daily lives. Most of us have had the experience of going to a shopping site and being met by name and a list of shopping suggestions. Similarly, we may open our favorite streaming video network and find a list of movie and film suggestions based on our past selections. These are examples of algorithms that businesses have developed to predict customer preferences and behavior. These are products of data science.

Career Outlook

The career outlook for data scientists is strong. While there are no specific data sets for the employment of data scientists, the federal government does have estimates for IT computer scientists that mine data. Data mining is part of data science, and this category is a good reflection of the data science occupation.

The Bureau of Labor Statistics estimates a 19 percent growth rate for computer science through the decade ending in 2026. The estimated growth rate is much stronger than the average growth rate with the Bureau estimating more than 5,000 new jobs in the decade ending 2026. The successful conversion of data resources into products and ideas creates leading-edge technology and applications. The demand for new and better technology is a constant; the opportunities to find greater values in data assets will also drive increased use of data scientists.

Data scientists succeed in solving problems or creating new opportunities. They take those successful projects or issue resolutions and apply them to future searches, creative activity, or problem-solving missions. Current technology advances using data science include machine learning.

Data Scientist Salary

According to PayScale, the salary ranges for IT Data Scientists is a national average of $93,000 annually. The entry-level range is approximately $85,000, and the late-career range is about $150,000. The estimates suggest that the market for data science is strong and that data scientists can expect to earn high-level salaries and take on meaningful work for their employer organizations.

How to Become a Data Scientist

Data science requires formal education, advanced education, and industry-specific knowledge and experience. The below listed required steps represent a starting point for a career that must grow by experience and the constant pursuit of new knowledge.

Required Steps

Students must take the below-listed basic steps to become a Data Scientist.

  1. Complete an accredited bachelor’s degree program in a mathematics-based field of studies such as Math, Physics, Computer Science, Data Science or Information Technology
  2. Complete a master’s degree in a data-related field, often a Master’s in Data Science or related
  3. Identify the fields or industries in which you want to work; You must gain experience and knowledge in the industries (for example Healthcare Information, Marketing, eCommerce)

Required Education

The bachelor’s degree in data or computer science can be the entry level for data science. Some government agencies accept this as an entry-level credential. The strong trend in employment is the master’s degree. The field requires specialization and working with advanced research protocols, complex product or projects, and urgent needs for solutions. Employers increasingly search for and recruit master’s degree holders with substantial amounts of industry-specific experience.

Courses needed to Become a Data Scientist

Schools determine the course content for degrees, majors, and minors. The most used courses of study to prepare for a career in data science include Computer Science, Statistics, Math, Economics, and Bachelor of Science programs in Social Science.

Some essential courses for data scientists include:

  • Technical areas- Coding, Quantitative Analysis, and Database Management
  • Computer programming, languages, and statistics
  • General Education- business, psychology, political science sociology, and Liberal Arts disciplines

Specializations

The field of data science can seem like many fields rather than a single type of occupation because of the extreme degree of specialization. Data science is an important tool for nearly every kind of business or organization. For data-centric companies, it may have the highest level of importance. Each industrial, policy, or commercial area can support specialization. Data scientists work in critical national defense infrastructure to strengthen defenses and detect threats. They can also help marketing companies reach precise audiences with their business messaging. The variety is extraordinary, and there are many needs for specialization.

A Day in the Life of a Data Scientist

Data scientists work on data, but their goals are to solve problems and create additional business or program opportunities for their employers. The work environments are typically offices, but they work with state-of-the-art software and powerful hardware configurations. Data scientists must collaborate across organizational lines and work under compelling time constraints. Many of the solutions they must develop are unknowns, and they must discover keys and patterns that can unlock opportunities or provide critical solutions.

Data scientists work in research and strategy to develop opportunities for investment or action and pathways to success. They often must formulate concrete solutions to organizational problems or remove barriers to reach the desired goal. They work in software and computing environments like SQL and with tools like “R” to present data. They use techniques such as data visualizations to attack issues and resolve problems.

Modern businesses often have production teams that discover, create, or work with promising concepts. Today’s product or project teams typically include engineers, designers, product managers, and one or more data scientists. In this way, data scientists have major impacts on their organizations. They affect growth, productivity, and overall success.

Licensure, Certifications and Continuing Education

Licensure may not matter as much for data scientists as continuing education and staying current in a dynamic and rapidly changing field. Overall, states do not license data scientists, and most employers will consider certifications in the context of the overall educational level. Certifications can establish some of the core elements and skill areas such as certification in coding languages and computer software development environments. Industry experience and records of achievement will carry weight in selection decisions and consulting engagements; certification can add weight to experience and demonstrate technical expertise.

Some useful certifications include the Certified Analytics Professional (CAP) by the Institute for Operations Research and the Management Sciences (INFORMS). This certification demonstrates end-to-end analytics that is particularly useful for the businesses and product life-cycle analysis. The EMCDS certification and training prepares students with important open source tools like R and Hadoop.

Continuing education is critical to data scientists. The field changes with many drivers, such as new technology and technological innovations. Social factors can also be major drivers of data science applications.

Sources:

Bureau of Labor Statistics, Occupational Outlook Handbook, Computer and Information Research Scientists- https://www.bls.gov/ooh/computer-and-information-technology/computer-and-information-research-scientists.htm

Payscale.com

EMC Certification- https://education.emc.com/guest/certification/framework/ds.aspx

INFORMS-https://www.informs.org/About-INFORMS