A data scientist is a very special individual in the data model. You might think with all the data automation that users can practically manage their own data, quite the contrary. With Big Data sets and more complicated data management and analysis tools, the role of the data scientist has simply become far more specialized.
Regardless of their specific role in data, that is whether processing, analyzing, or engineering data, the absolute role of a data scientist is to ensure integrity and scientific principles are applied in all aspects of handling data for information extraction.
Below I’m going to explain more about the development of data as a science and the different roles data scientists play.
What Exactly is Data Science?
Data science is a field that uses scientific processes to extract meaningful information from data. The field has grown out of the interconnections between mathematics, computer science, information sciences, and technology. Data science is now integrated into some applied sciences, in particular business studies, economics, and marketing.
History of Data Science
Initially, data science was treated as a branch of mathematics or computing science. Peter Naur first used the term liberally when he published the Concise Survey of Computer Methods in 1974.
Data science itself is a relatively new classification considering the age of computers. Even up to the late 90’s it was still classified by some as an extension of Statistics, for example in 1997, C.F. Jeff Wu’s lecture Statistics = Data Science? Says it all. By 2002 Data Science had emerged as a field, and the ICSU (International Council for Science) published the first Data Science Journal.
Today data science is an extremely high demand field, with a transportable skill set which continues to expand.
Who is a Data Scientist?
A simple answer to this question is that anyone trained in the field of data science is a data scientist. This ranges from computer science graduates specializing in data and information, to data analytics specialists, to business professionals specializing in data applications.
Data scientists are in huge demand in the technology sector, among others. Their specialist skills are used to give companies valuable data which drives new developments, streamlines existing processes, and increases profits.
What Qualifications Are There in Data Science?
Some qualifications in data science today include degree programs in:
- Statistics or applied mathematics
- Data analytics
- Computer science
- Data Engineering
- Business analytics
- Information management
Alongside degree programs there are many non-degree qualifications that can get someone started on the path to becoming a data scientist, providing access to practical skills from an entry-level job where further study can be undertaken.
What Skills Does a Data Scientist Need?
Data science is mostly a mix of mathematics, computer programming, and analytics.
The primary skill a data scientist needs is the ability to ask questions. This is the fundamental of all science, and applies equally well here. Without asking, why am I analyzing this data? What key results are we trying to prove? Effective results won’t be obtained.
Apart from asking questions, to succeed with the detail orientated work, good analytical skills and logical thinking are needed.
What are the Different Roles of a Data Scientist?
Data scientists may be employed in research, industry, business, or public sectors.
A data scientist may have many roles depending on the industry that they train and work in. I’m going to talk about some of the key parts of a data scientists job below.
Data Processing Role
Within data-processing, there are many tasks which a data scientist can perform. Data must be captured, stored, cleaned, validated, transformed, and presented in a useful way before it can be analyzed. The extension of data sets into big data means that data processing itself is a science. Data processing scientists are specialists in completing these important tasks to take data from unused and raw to useful and informative.
Data processing needs methodology and attention to details.
Application Development Role
A data scientist can be involved in developing data applications through programming or database design, for example, batch processing, machine learning applications, pay role integrations, billing applications, fraud detection, and customer behavior tracking, to name a few.
Application development requires practical thinking and problem-solving skills.
Data Analytics Role
A data analyst will take processed data and draw conclusions from it. Data analytics can be used in many fields, from business to research.
A data analyst will use processes such as modeling, algorithms, mapping, and data analytics software, to extract conclusions from data sets. Comparing databases, running data queries, and finding relationships are all components of a data analyst’s job.
Data analysts need a sound programming and a strong mathematical background.
Data Science Software and Hardware Development Roles
A data science engineer is someone who develops or fixes software and hardware in data systems.
Data sciences seem to have an insatiable appetite for new software and hardware to process the data. From automatic database programs to specialized applied database management, and distributed systems architecture, data engineering is a highly challenging field.
Data engineers need creativity, along with good technical knowledge, and strong programming skills.
Applied Data Science
Applied data science means data science applied to a particular field. The most common is business, technology, economics, and marketing. However, with the spread of data applications, the amount of applied data science roles are growing constantly.
Applied data science suits individuals who have a significant interest in a particular field, combined with a scientific or analytical mind.
Jack of All Trades Data Scientist
A data scientist in a small organization, or in a company where there is a small section devoted to data management may need to be a jack of all trades, applying all aspects of data processing and analysis to the companies needs. This may involve digging for information projects by speaking to concerned parties in the organization, finding data sources, data processing, analytics, system development, and presentation. It’s all about finding meaning in figures and facts collected within an organization and by external search.
This may be one of the more exciting data scientist roles since it covers such a variety of disciplines, and more available than one might think since many companies still have a small data analytics team.
How Does a Data Scientist Help?
Data science has been used in research and academia for years, and some may think of a data scientist as a white lab coat type in a research facility, however, data science is now becoming the norm in commercial and public sector applications.
A data scientist is the critical glue between an organizations’ questions and scientific proof backing up possible answers. I only mention possible, as until put in action practically, all data only provides theories, but theories with good statistical probabilities, compared to the alternative of guesswork.
Without the role of data scientists, much of business decision making is guesswork. Companies that leverage data sciences have succeeded where those that don’t fail. Examples of new startups that use data scientists to corner the market are AirBNB and Uber, of course, technology giants like Facebook, Google, and Amazon rely on data science.
In today’s environment the field of data science is very much in demand, and likewise, the avenues and roles of a data scientist are extremely varied and far-reaching.
For those looking to become a data scientist, the world is open to you, look for a field that suits your skills, and study hard, it’s a challenging, but rewarding role.
For those looking to employ or engage data scientists, it’s a matter of finding out what you need to do with data and looking for the right skills and qualifications that suit your purpose.