A data scientist collects, analyzes, and interprets data sets to get information and insights that they can then apply to the operations of a business or organization. In most cases, data scientists work with extensive collections of data.
This profession is relatively new. Mathematicians, statisticians, analysts, and computer scientists used to perform some of the duties that a data scientist now handles. Some data science experts started their careers in one of these related fields, while others received training in a math or computer science-related subject before specializing in data science.
Data scientists have specialized skills that include creating advanced database querying tools, frameworks, and analytical applications. They may also be familiar with big data, artificial intelligence, machine learning, and computer modeling.
Businesses make decisions based on the data, models, and analysis performed by data scientists.
A data scientist is different than a data engineer. Data engineers focus on the structure of database systems and applications that collect and prepare data for analysis. In other words, a data engineer creates the pipeline for the data, and the data scientist takes data from that pipeline to perform their analysis.
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What Does a Data Scientist Do?
A data scientist uses data to perform various functions for a company. Their job usually involves the analysis of data using computer models, mathematical formulas, or algorithms. They may create or customize models or algorithms to automatically analyze data.
Data scientists usually work with information from large databases. They use computer languages such as Structured Query Language (SQL) to extract and organize their data. Depending on the nature of their work, data scientists can also work on the internet using application programming interfaces (APIs).
Data scientists usually need to know common programming languages such as Python, R, and Java. Some even know about computer modeling and artificial intelligence. They use this knowledge to create analytical and predictive models. They employ these models to make forecasts and organize operational data, both of which help their employer make decisions about business operations and strategies.
Another facet of a data scientist’s career involves organizing and presenting findings to their company’s decision-makers. These tasks could include creating reports and visual representations of data and analysis. In many cases, data scientists need to be excellent communicators able to interact successfully with both technical and nontechnical employees.
Data scientists often work with algorithms. They use these complex formulas to find and extract relevant data and “clean” the data so that they only collect information pertinent to their current tasks. These algorithms can also help with modeling and predictions. Algorithms also measure efficiency, financial performance, and other variables within a company.
Some data scientists function mainly as analysts, while more senior members of the department may be responsible for planning projects so that they achieve the required results and directing junior scientists as they carry out the analysis.
What Is a Data Scientist Salary?
As in other growing data and IT-related fields, there is plenty of demand for qualified data scientists. Employers reward knowledge and skill because data scientists perform analysis that is vital to company operations and decision-making. As a result, people in this profession are reasonably well paid.
According to Glassdoor, the average salary for a data scientist in the United States is $113,309. The salary range is from $83,000 to $154,000. In general, those with job experience or a master’s degree can earn more than bachelor’s degree holders.
What Are Data Scientist Qualifications?
Data scientists start on their career path by earning a bachelor’s degree in data science, statistics, computer science, or a closely related field that involves programming or mathematical analysis. Because this is a new and growing profession, you can also gain experience through an internship.
Some education companies offer boot camps for people who have a relevant degree or experience, but who want to learn specific data science techniques and computer languages in a hands-on environment.
According to the Bureau of Labor Statistics, employers prefer data scientists who have a bachelor’s degree in relevant math or computer fields and a master’s degree in data science. You can also gain experience working on machine learning, artificial intelligence, or computer modeling.
Most data scientists work with databases, so they need some knowledge of SQL as well as skills in conventional programming languages such as Python, R, C++, and Java. You can develop these coding skills in school or during a boot camp or coding camp program.
Data Scientist Career Outlook
In 2018, the Bureau of Labor Statistics published projections for all computer and information science research positions. The demand for these professionals should rise by 16% by 2028. All computer-related professions should grow by 12% over the same timeframe.
Should I Become a Data Scientist?
If you want to become a data scientist, you should be willing to develop a very specific set of skills. This career is ideal for people with strong mathematical and analytical abilities. However, you also need to have a willingness to develop those abilities at the undergraduate and graduate level or through a training program or boot camp.
To feel fulfilled in this career, you need to enjoy analysis and be at ease working with computers, algorithms, and computer languages. Problem solvers thrive in data science careers because much of your work revolves around using data to find answers, solve operational problems, or find areas for improvement.
If you can work independently, you may enjoy a data science career. Most data scientists have some degree of autonomy, and their employers will expect them to work on their own to collect and analyze data.
Finally, this career is ideal for lifelong learners. Data science is a rapidly developing field, so data scientists may have to learn new techniques, computer languages, and programs during their careers.
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