Data scientists mine complex data and systems-related data for their companies. These scientists are responsible for designing new ways to incorporate large amounts of information with a focus on IT topics. Data scientists work with teams of related technical professionals to manage massive amounts of statistical data and create different models based on the organization. (Payscale.com)
Data scientists also engage in the following: (BLS.gov)
- Explore the fundamental issues in data and computing and devise models and theories to address those problems.
- Assist scientists and engineers in solving complex data-driven computer problems.
- Devise new ways to enhance data and search quality, as well as predictive capabilities.
- Perform and interpret data studies and product experiments regarding new data sources.
- Develop proof of concepts, algorithms, predictive models, and custom analysis.
- Come up with new computing languages, tools, and methods to enhance how people work with computers and the data they contain.
- Design new experiments to test the operation of new software and data systems
If you want to become a data scientist, below is more information about this rapidly growing field.
What Is Data Science?
Data science is used to make predictions and decisions that use predictive causal analytics, prescriptive analytics, and machine learning: (Edureka.co)
- Predictive causal analytics: If a model is needed to predict the chances of an event in the future, this type of analytics is required. For example, if a company provides money on credit, how likely the customer will make credit payments in the future is important. Here the data scientist can construct a model that can do predictive analytics on the customer’s payment history to see if future payments will be timely.
- Prescriptive analytics: If a model is needed that has the ability to take its decisions and to modify them with new, dynamic parameters, prescriptive analytics is required. A good example is a self-driving car. The data collected by cars can be used to train cars to drive better.
- Machine learning: If there is transactional data held by a finance company and it needs to construct a model to determine future trends, machine learning algorithms written by data scientists are the best thing. The data scientist has data that it can use to train machines to perform certain tasks.
What Do Data Scientists Do?
In the last 10 years, data scientists have become essential in most companies and organizations. These highly educated professionals have the skills to build complicated quantitative algorithms to organize and synthesize large quantities of information that are used to answer questions and affect the strategy of their organization.
Data scientists examine the questions that need to be answered and where the data can be found. These technical workers have business skills and analytical skills. They also can mine, clean, and present data. Many companies use data scientists to source, manage, and analyze massive amounts of data. The results are then analyzed and synthesized and are given to executives and stakeholders in the organization. (Berkeley.edu)
Data scientists also work on data privacy rights to ensure clients are satisfied and the company avoids legal issues. Further, data scientists work in teams using such techniques as collaborative filtering, market basket analysis, and matrix factorization.
Where Do Data Scientists Work?
It is estimated that data scientists, a type of computer and information research scientist, held about 32,000 jobs in 2018, but this field is growing quickly. The largest employers of data scientists are: (BLS.gov)
- Federal government: 28%
- Computer systems design and related services: 20%
- Research and development in engineering, physical, and life sciences: 16%
- Colleges and universities: 8%
- Software publishers
Below are some specific companies that are hiring data scientists today: (Glassdoor.com)
According to another source, these companies have hired high numbers of data scientists and related professionals recently: (Techrepublic.com)
- IBM: 2,563 employees
- Amazon: 1,846 employees
- Microsoft: 1,800 employees
- Facebook: 1,220 employees
- Oracle: 1,210 employees
- Google: 904 employees
- Apple: 568 employees
What Is the Job Outlook for Data Scientists?
The Bureau of Labor Statistics reports that the employment of data scientists and related computer and information research scientists will grow by 16% by 2028, which is much faster than average. The rapid growth of data collection by many organizations will lead toa much greater need for data scientists that have data mining skills. Experts will be needed to devise new data algorithms that help companies to make informed conclusions from large amounts of data. With this information, organizations will have a better understanding of their customers. (BLS.gov)
Also, a growing concentration on cybersecurity will lead to new jobs for data scientists because data scientists are an integral part of learning new ways to fight cyberattacks.
How Can You Become a Data Scientist?
Most jobs for data scientists and related professions in computing and information research require a master’s in computer science, computer engineering, or a related field. (BLS.gov). Other sources say an advanced degree in physics, mathematics or even business can be an entryway into data science. (Towardsdatascience.com). It is necessary to have a good understanding of supervised and unsupervised machine learning techniques as well as an excellent grasp of statistics and the ability to evaluate modern statistical models.
Skills that are in demand for data scientists today include: (Payscale.com)
- Data modeling
- Big data analytics
- Data mining
- Apache Spark
- Machine learning
- Statistical analysis
What The Experts Say
- I started learning data science about 4 years ago. I had no real programming background. This is mostly geared towards people who are in the same position I was in. A lot of advice around learning data science starts with “first learn python”, or “first take a linear algebra course”. This advice is fine, but if I followed it, I never would have learned any data science. –, Founder, Dataquest
- To become a data scientist, you need a solid background in mathematics (especially analysis, multivariate calculus, and linear algebra), statistics, and programming (python and R). Anyone wishing to get into the field of data science must start by acquiring a strong background in Python and R programming language. I have discussed a list of free online courses that can enable you learn the basics of data science using Python and R in this article:
- I get requests to answer many variants of this question, so I’m going to put one general answer here, so I can point people to it. If you’re wondering whether you can be a data scientist, you can see what boxes you need to check to get there. There are two steps to being a data scientist. 1) You have to know how to do the things a data scientist is going to have to do on the job. 2) You have to be able to convince someone with the money to pay you that you have completed step 1. –, is a data scientist
What Do Exams and Licensing Involve?
Data scientist positions are some of the most in-demand in the country. Companies rely on their data and want to hire the best data professionals. If you want to stand out from other data scientists, you do not need a license, but data science certifications are critical.
Below are some of the most important certifications you can earn to increase your job prospects and earnings. (CIO.com)
- Certified Analytics Professional (CAP): Vendor-neutral certification that helps you transform data into valuable insights and company actions.
- Data Science Council of America Senior Data Scientist: Designed for professionals with at least five years of experience in analytics and research. You should understand databases, spreadsheets, statistical analytics, R, SAS, and quantitative methods.
- Dell EMC Data Science Track: You will learn the foundational principles of data science and big data analytics. Also covered are natural language processing, advanced analytical techniques, Hadoop, Pig, Hive, and HBase.