Working as a data scientist can be intellectually challenging, analytically satisfying, and put you at the forefront of new advances in technology. Data scientists have become more common and in demand, as big data continues to be increasingly important to the way organizations make decisions. Here’s a closer look at what they are and do—and how to become one.
What does a data scientist do?
Data scientists determine the questions their team should be asking and figure out how to answer those questions using data. They often develop predictive models for theorizing and forecasting.
A data scientist might do the following tasks on a day-to-day basis:
- Find patterns and trends in datasets to uncover insights
- Create algorithms and data models to forecast outcomes
- Use machine learning techniques to improve the quality of data or product offerings
- Communicate recommendations to other teams and senior staff
- Deploy data tools such as Python, R, SAS, or SQL in data analysis
- Stay on top of innovations in the data science field
Data analyst vs data scientist: What’s the difference?
The work of data analysts and data scientists can seem similar—both find trends or patterns in data to reveal new ways for organizations to make better decisions about operations. But data scientists tend to have more responsibility and are generally considered more senior than data analysts.
Many data scientists can begin their careers as data analysts or statisticians.
Read more: Data Analyst vs. Data Scientist: What’s the Difference?
Data scientist salary and job growth
A data scientist earns an average salary of $122,499 in the United States as of April 2022, according to Glassdoor
The high demand has been linked to the rise of big data and its increasing importance to businesses and other organizations.
How to become a data scientist
Becoming a data scientist generally requires some formal training. Here are some steps to consider.
1. Earn a data science degree.
Employers generally like to see some academic credentials to ensure you have the know-how to tackle a data science job, though it’s not always required. That said, a related bachelor’s degree can certainly help—try studying data science, statistics, or computer science to get a leg up in the field.
Already finished with college?
Consider getting a master’s in data science. At a master’s degree program, you can dive deeper into your understanding of statistics, machine learning, algorithms, modeling, and forecasting, and potentially conduct your own research on a topic you care about. Several data science master’s degrees are available online.
2. Sharpen relevant skills.
If you feel like you can polish some of your hard data skills, think about taking an online course or enrolling in a relevant bootcamp. Here are some of the skills you’ll want to have under your belt.
- Programming languages: Data scientists can expect to spend time using programming languages to sort through, analyze, and otherwise manage large chunks of data. Popular programming languages for data science include:
- Python
- R
- SQL
- SAS
- Data visualization: Being able to create charts and graphs is a significant part of being a data scientist. Familiarity with the following tools should prepare you to do the work:
- Tableau
- PowerBI
- Excel
- Machine learning: Incorporating machine learning and deep learning into your work as a data scientist means continuously improving the quality of the data you gather and potentially being able to predict the outcomes of future datasets. A course in machine learning can get you started with the basics.
- Big data: Some employers may want to see that you have some familiarity in grappling with big data. Some of the software frameworks used to process big data include Hadoop and Apache Spark.
- Communication: The most brilliant data scientists won’t be able to affect any change if they aren’t able to communicate their findings well. The ability to share ideas and results verbally and in written language is an often-sought skill for data scientists.
3. Get an entry-level data analytics job.
Though there are many paths to becoming a data scientist, starting in a related entry-level job can be an excellent first step. Seek positions that work heavily with data, such as data analyst, business intelligence analyst, statistician, or data engineer. From there, you can work your way up to becoming a scientist as you expand your knowledge and skills.
4. Prepare for data science interviews.
With a few years of experience working with data analytics, you might feel ready to move into data science. Once you’ve scored an interview, prepare answers to likely interview questions.
Here are a few questions you might encounter:
- What are the pros and cons of a linear model?
- What is a random forest?
- How would you use SQL to find all duplicates in a data set?
- Describe your experience with machine learning.
- Give an example of a time you encountered a problem you didn’t know how to solve. What did you do?
A data professional at IBM offers his advice for aspiring data scientists:
Getting started
Becoming a data scientist might require some training, but an in-demand and challenging career can be waiting at the end.
Are you just starting out in data science? Get a crash course in the basics with IBM’s Data Science Professional Certificate.
How to Become a Data Scientist
Data scientists are among the top jobs in the US. To become a data scientist, you’ll need a combination of technical skills and workplace skills and education.Learn more on how to become a data scientist from this guide.
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Careers as data scientists consistently rank among the top jobs in America. Glassdoor ranks data scientists as the third best job in America in their 2022 50 Best Jobs in America report.The job ranks high on best jobs lists because data scientists tend to command high salaries while experiencing high levels of job satisfaction.
If you are thinking about becoming a data scientist, you could be setting yourself up for a satisfying career. This article will break down exactly how to become a data scientist to help you decide this career path is for you.
Skills to sharpen
If you want to become a data scientist, you will need to sharpen both your technical and workplace skills, as both are essential for success in this field.
Technical skills
Technical skills that you will need to know to become a data scientist include:
- Big data technologies such as Hadoop, Hive, Pig, Cassandra, and MapReduce
- Data-driven predictive model development
- Ruby, R. Python, Matlab, C++, Java, SQL, SPSS, SAS, PHP
- Multivariate statistics, such as regression principal components analysis and clustering
- Large dataset management using Teradata, Oracle, or SQL
- Business intelligence tools such as MicroStrategy, Business Objects, and Tableau
Workplace skills
Being a data scientist involves working with many different software programs, computing languages, people, and data types. Data scientists constantly have to be able to figure out the best way to process data, analyze it, and articulate the findings to various people. As a result, to succeed as a data scientist, all the following workplace skills can be helpful:
- Open-mindedness
- Communication skills
- Teamwork
- Curiosity
- Persistence
- Agreeableness
- Leadership
- Articulation
Educational requirements
In most cases, you will need at least a bachelor’s degree in a related field to get an entry-level job as a data scientist. However, for many non-entry level jobs in the data science field, you will need a master’s degree. Roughly four out of every five data scientists have a master’s degree.
Degrees
The following are degrees that data scientists commonly earn:
- Bachelor’s degree in IT, computer science, math, or business
- Master’s degree in data science, computer science, or a related field
Certifications
Many certifications can help a data scientist in their career. Some of the top certifications for data scientists include:
- Certified Analytics Professional (CAP)
- SAS Certified Data Scientist
- Open Certified Data Scientist (Open CDS)
- IBM Data Scientist Professional Certificate
- Cloudera Certified Associate (CCA) Data Analyst
- Dell EMC Data Scientist Track (EMCDS)
- Google Professional Data Analytics Certification
- Tensorflow Data Scientist Certificate
Boot camps
Some data scientist boot camps can be helpful for you to become employed as a data scientist. Many of these boot camps take only 12 weeks and offer pay-after-you-are-hired structures. These can be very helpful for learning critical skills for data scientists. However, because 80 percent of data scientists have master’s degrees, you might be better off pursuing a master’s degree if you want to maximize your earning potential. It is possible to get an entry-level data scientist job with a boot camp certificate, and you can also take coding boot camps to improve your coding skills.
Salary range
According to ZipRecruiter, the national average salary for data scientists in America is $119,413.However, data scientist salaries range from $36,500 to $190,500. If you have more experience and are in the latter stages of your career, you may earn significantly more. The high salaries that data scientists command make this profession very attractive for math and science-oriented professionals.
Job outlook
According to the US Bureau of Labor Statistics, the number of data scientist positions in the United States is expected to increase by 22 percent from 2020 to 2030. The potential increase means that job growth for data scientists is about three times higher than the national average for all careers.
Careers as data scientists are still a relatively new concept. However, since the advent of big data, it is becoming more and more critical for large and mid-size companies to have a data scientist on their staff who can help them make the most of all of their data. Frequently, the insights that data scientists provide can have enormous benefits for companies.
Possible career pathway for a data scientist
The first step for anyone who wants to become a data scientist is to learn the technical skills mentioned above, which are required for this field.
You can do this through a boot camp, a bachelor’s degree, or studying independently. Having these skills is an essential requirement for becoming a data scientist. Once you have these skills, you will have a good chance of landing an entry-level position as a data scientist.
However, if you want to increase your chances of landing a data scientist position, you should complete a master’s degree in either data science or computer science. You should also try to earn as many of the certifications mentioned above. The more credentials you have, the better your chances of getting a good data science position.
After becoming an entry-level data scientist, you can move ahead to a mid-level data scientist and then a senior data scientist. You can typically expect a significant pay raise as you move up the ranks. The more years of experience you gain building and sharing projects, the better it is for your career. Senior data scientists earn an average of $20,000 more per year compared to non-senior data scientists, according to calculations performed by DataQuest on Indeed salary estimates.
Some people cross over from other industries, such as machine learning. If you know both machine learning and data science, it can increase your salary. However, if you plan to cross over from machine learning, you should make sure that you boost your skills and knowledge before making the switch.
Next steps
Following these steps can help you learn more about data science and pursue a career in this field:
1. Develop your abilities.
The first step to success as a data scientist is to develop your current abilities in any form of data science sector you desire.
2. Pursue education and certification.
Pursue a degree in data science and obtain all required forms of certifications. Refer to the list of the top types of certifications earlier stated in the article to check out what certificates you need.
3. Learn a language.
Learn your favorite coding language to the point that you can use it fluently in any situation to give yourself a head start in the data science industry.
4. Learn from other industry experts.
It’s helpful to learn from others as a beginner data scientist. Follow reputable data scientists on social media or other outlets to learn from their experiences and improve your present ones.
5. Create a portfolio.
To assist you in landing a data science job as a freelancer, or working for a company on-site, create a portfolio.
7. Develop your business and analytical abilities.
If you are interested in creating your own data science business, you would need to develop your business and analytical abilities, such as:
- Creative thinking
- Industry knowledge
- Analytic problem-solving skills
- Effective communication