The demand for data scientists is projected to grow by 36% from 2021 to 2031, according to data from The Bureau of Labor Statistics. Within this period, about 13,500 job openings will be available each year. This goes to show that data scientists are still in great demand in 2023.
While data scientists require skills in project management, programming, statistics, machine learning and visualization, their work primarily involves the use of analytical tools and techniques to extract insights from data.
If you’re interested in breaking into the well sought-after field of data science, we’ve created a guide with the most important details and resources.
Current demand for data scientists
According to reports, 97.2% of companies are investing heavily in data and artificial intelligence projects, and among the companies sampled, 24% of them now use big data analytics. This rapid growth in data and AI projects follows the rapid technological advancement affecting every sector.
With many companies relying on intelligent data to some degree to make business decisions, the need for skilled professionals who can analyze that data and glean business insights increases.
SEE: Discover why reskilling will be necessary as AI changes how we work.
Given the sheer volume of the data businesses need to analyze, using traditional data management tools like spreadsheets and traditional databases will not suffice. Hence the need for data scientists who can use big data analytics tools to help businesses analyze data in real-time and make data-driven decisions from them.
What are some of the data scientist job roles?
Generally speaking, data scientists mine data and analyze it for specific company interests. They then work with marketing departments to capitalize on that knowledge. These workers must be familiar with data-gathering software, programming and warehousing techniques.
As data can be implemented in a variety of use cases, the role of data scientist takes on different forms.
A data scientist analyzes complex datasets to uncover insights and trends, builds predictive models, and translates data into actionable insights. They are in charge of researching and developing new algorithms and data models.
SEE: Here are some signs you may not be cut out for a data scientist job.
According to Glassdoor, the annual salary of a data scientist in the U.S. is estimated to be about $152,000, with an average of $117,656.
Data analysts are responsible for visualizing, transforming and manipulating data. They are often in charge of preparing the data for communication by making reports that show trends and insights.
Depending on level of experience, education and certification, the salary of a data analyst, according to Salary.com, ranges between $74,787 and $93,484 per year.
Data engineers are responsible for designing, building and maintaining data pipelines. They make sure the data is ready to be processed and analyzed. Data engineers need to keep the ecosystem and the pipeline optimized and efficient.
Based on the salary range published on Payscale, data engineers are paid between $96,000 to $135,000 per year.
A data architect is similar to a data engineer. They both need to ensure the data is well-formatted and accessible. While a data engineer constructs and maintains data pipelines and infrastructure for efficient data processing, a data architect designs the overarching data ecosystem, including standards, models and strategies, to support organizational goals.
The estimated annual salary of a data architect, according to Glassdoor, is $155,022.
This is the newest job role in this list. Data storytelling is not just about visualizing the data and making reports and stats. Rather, it is about finding the narrative that best describes the data and using it to express it. The data storyteller helps people understand the data.
The salary of a data storyteller ranges from $67,000 to $106,500 per year, according to ZipRecruiter.
Machine learning scientist
A machine learning scientist works with large datasets to design predictive and analytical systems that can learn from data, make predictions and extract valuable insights from them.
According to Glassdoor, the estimated salary of a machine learning scientist is $163,959 per year, with an average salary of $128,663 per year.
Machine learning engineer
Machine learning engineers need to be familiar with the various machine learning algorithms like clustering, categorization and classification and need to be up-to-date with the latest research advances in the field. Machine learning engineers need to have strong statistics and programming skills in addition to some knowledge of the fundamentals of software engineering.
SEE: Here’s everything you need to know about becoming a machine learning engineer.
Based on Indeed, a machine learning engineer is paid an annual salary of about $159,851 per year.
Business intelligence developer
Business intelligence developers design and develop strategies that allow business users to find the information they need to make decisions quickly and efficiently. BI developers need to have at least a basic understanding of the fundamentals of business models.
The annual salary of a business intelligence developer ranges from $94,697 to $139,000, as per Indeed.
Database administrators ensure the optimal performance, security and reliability of database systems in organizations. They are responsible for creating backup and recovery strategies to safeguard data integrity, collaborating with developers to optimize query performance and enforcing data access and security policies.
According to ZipRecruiter, the average salary of a database administrator is $98,248 per year.
Technology specialized roles
As the data science field grows, more specific technologies will emerge. As a result, new specialized job roles will be created to manage these technologies. These job roles apply to data scientists and analysts as well.
What skills do you need to be a data scientist?
Here are the 11 marketable skills a data scientist might need, according to an Indeed report:
- Cloud computing.
- Statistics and probability.
- Advanced mathematics.
- Machine learning.
- Data visualization skills.
- Query languages.
- Database management.
- Python coding.
- Microsoft Excel.
- R programming.
- Data wrangling.
“If you’re looking to enter the field of data science and build a solid foundation of experience that will stand out in the eyes of future employers, there are three core skills you need: Python, R and SQL,” said Pablo Ruiz Junco, Glassdoor economic research fellow. “With these skills, you’ll be eligible to apply to over 70% of all online job postings for data scientist roles. Plus, expanding your skills beyond these foundational languages can lead you to a higher salary and allow you to cast a wider net when applying.”
SEE: Explore these top programming languages data admins should know.
What is the average salary of a data scientist?
Average salary figures differ slightly for U.S. data scientists depending on which job site you look at. For example, Indeed says the average base pay is $124,528, while Glassdoor says the average base pay for the position is $117,658.
Data scientists in Palo Alto, California, receive the highest pay, at $166,954, followed by San Francisco, at $157,969 and Bellevue, at $147,413, according to Indeed.
The Bureau of Labor Statistics said the median pay for a data scientist with a master’s degree in 2021 was $131,490 per year.
With the salary differences between core data scientists, researchers and big data specialists, the skills that individual data scientists bring to the table can have a large impact on pay. Job seekers should consider what role they are most interested in and make a cost-benefit analysis of which skills are worth spending time learning.
What are typical interview questions for a career in data science?
A junior data scientist can expect questions like the following in a job interview, according to Forrester analyst Kjell Carlsson:
- Walk me through the project you are most proud of where you used data, data science, machine learning or advanced analytics. What was your role on the project, and what did you do in each step?
- Tell me about a project where you used (insert programming language or skill here).
- Tell me about a time you had to work with someone who is not data-savvy on a data science project.
- Tell me about a time when you had to become an expert on a new technique quickly.
The interviewee might be given a mini-case study based on a data science project the team has undertaken, with questions such as: What data would you need? What are the hypotheses you would like to test? What technique(s) would you use to evaluate them?
SEE: Check out these data scientist interview questions to ask employers.
For more senior positions, these questions may come up, according to Daniel Miller, vice president of recruiting at Empowered Staffing:
- Have you built a data warehouse from scratch? If so, tell me about the process you created in order to successfully implement the data warehouse. If they have not been part of it from scratch, interviewers may ask if the interviewee has been part of a department that dealt with a company merger or acquisition of data and how they handled it.
- What types of customized dashboards have you built, and what information or analytics were being presented through your dashboard?
- Tell me about the most complicated data project you have worked on, and what you were able to do in order to achieve success.
Data scientist education and certification requirements
To become a data scientist, the education and certification requirements can vary based on your background, experience and the specific roles you’re aiming for. However, here’s a general overview of what is commonly sought after.
- Bachelor’s degree: A strong foundation in mathematics and programming is essential, so many data scientists hold a bachelor’s degree in a related field such as computer science, mathematics, statistics, engineering, physics or economics.
- Master’s or doctoral degree: These are optional but increase a candidate’s chances of landing more advanced or research-oriented positions, as they indicate a deeper knowledge of data science concepts and techniques.
Some key certifications include the following:
- Certified Analytics Professional (CAP): This certification does not focus on a specific commercial platform but prepares candidates to learn how to get big data insights. It is valid for three years, and candidates are required to pass the Associate Certified Analytics Professional exam before they can receive a CAP certification.
- IBM Data Science Professional Certificate: This certification is offered on platforms like Coursera, edX and IBM Training and covers data science tools, methodologies and programming languages.
- Microsoft Certified: Azure Data Scientist Associate: Candidates with the Azure Data Scientist Associate certification are expected to possess specialized knowledge in the application of data science and machine learning on the Azure platform.
- Google Cloud Professional Data Engineer: There is no prerequisite for going for this certification. It focuses on data engineering and using Google Cloud technologies.
The market for data analysis tools has exploded over the past several years, with numerous platforms available for organizations of all sizes and suited for all industries. Here are some data science tools any prospective data scientist should check out.
Tableau is a powerful data visualization tool that connects to various data sources and simplifies creating interactive dashboards. Learning Tableau enhances data scientists’ ability to present findings, drive data-driven decisions and make their work accessible to a broader audience.
SEE: Check out our cheat sheet on Tableau.
TensorFlow is an open-source tool for machine learning, especially for advanced techniques like deep learning. Proficiency in Tensorflow enables data scientists to leverage deep learning for complex tasks like image generation, natural language processing and reinforcement learning.
SEE: Learn more with our cheat sheet on TensorFlow.
Jupyter Notebooks is another important tool in any data scientist’s arsenal. It offers an interactive web-based environment for combining live code, visualizations and sharing analyses in an understandable format. Data scientists benefit from Jupyter Notebooks for communication and collaboration.
SEE: Discover how Jupyter Notebooks compares to Google Colab.
Python’s versatility, extensive library and ease of use make it essential for any data scientist. With Python, you can run data manipulation, analysis, visualization and machine learning workloads.
SEE: Explore our Python cheat sheet.
This post originally appeared on TechToday.