It’s no secret that we live in a data-driven world. But oftentimes, data in and of itself isn’t useful and must be interpreted to have real meaning for organizations.
That’s where data scientists come in.
Data scientists help us make sense of the data in the world. They translate large amounts of data into actionable insights. As more and more companies see the value in understanding the data they have at their disposal, the demand for data scientists is growing. This, paired with other factors such as good salary, work-life balance, and job stress levels, landed data scientists in the top ten in the U.S. News & World Report’s “100 Best Jobs” list in 2021.
Interested in a career as a data scientist? Here’s what you need to know.
IN THIS ARTICLE…
- What does a data scientist do?
- How to become a data scientist
- The traits a data scientist needs to be successful
What does a data scientist do?
Data science is a broad term that encapsulates many specific lines of work. Generally speaking, data scientists interpret and extract meaning from large sets of data to uncover insights, find patterns, forecast outcomes, improve products, and more.
Data science applications
Data scientists are behind everything from the algorithms that power your Netflix “Suggested For You” page to tools that help healthcare professionals identify and predict disease spread. If an industry uses digital technology of any kind, chances are that data can be analyzed to improve products, services, or business efficiency.
Here are just a few of the fields that data science plays an important role in:
- Banking and financial services: The financial sector uses data science in fraud detection, predictive analytics, management of customer data, and more.
- Politics: Political data scientists use data to predict voter turnout, partisanship support, and issue support.
- Healthcare: Data scientists are at the forefront of developing technologies that analyze medical imaging, understand genomic data, and predict the spread of disease.
- Manufacturing: Manufacturers use data science to optimize production, reduce costs, and predict production issues.
- Transportation: From making self-driving cars possible to optimizing rideshare services, data science is becoming increasingly important to the transportation industry.
- E-commerce: Data science allows online retailers to identify and reach their customer base effectively, predict product trends, and optimize pricing structures.
Though each of these industries use data in different ways, there’s one common denominator— data scientists are the professionals that aggregate and turn the data into insights that organizations can use to make informed decisions or build better products.
Duties and responsibilities of a data scientist
The exact duties and responsibilities may vary depending on the specific role a data scientist has or the industry, but in general, a data scientist may perform the following tasks day-to-day:
- Ask questions that begin the discovery process
- Acquire and analyze data to uncover trends and patterns
- Employ machine learning techniques and artificial intelligence to improve data collection
- Build algorithms and data models to create outcome forecasts
- Use programming languages like Python and R to analyze data
- Communicate findings to stakeholders and team members using data visualization techniques
Though data scientists work in coordination with team members and key stakeholders within organizations, you should also be prepared to carry out tasks and projects independently.
How to become a data scientist
Though there are a number of paths that a person can take in their data science career, nearly all start at the same place: a college or university.
For the overwhelming majority of jobs in data science, you will need to hold a bachelor’s degree at a minimum. Most professional data scientists, however, hold an advanced degree— 91% of data scientists have at least a Master’s degree and 48% have a PhD.
While a Master’s might not be required for entry-level roles, your opportunities for growth may be significantly limited without an advanced degree.
Data scientists typically earn bachelor’s degrees in concentrations like computer science, math, business, or IT. Many hold their master’s degrees and PhDs in data science.
Data science certifications
Data scientists may wish to bolster their resumes by attaining additional certifications. Though they are not required, certifications can make a candidate more competitive and desirable to an employer.
Traits a data scientist needs to be successful
Data science exists at the intersection of statistics, programming, business logic, and communication. That said, it’s a career that requires a wide range of skills and areas of expertise. Here are some common traits that most data scientists share.
Critical thinking and reasoning
Critical thinking, reasoning, and problem-solving allows data scientists to objectively analyze data, test hypotheses with experimentation, and assess the credibility of conclusions.
Data scientists think about the big picture. However, they must also pay fine attention to detail. From finding small coding errors to weeding out unreliable data points, attention to detail allows data scientists to carry out high-quality work.
For many data scientists, business acumen allows them to understand the mission and objectives of their employer. This understanding helps them work on problems that are aligned with their organization’s goals and will have the greatest impact on bottom lines.
Communication skills and data visualization
Large data sets may make sense in a data scientist’s mind, but other stakeholders may not as easily understand them. Data scientists must be able to communicate their findings in digestible ways. Oftentimes, this means creating visuals like charts and graphs.
Data scientists don’t just find the answers to questions using data. They are also often the ones asking the questions in the first place. Data scientists must have a natural curiosity that propels them to find problems and seek out the solutions.
Furthermore, an enthusiasm for learning also allows scientists to stay on the cutting edge of this ever-evolving field. As a data scientist, you must constantly learn new skills, methods, and technologies to effectively and efficiently do your job.
Explore a career in data science
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