A great data scientist combines expert knowledge of various interrelated academic disciplines to help global enterprises make agile decisions for improved business performance. Data scientists use statistics, mathematics, data mining, and computer science to analyze data sets for observable trends and patterns. They are also experts in data collection and storage methods. The Bureau of Labor Statistics had predicted that as one of the “top 10 fastest-growing occupations in the U.S.,” the field of Data Science will grow 36% between 2021 and 2031.
Data Science, as a scientific discipline, is continuously evolving, so having a “can-do” attitude is probably the most prized skill that any aspiring data scientist can possess. As newer and better data technologies and tools continue to emerge, learning and mastering them will remain a constant challenge. Apart from the core technical skills, which this article highlights later, a data scientist will also need to develop many semi-soft and soft skills to succeed.
Data Science involves using highly mathematical or statistical methods to solve day-to-day business problems. Statistics is at the core of Data Science explorations, so a solid knowledge of statistical theories, along with hands-on experience in programming languages like Python and R, and a deep interest in machine learning (ML) and deep learning (DL) will set a great data scientist apart from their competitors.
The final penchant of a data scientist is a lifelong passion for data and data technologies. The successful data scientist will continue to develop newer and better data technology skills and apply them when opportunities arise.
Though these technical qualifications are critical for a data scientist to succeed on the job, they are by no means exhaustive. The data scientist working in the world of business will need to develop an additional passion: understanding business domains. The rising stars in this profession will not only manipulate massive amounts of data using complex statistical and visualization techniques, but also exhibit sound judgment in making data-enabled business decisions.
Shashi Upadhyay, GM/VP of Google, once referred to data scientists are “unicorns” possessing diverse skill sets not typically found in one person. Recent research shows that 90% of data scientists hold a master’s degree or Ph.D.
All aspiring data scientists in 2023 are expected to demonstrate three distinct sets of qualifications:
- Technical skills like math, statistics, programming, data wrangling, data visualization, and machine learning skills
- Semi-soft skills like project management and business skills
- A set of soft skills or personality traits like a great deal of curiosity, good communication skills, team-playing skills, ethical hacker-spirit, and can-do attitude
In 2023, a great data scientist will be expected to be part mathematician, part statistician, part programmer, part database expert, part ML expert, part business expert, and part storyteller. Sound tough?
The Data Scientist Role in 2023: A Quick Overview
As mentioned before, data scientists use their combined knowledge of mathematics, statistics, data mining, and computer science to solve business problems with data. Thus, this role blends many important skills and leaves an open challenge to keep up with ever-evolving data technologies and tools.
At the workplace, data scientists must frequently demonstrate superior problem-solving skills and take immediate corrective actions to fix errors in a learning model. A data scientist is also expected to be able to read and debug their computer programs, as a single mistake could cause major differences in the data analytics project.
Anyone interested in entering the field can explore the following links describing Data Science academic programs in the U.S.:
Additionally, here is a useful article about entering and surviving in the field of Data Science.
Essential Skills for a Great Data Scientist in 2023
Here are the core technical skills or knowledge that will be expected of a data scientist in 2023:
- Statistical techniques such as maximum likelihood, distributions, estimation, logistic regression, clustering, and linear regression
- Strong fundamentals in mathematics and programming languages like Python, Java, and R
- Knowledge of big data, Hadoop, Oracle, or SAP HANA
- Knowledge of data visualization tools like Tableau, Qlikview, Plotly, or Sisense
- Knowledge of artificial intelligence (AI) and machine learning (ML) and the ability to apply them to Data Science practices
- Strong analytical and problem-solving skills to conduct data analytics and extract insights for making predictions and recommendations
- A keen understanding of business issues
- Ability to craft excellent visuals and stories to communicate data insights and convey results to the end users.
Mathias Golombek of Exasol explains why storytelling matters in a Data Science career.
Finally, the data scientist will need to work with professional peers, cross-functional teams, and business managers, so good interpersonal and communication skills will help a data scientist to move forward.
Desirable Qualities of a Data Scientist in 2023
Here is a list of behavioral traits expected in a career data scientist in 2023:
- Curious by nature with explorative capabilities
- Highly ethical
- Does not give up easily till a problem is solved
- Interested in acquiring business knowledge
- Hacker spirit
- Blend of creativity and technical skills
- Soft skills like communication, lifelong learning, team playing, and ethical hacking skills
While having some hands-on experience and a theoretical grasp of various concepts in Data Science are important for achieving success individually, all data scientists need to take
into account that soft skills are also important to move up the corporate ladder.
Wrapping Up
When planning your next hiring decision in Data Science, seek candidates who demonstrate a healthy blend of data intuition, statistical reasoning skills, hacker spirit, and creativity alongside more technical skills. A skills-based technical interview will let you know whether a candidate has a strong Data Science and big data analytics background, but automated tools can mask mathematical and statistical talents – or lack of them. Having deep knowledge of AI and ML would allow any data scientist to tackle and solve difficult problems related to forecasting or setting future goals.
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