The data science profession is exploding, but what does a data scientist do exactly? Basically, think of them as modern-day detectives who solve business mysteries with data. They take mountains of information and turn it into useful insights that help companies make smarter decisions. While data analysts focus on interpreting existing data to help with day-to-day decisions, data scientists go deeper. They use the scientific method, software development tools and advanced analytics to create new ways to understand data, build predictive models, and develop tools that make sense of complex information.
Moreover, companies can’t hire data scientists fast enough, with over 100 thousand positions open and projected 36% growth in the next 10 years. That’s more than five times faster than most other jobs. Whether you’re considering a career change or just curious about the profession, this article will look at why businesses need data scientists. Also, I’ll detail what skills data scientists need, their educational background, and what a typical day looks like in this technical field.
1. What Is A Data Scientist?
To keep this simple, below are some definitions for data scientist and data science.
Data Scientist Definition
“Uses scientific methods (data science) to liberate and create meaning from raw data.”
Data Science Association
Data Science Definition
“… is the discipline of making data useful.”
Cassie Kozyrkov, Chief Decision Scientist, Google
So a data scientist conducts basic and applied research using data. Further, they use the scientific method to create meaning, data models, and software applications. Lastly, for a more detailed discussion of the definition of data science, see my article, Data Science Definition – The Truth About This Discipline And Its Massive Growth
“The best minds of my generation are thinking about how to make people click ads. That sucks.”
Jeff Hammerbacher, Data Scientist
2. What Does A Data Scientist Do To Empower Businesses?
Data scientists make data useful by helping businesses make smarter decisions. Specifically, they build models and algorithms that reveal patterns in data. Additionally, they work closely with all departments in the organization to develop data-driven solutions. Below, are two lists that further detail exactly what does a data scientist do to empower businesses. This includes a listing of a data scientist’s typical technical functions and a listing of the different business disciplines they help.
a. Examples of Use Cases Where Data Scientists Empower Organizations.
Here are seven things that data scientists can do for businesses:
1) Anomaly Detection.
First, they optimize a financial application to detect fraud.
2) Pattern Recognition.
For instance, retailers use data scientists to discover purchasing patterns.
3) Predictive Modeling.
Also, businesses use data scientists to improve business forecasting.
4) Recommendation Engines and Personalization Systems.
For example, online streaming and retailers use data scientists to help consumers make product selections. Also, this can include full-blown decision intelligence platform to assist executive decision-making and even automate decision flows.
5) Classification and Categorization.
Additionally, data scientists create or optimize systems to automatically categorize documents and images
6) Sentiment And Behavioral Analysis.
For instance, marketing departments use data scientists to identify buying and usage patterns of customers.
7) Conversational Systems.
Lastly, data scientists can create and optimize programs to support customer service and research functions.
See Techtarget’s 8 top data science applications and use cases for businesses and Edureka’s Top 10 Data Science Applications for a more detailed explanation of the use cases, tasks, and functions where data scientists can help businesses.
b. Different Business Domains Where Data Scientists Work and Help Organizations.
Data Scientists are often hired by different industries and business professions. For example, business functions can include marketing, finance, IT departments, and operations to name a few. Also, Data scientists can also work in different industries and domain areas such as healthcare and cybersecurity.
Moreover, data scientists have a variety of education backgrounds with the majority having either a masters or doctorate degree. Also based on their experience and educational background, they usually specialize in one area of data science and a specific business domain. Surprisingly, data scientists primarily educational backgrounds is not necessarily just statistics or computer science. Indeed, it is more diverse than just hard-science degrees. To list, see below for the primary educational backgrounds of data scientists today.
Listing of Educational Backgrounds of Data Scientists
- Economics And Social Sciences. This includes economics, finance, business studies, politics, psychology, philosophy, history, and marketing and management.
- Natural Science. This includes physics, chemistry, and biology.
- Statistics And Mathematics.
- Computer Science. Also, this includes machine learning and artificial intelligence (AI).
- Engineering.
- Data science And Analysis.
- Other. As data science is transforming and industry or profession that has data, data scientist can also have degress in Art and Design and Atmospheric Science to name a few.
For a more detailed discussion on the education backgrounds of data scientists, see 365DataScience’s The Data Scientist Profile.
3. Best Tech And Non-Tech Skills For Data Scientists.
Data scientists must have a strong foundation in mathematics, statistics, computer science, and machine learning. In addition, data scientists must have strong coding and programming skills, experience with data mining and data visualization tools, and knowledge of data-related technologies. On the other hand, non-technical skills such as communication and problem-solving are also essential for data scientists. To list, here are the top skills for data scientists.
a. Hard Skills For A Data Scientist.
1) Education.
Not surprisingly, data scientists are highly educated – 88% have at least a Master’s degree and 46% have PhDs.
2) Analytical Tools.
Also, they need an in-depth knowledge of at least one analytical tool like R. In fact, R is designed for data science needs.
3) Programming Language.
Moreover, data scientists ideally should have experience in a coding language like Python. Also, there are other acceptable coding languages such as Java, Perl, or C/C++.
4) Unstructured Data Tools.
Ideally, data scientists have experience with software that can handle unstructured data such as video, audio, images, social media posts, etc. For example, this can include software frameworks for storage and large scale processing of data-sets such as Hadoop or Apache Spark. Additionally, experience in Hive, Pig, or in Amazon S3 is beneficial.
5) Database Query Building.
Many large businesses in finance and logistics are transactional-based and have their data stored in SQL databases. So a data scientist needs to have experience writing and executing complex queries in SQL. Also, their database work can include knowledge graphs, meta-data, and other methodologies to query unstructured data and data from dissimilar data structures.
6) Machine Learning and AI.
Also, more and more Artificial Intelligence (AI) is data-driven. Hence if data scientists are working in the area of AI, there is a need for them to be proficient in machine learning areas and AI applications.
7) Data Visualization / User Interface (UI).
In many cases, data scientists are solving real-world problems. So in order to present information in a comprehensive manner, data scientists need to translate their results visually using data visualization tools and other types of user interfaces (UI).
b. Soft Skills For Data Scientists.
1) Intellectual Curiosity.
It would be extremely difficult to be a data scientist, if you were not intellectually curious. Indeed, this is a profession where you continually do research, learning new things, and always asking why. To explain further, see Unvarnished Facts, Intellectual Curiosity – How To Make It Better, It’s More Than Just Pointless Browsing.
Intellectual Curiosity – How To Make It Better, Its More Than Just Pointless Browsing. Many of us endlessly browse the internet watching cute cat videos or funny YouTube “fail” videos. We do this because it is part of our human nature to be curious. However, this type of curiosity is not what “put a man on the moon” or painted the Mona Lisa. This is because the curiosity that is the source of creativity and innovation is intellectual curiosity. Click Here for an explanation of the three types of curiosity, why intellectual curiosity is important, and how to improve your intellectual curiosity.
2) Business Acumen.
Most data scientists are focused on a business problem. So you need to be knowledgeable of the industry and data that you are working with. Further without domain knowledge, a data scientist can easily go off track and not produce anything actionable for the business.
3) Communication Skills.
Data scientists are dealing with large data sets that are difficult to fathom even for an expert. Thus, data scientists require strong communications skills to translate their technical findings to people who work in non-technical such fields as marketing and operations.
4) Teamwork.
Data science is a team effort. For instance in many companies, a data science initiative involves many departments such as IT, finance, marketing, and operations. Also in many cases, the data scientist is the technical lead. Further, the data scientist is working with higher management, department stakeholders, and diverse team members to include software developers, data analysts and domain experts. Obviously, a data scientist cannot work alone and needs to be a team player.
For a more detailed explanation of data scientist skills, see Kdnuggets’ 9 Must-have skills you need to become a Data Scientist, updated.
4. The Scientific Method – What Does A Data Scientist Do Every Day?
Recently, data scientists have begun to document the unique scientific method for the emerging field of data science. As with any scientific process, the process starts with a statement of the problem. Moreover, this problem is usually a business problem where the business stakeholders have tasked the data scientist to research and come up with a possible solution. See my article, Scientific Method Example which is key methodology used by data scientists.
Scientific Method Example: Data Scientists Use An Unique Way To Achieve The Best Results.
The field of data science provides an excellent example of the scientific method within a specific scientific community. This emerging and rapidly expanding field demonstrates the universal application of the scientific method across various disciplines. However, distinctions exist depending on the discipline. As with any developing science, data scientists continually refine their methods for acquiring knowledge, as well as their use of scientific tools and techniques. Click here for a scientific method example of data science that exemplifies the scientific method while also noting variations that may occur when applying it in specific scientific disciplines.
“The world is one big data problem.”
Andrew McAfee
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