An explanation of data science and data scientist jobs
In this video, Informa TechTarget managing editor Sabrina Polin describes the field of data science and explains the role of the data scientist.
Data science makes meaning out of numbers.
In 2012, the Harvard Business Review famously claimed that the data scientist was the sexiest job of the 21st century. And in looking at how the internet, big data and AI exploded since then, we think they were on to something.
Data science is the field of using advanced analytics techniques to get meaningful insights from data. This insight is critical for businesses; data is only as useful as the insight you glean from it.
Here, we'll talk about the basics of data science and the role of the data scientist.
In some ways, data science is an amalgamation of other analytics practices. It incorporates elements of business intelligence, data engineering, predictive analytics, machine learning, math, statistics, programming … the list goes on.
But for the sake of time, the data science process involves:
- Identifying a hypothesis about a business concern.
- Gathering data.
- Developing and training analytical models.
- And running data through the chosen model.
For example, say sales are falling in the western region. A data scientist may see that there are more younger people in the region, and hypothesize, "Young people are less likely to buy our products, and that's why sales are falling in the west."
The data scientist would run sales data through the model they created. Let's say the results show that, yes, young customers are less likely to buy their product. Ideally, this will trigger business executives to make changes among the sales, marketing and product design teams.
The business will continue to feed new data into the working model that will show if the actions taken were effective in addressing the gap in younger patrons.
Data science can be helpful in any predictive modeling, pattern recognition, anomaly detection, classification and sentiment analysis applications. This could look like:
- In regular business operations, optimizing supply chains, product inventories, distribution networks and customer service.
- In healthcare, patient diagnosis, image analysis, treatment planning and research.
- In academia, monitoring student performance or improving marketing to prospective students.
- In sports, analyzing player performance and planning game strategies.
- In HR, creating aptitude tests and games to make better hiring decisions.
- And in streaming services, making both programming and viewer recommendations -- to name a few.
Most data science jobs require at least a bachelor's degree in a technical field, such as statistics, data science, computer science or math. However, most have advanced degrees, given that they need expansive knowledge of:
- Big data platforms like Hadoop, Kafka and Spark.
- Programming languages like Python, R and SQL.
- Data warehouse and lake structures.
- Machine learning frameworks.
- Data visualization tools.
- And much more.
Data scientists also need soft skills -- like communication, leadership and intuition -- to be able to lead a team and communicate findings with C-level business executives to effect change. This makes good data scientists hard to find, but they usually earn six-figure salaries.
So, what do you think? Was Harvard Business Review right all those years ago? Share your thoughts in the comments.
Sabrina Polin is a managing editor of video content for the Learning Content team. She plans and develops video content for TechTarget's editorial YouTube channel, Eye on Tech. Previously, Sabrina was a reporter for the Products Content team.