As technology advances, areas that were once covered by the same position have become more specialized.
Nowhere is that more distinct than the field of Data Science.
With so many evolving disciplines covered by that one umbrella term, it can be hard for executives to distinguish the exact type of specialist to use for a particular project.
To confuse the matter, job titles in Data Science are often very close to each other while having nearly opposite areas of interest.
Take data scientists and data engineers, for example. The disciplines are commonly confused for each other.
While each could probably do some part of the other’s job, however, their primary functions address different segments of the Data Science process.
What is a Data Scientist
Data scientists focus on analysis. They collect and clean data.
While data scientists need to have a solid grounding in statistics and computer programming, they should be familiar with business science, too.
It’s their job to find real-world value within data. To do that they that need to identify business challenges and decide which specific data-analytics solution is best suited to provide answers.
Data scientists are also responsible for visualization methods that bring data to the average team member.
Not everyone is versed in technical jargon, but visual representations let anyone with understanding of the business interpret data through dynamic models.
Some typical responsibilities a data scientist might have include:
- Statistical modelling
- Machine learning algorithms
- Data mining
- Data cleaning and preparation
- Automating work with predictive analytics
- Presenting data for enterprise use
There are a number of tools they might use to accomplish these tasks. Statistics programs like SPSS, MatLab, and SaS are common.
As far as programming languages they might prefer R, C++, or Python (Python is popular).
Data scientists with a focus on predictive analytics and machine learning are likely to be familiar with RapidMiner.
What is a Data Engineer
While data scientists are concerned with preparing and interpreting data, data engineers have a material focus: architecture.
They’re in charge of the “data pipeline” that feeds other disciplines.
Data scientists design and build systems that accept, store, share, manipulate, and maintain data.
What exactly does that entail? Data engineers are generally responsible for:
- Data warehousing
- ETL (Extract, Transform and Load)
- Collecting and managing data
- Large scale processing systems
Some data engineering software, like Hadoop, overlap with the typical data scientist toolkit.
Data engineers use MySQL and NoSQL database tools. Warehousing software such as Hive and database management systems (DBMS) like Oracle are fairly well-established tools as well.
Finding Common Ground
Data engineers build, optimize, and maintain the tools data scientists use to explore and interpret data.
There is a skill overlap, but since nearly everyone specializes it would be unreasonable to expect them to do each other’s jobs.
Finding one person who can oversee the data architecture while simultaneously doing regular data science duties is a Herculean task.
The combination is so rare that HR managers jokingly call data scientists who also do data engineering “unicorns”.
Taking a Practical View
Instead of trying to navigate the subtle nuances of data science titles, many companies sidestep the issue by outsourcing their data science needs.
There’s also a growing trend towards self-service analytics, where analytics tools built into enterprise apps or other internal software let executives handle their own data.
What data science skills does your company lack? Concepta’s developers can help fill the gap with the latest data science and business intelligence tools. Schedule your complimentary consultation to find out more!