What is a Data ScientistData scientists focus on analysis. They collect and clean data. Once it’s ready for use they interpret it, drawing meaning from the data to address practical business problems. 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
What is a Data EngineerWhile 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
Finding Common GroundData engineers build, optimize, and maintain the tools data scientists use to explore and interpret data. In other words, engineers supply the scientists with data and keep it under control while scientists turn the data into business solutions. The two fields work in tandem. 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 ViewInstead 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!