Because the work Data Scientists do touches so many different industries and disciplines, the roles Data Scientists can fill go by many different names, including:
There are many other variations out there, and these will continue to evolve as data science becomes ever more prevalent. But while the list of job titles in data science may seem to be never-ending, there are four main categories that describe the different roles Data Scientists most commonly fill.
Data Engineers collect, store, and organize data. Job ads for Data Engineers will typically list a range of responsibilities, including the ability to source external data, build data warehouses, and design data models – three tasks that also build a foundation for data analytics and machine learning. Data Engineer is a relatively advanced professional position, and so typically requires a background in computer science, math, or engineering, as well as knowledge of SQL, Python, Java or Ruby, and the ability to manage and design databases.
Data Analysts use the data organized and made accessible by the work of a Data Engineer, turning it into insights that can solve problems, optimize products, and help make evidence-based decisions. Data Analysts can take complex information and turn it into stats that business execs can use to inform strategy and planning, often in the form of easy-to-understand data visualizations like charts and graphs. Related job titles include Operations Research Analysts and Business Intelligence Analysts. SQL is the foundation for a career in data analytics, as well, alongside knowledge of Python or R, and the ability to create data visualizations using software like Tableau.
Depending on the company, people with the job title of “Data Scientist” might be expected to do the work of a Data Engineer and Data Analyst (collect, organize, and analyze data), as well as more strategic data work. Where the Data Scientist role differs from the Data Analyst and Engineer’s role is in the Data Scientist’s ability to lead a company’s big data strategy by asking the right questions and developing new ideas, products, and services. Here, knowledge of Python, SQL, and Tableau are key, alongside other programming languages, an understanding of how databases are built and maintained, strong communication skills. and business acumen.
There is quite a bit of overlap between Data Scientists and Machine Learning Engineers; both work with data to produce insights. The difference is that Data Scientists uncover insights to present to people (for example, CEOs and other business leaders), while Machine Learning Engineers design the software that can uncover insights and learn from results as more and more data is gathered. Machine Learning Engineers depend on advanced math skills, programming skills (in Python, R, and Java), knowledge of Hadoop, data modeling experience, and experience working in an Agile environment.
The good news is that almost all of these positions are in great demand. If you have data science skills and experience, you are already in a great position when it comes to career development and progression.
IDSA courses are designed to prepare students for real commercial work in the area of data science, AI and big data.
IDSA trainers are actively working in the industry and will teach you how to practice data science and advanced analytics.
The member network offers support and guidance from mentors and direct connections to the industry. You will meet employers in-person at IDSA events.