Data scientists are increasingly important to organizations as decisions become more data-driven. You need to be able to tell the real and talented data scientists from a charlatan who talks the talk. Here are five questions every data scientist should be able to answer.
What is data science, and how does it differ from data analysis? It's one thing to say you can look at data and tell what it means. It's another thing to be able to show replicable results and develop new methods for discovering things.
What can't data scientists do? A real data scientist will easily be able to describe the limits of data science today and what progress is being made.
How do we balance privacy with the collection? This should be top of mind for all data scientists, if for no other reason than dealing with GDPR. On the flip side, any data scientist who claims to have perfectly solved this balance should be viewed with extreme skepticism.
What data is most useful for my business? Collecting data can be the easy part even today. Deciding what data to collect because it's value is where the science comes in. It's tempting to just collect it all and worry about it later, but that's not only increasingly more unpopular, but it also slows things down as well.
What production-level machine learning techniques do you work with? Unless you're specifically hiring a research data scientist, you need to know that the person can carry out concrete applications.
These Are the courses for Data Scientists To Boost Their Career:
1. Mining Massive Data Sets Graduate Certificate:
This certification is important for gaining the fundamentals of data mining, which is the start to the data science process, Circelli said. "While it can be an expensive course with larger universities, it is one of the most important to take into consideration given that it helps talent develop core data mining skills," he added. "After all, how can you manipulate data if you can't even understand it? Once you're able to aggregate data effectively, then you'll be able to manipulate the data to best serve your needs."
2. PGP in Big Data Analytics and Optimization:
This hands-on course can be completed on nights and weekends a huge plus for working data scientists and other professionals, Circelli said. The course specializes in coding various data manipulation programming languages, like Python and R, which can be used in ecosystems like Hadoop and Spark. "It also helps highlight how to best manipulate data using the code with a hands-on approach versus a more conceptual approach that you'll find with other certifications," Circelli said.
3. Cornell Data Science Certification:
This certification demonstrates skills in using predictive data analysis as a marketing tool. "This certification is awesome because it has so many different areas available for you to choose from in terms of what you want to focus in, from Business Analytics to Data-Driven Marketing," Circelli said. "So you can truly specialize and build the niche skills for the area you want to focus in. It's also more affordable than other certifications, but definitely still an investment."
4. Hortonworks Certified Associate (HCA):
This is a more beginner-level associate course that specializes in hands-on experiences with industry-standard data manipulation tools like Hadoop, Spark, Pig, Hive, and Solr to help build fundamental skills, which can translate to a variety of data science roles, Circelli said.
"Since there are so many different certifications out there, you can get very specific to the specialized skill development you need," Circelli said. "I will say the primary con is these certifications tend to be expensive, but are always worth the investment."