4. Hadoop Platform
Although this isn’t always a requirement, it is heavily preferred in many cases. Having experience with Hive or Pig is also a strong selling point. Familiarity with cloud tools such as Amazon S3 can also be beneficial. A study carried out by CrowdFlower on 3490 LinkedIn data science jobs ranked Apache Hadoop as the second most important skill for a data scientist with 49% rating.
As a data scientist, you may encounter a situation where the volume of data you have exceeds the memory of your system or you need to send data to different servers, this is where Hadoop comes in. You can use Hadoop to quickly convey data to various points on a system. That’s not all. You can use Hadoop for data exploration, data filtration, data sampling and summarization.
5. SQL Database/Coding
Even though NoSQL and Hadoop have become a large component of data science, it is still expected that a candidate will be able to write and execute complex queries in SQL. SQL (structured query language) is a programming language that can help you to carry out operations like add, delete and extract data from a database. It can also help you to carry out analytical functions and transform database structures.
You need to be proficient in SQL as a data scientist. This is because SQL is specifically designed to help you access, communicate and work on data. It gives you insights when you use it to query a database. It has concise commands that can help you to save time and lessen the amount of programming you need to perform difficult queries. Learning SQL will help you to better understand relational databases and boost your profile as a data scientist.