Data scientists are highly educated – 88% have at least a master’s degree and 46% have a Ph.D. – and there are some notable exceptions, but typically require a very strong academic background to perform. To become a data scientist, you can earn graduate degrees in computer science, social science, physical science, and statistics.
The most common fields of study are Mathematics and Statistics (32%), followed by Computer Science (19%) and Engineering (16%). A degree in any of these courses will give you the skills needed to process and analyze big data.
The truth is, most data scientists have master’s degrees or PhDs and also take online training to learn a particular skill such as how to use Hadoop or Big Data querying. Therefore, you can enroll for a master’s degree program in the field of data.
2) R Programming
You must have in-depth knowledge of at least one of these analytical tools, which R is generally preferred for data science. R is specifically designed for data science requirements. You can use R to solve any problem in data science. In fact, 43 percent of data scientists are using R to solve statistical problems. However, R has a steep learning curve.
Especially if you have already mastered a programming language then it is hard to learn. Nevertheless, there are very good resources on the internet for getting started in R, such as Simple Learn Data Science Training with R Programming Language.
3) Python Coding
Python is the most common coding language commonly seen as essential in data science roles with Java, Perl, or C / C ++. Python is a great programming language for data scientists. This is why 40 percent of respondents surveyed by ‘average really’ use Python as their main programming language. Because
Due to its versatility, you can use Python for almost all the steps involved in data science processes. It can take various formats of data and you can easily import SQL tables into your code. This allows you to create a dataset and you can literally find any type of dataset on Google.
4) Hadoop Platform
Although it is not always required, it is preferred in many cases. Having experience with Hive or Pig can also benefit you a lot. Familiarity with cloud tools such as Amazon S3 can also be beneficial. In a study conducted by CrowdFlower, 3490 LinkedIn Data Science Jobs ranked Apache Hadoop as the second most important skill for a data scientist with a 49% rating.
As a data scientist, you may face a situation where the amount of your data exceeds your system’s memory or you need to send data to different servers, this is where Hadoop comes in. You can use Hadoop to quickly move data to different points on the system.
5) SQL Database / Coding
Although NoSQL and Hadoop have become a major component of data science, it is 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 perform operations such as editing, deleting, and extracting data from a database. It can also help you perform analytical tasks and change 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 with data. When you query a database it gives you insight.
It has brief commands that can help you save time and reduce the amount of programming required to perform difficult queries. Learning SQL will help you better understand relational databases and boost your profile as a data scientist.
Web Title: To become a data scientist, you must have the following skills
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