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Data science skills have gained prominence like never before. “In this new world, data is the new oil; and data is the new wealth” quoted the richest man in India, Mr. Mukesh Ambani.
“Data is the new oil”: Mukesh Ambani.
The arrival of the computer and the subsequent arrival of the age of the Internet has made humans depended on technology. It has given birth to the prominence of data. Today, the top five giants of the tech world – Apple, Amazon, Facebook, Google, and Microsoft – use data science skills to know more about us than we ever will. Companies gather data from hundreds of millions of users every single day. According to a report from IBM, in 2015 there were 2.35 million openings for data science jobs in the US. It estimates that the number will rise to 2.72 million by 2020.
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All these changes in global corporate and social dynamics have made data scientists a hot commodity for corporations across the globe.
Following are a few of the most in-demand data science skills for data scientists:
Developed in 1990, R is a highly preferred language. R is specifically designed for data science needs. One can use R programming to decipher any problem that emanates in data science. One really big advantage of R, however, is its extensibility. Developers can easily write their own software and distribute it in the form of add-on packages.
Python is one of the most popular coding languages being used in data science and IoT. Tiobe analysts believe python could replace C and Java as the most popular language in three years. Its popularity is being driven by large numbers of beginners moving into software engineering and its usability. Companies like Netflix use Python everywhere, including for building recommendation algorithms.
Although this isn’t always an essential requirement, it is preferred in solving complex data science problems. Often data scientists face local memory problem, which means the volume of the data exceeds the memory of your machine/computer. At times like these different servers are required, and Hadoop helps save the day.
Even though NoSQL and Hadoop are two essential components of data science, it is still expected that a potential data scientist can write and execute complex queries in SQL. SQL 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 carry out analytical functions and transform database structures.
Apache Spark has bloomed into one of the most popular big data technologies in the world. Apache Spark is explicitly designed for data science, to help run its complicated algorithm at a rapid speed. It helps in distributing data processing when you are dealing with enormous data through cloud servers, hence, it saves an ample amount of time. It also helps data scientists in handling complex unstructured data sets. It prevents loss of data in data science.
The business world produces a vast amount of data, more frequently than one can imagine. All the data needs to be translated into a format that is easy to understand. People understand pictures and visuals in forms of charts and graphs more easily than raw data.
As a data scientist, one ought to be able to convert raw data into visualized data with the help of data visualization tools such as ggplot, d3.js, and Tableau. These tools will help you to convert complex results from your projects to a format that will be easy to comprehend.
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According to SlashData, there are now 8.2 million developers in the world who code using Python and that population is now larger than those who build in Java, who number 7.6 million. Last September, there were seven million python developers and 7.1 million Java developers.
Python adoption has been rapid, with SlashData estimating python’s growth more than two million in 2018.
Also you like this article: 10 Most Used Data Science Tools You Must Know in 2019
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