Data Science is everyone’s word of the mouth in the current analytical eco-space. The study of Data Science which encompasses various subjects like Machine Learning, Deep Learning, Artificial Intelligence, Natural Language Processing, and so on has made tremendous advancement in the recent past.
Data Science is not something that emerged recently. It was there since computers were invented as the first Data Science application was classifying an email as Spam or Not Spam based on certain trends in the mail. However, the recent hype is a result of the massive amounts of data that are available, and the huge computational capacity that modern computers possess.
In terms of career, Data Science is considered as one of the most lucrative jobs in the 21st with salaries next to none. Hence, out of the curiosity to mine insights from the data, and also for a better career, professionals from various disciplines such as Healthcare, Physics, Marketing, Human Resource, IT, want to master the state-of-the-art Data Science methodologies.
To be called a Full Stack Data Scientist, one needs to master a plethora of skills as mentioned below.
Apart from these four basic skills, there are few other skills like building data pipelines are also important, but on most occasions, an organization would have a separate team for that.
In layman terms, Data Science is a process of automating certain manual tasks to mitigate the resource, budget, and time constraints. Thus learning to code is an important component to automate those tasks.
To build a simple predictive model, the data set should be first loaded and cleaned. There are several libraries, and packages available for that. You need to choose the language to code, and use those libraries for such operations. After the data is cleaned, there are several programmed algorithms which need to be used to build the predictive model.
Now, each algorithm is a set of a class which needs to be imported first, and then an object is created for that class which would use the methods or the functions associated with that particular class. Thus this entire process is a concept of Object Oriented Programming. Even, to understand the process behind the algorithms, one needs to be familiar with programming
There is an ongoing debate about which is the best programming language for Data Science. It never harms to master all the three languages but one needs to be expert in a particular language, and understand its various functionalities in different situations.
The choice of language depends on interest, and how comfortable the person is to program in that language. Python is generally considered as the Holy Grail due to its simplicity, flexibility, and the huge community which makes it easier to find solutions to all sorts of problems faced during the building stage. However, R is not far behind either as people from different backgrounds other than IT, seems to prefer R, as their go-to language for Data Science.
R is an open-source programming language which is supported by the R Foundation and is used in statistical computing, and graphics. Like Python, it is easy to install and is better than SAS which however is high-level, and easy to learn designed additionally for Data Manipulation.
The graphical representations and the statistical computations of the data gives R an edge over Python in this regard. Additionally, the programming environment of R has input, and output facilities, and several user-defined recursive functions. In the early ’90s, R was first developed, and since then its interface has been improved with constant efforts. R has made an outstanding journey from being a text editor to R studio, and now to the Jupyter Notebooks which has intrigued all the Data Scientist across the world.
Below are some of the key reasons why R is important in Data Science.
There are several companies who have used R in their applications. For example, the monitoring of user experience in Twitter is done in R. Also, in Microsoft, professionals use R on sales, marketing, Azure data. To forecast elections, and improve traditional reporting, the New York Times uses R language. In fact, R is used by Facebook as well for analyzing its 500TB of data. Companies like Nordstrom ensures customer delight by using R to deliver data-driven products.
Data Science is the sexiest job of the 21st century, and it would remain so for years to come. The exponential increase in the generation of data would only allow more development in the Data Science field, and there could be a gap in supply-demand at a certain age.
As several professionals are trying to enter this field, it is necessary that they first learn to programme, and R is an ideal language to start off their programming journey.
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