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THREE R LIBRARIES EVERY DATA SCIENTIST MUST KNOW

Data science is still an emerging field and thus has a high-demand and lucrative job market. But for anyone to break into the data science industry, getting started can be daunting. Some people go back to college, some teach themselves and some enroll in data science certifications. Regardless of which path you pick; data science involves high coding expertise. Just as in many other technical fields, skill demand and expectations are always evolving. In the upcoming year 2022, there are many leading and competing data science programming languages that an aspirant must equip themselves with to scale up their chances of landing a great job in the field. 

Programming languages, to put their simple words, are the languages used to write lines of code that make up a software program. The foremost thing to consider while choosing a programming language is the goal that you’re trying to accomplish as different tasks require different levels of knowledge & specific languages. You also need to gauge your data science skills for the programming language(s) that you are capable of acquiring ahead in the next level. There’s an array of programming languages that give each other a tough fight to lead up the ladder to becoming the best programming languages in the data science field. As an aspirant, you must have come across a tough call to take between R and Python as both of these languages fight for the top slot. R is a programming language for statistical computing and graphics supported by the R core team and the R foundation for statistical computing. R is used among data miners and statisticians for data analysis and developing statistical software. Users have created packages to augment the functions of the R language. 

R and Python are state of the art in terms of programming language oriented towards data science. Learning both of them is definitely the ideal solution. Python is a general-purpose language with a readable syntax, whereas R is built by statisticians and encompasses their specific language. R and Python require a time investment and such luxury is not available for everyone. Developed two decades ago, it has one of the richest ecosystems to perform data analysis. There’re around 12000 packages available in CRAN (open-source repository). The cutting-edge difference between R and other statistical products is the output. R has fantastic tools to communicate the results. R studio comes with the library Knitr. It handles tasks like data wrangling, engineering, feature selection web scrapping, app and so on. These incredible features put R at a pedestal higher than Python, when it comes to selecting the best language for escalating programming career.  

Python vs R is a never-ending quest, but to start with R is a software environment and statistical programming language built for statistical computing and data visualization. R’s numerous abilities tend to fall into three broad categories:

  • Manipulating data
  • Statistical analysis
  • Visualizing data

Whereas Python has three popular applications that include:

  • Data science & data analysis
  • Web application development
  • Automation/ scripting

As it is said that once you’ve learned one programming language, it typically gets easier to learn another one. R tends to have a steeper learning curve at the beginning and once you understand its features, it gets significantly easier. 

When focussing on statistics, enlisted below are the reasons why you should invest in R for data scientist roles:

  • R IS BUILT FOR STATISTICS

It remains the programming choice for most statisticians today as R’s syntax is easier to create complex statistical models with just a few lines of code.

  • IT’S A POPULAR DATA SCIENCE LANGUAGE AT TOP TECH FIRMS

Not just the tech firms, R is in use at analysis and consulting firms, banks & other financial & academic institutions & research labs for data analysis and visualization.

  • LEARNING DATA SCIENCE BASICS IS ARGUABLY EASIER IN R

Specifically designed with keeping data manipulation and analysis in mind, core data science skills are a cakewalk with R.

  • AMAZING PACKAGES THAT MAKE YOUR LIFE EASIER

It has a fantastic ecosystem of packages and other resources that are great for data science eg: the Dplyr package makes data manipulation a breeze and Ggplot2 is a great data visualization tool.

  • INCLUSIVE & GROWING COMMUNITY OF DATA SCIENTISTS & STATISTICIANS

It’s easier to find answers to questions and community guidance as you work your way up through the projects in R. 

  • INCREDIBLE TOOL IN YOUR TOOLKIT

It automatically makes you a more flexible and marketable employee when you’re looking for jobs in data science & makes many projects easier. Being able to look at R and translate it into Python means that the amazing resources of both languages are open to you.

Without further ado, here are the three R packages (highly recommended) that you take the time to learn and equip in your arsenal of tools as they’re insanely powerful for every data scientist:

  • CAUSAL IMPACT WITH GOOGLE

It attempts to predict what would have happened if forsake the tv campaign never occurred, that is called the counterfactual. It basically attempts to predict the counterfactual & compares the actual values to the counterfactual to estimate the data. It’s super useful for marketing initiatives, expanding to new regions, testing new product features & much more.

  • ROBYN WITH FACEBOOK

Marketing mix modeling is a modern technique used to estimate the impact of several marketing channels or campaigns on a target variable like conversions & sales. Not only you can assess the effectiveness of each marketing channel with Robyn, you can also optimize your marketing budget with it.

  • ANOMALY DETECTION WITH TWITTER

Also known as Outlier analysis, it is a method that identifies data points that differ significantly from the rest of the data. A subset of general anomaly detection is anomaly detection in time-series data, which is a unique problem as you have to consider the trend & seasonality of the data as well. It is an intricate algorithm that can easily identify global and local anomalies.

Having talked about the three major R libraries that every data scientist needs to arm in order to become the best in the league, credible data science certifications from MIT, Stanford, or USDSI™ also plays an elemental role in leading the way ahead towards future growth. Being a versatile developer or a data science professional and knowing multiple programming languages means your skills will never be outdated and you can quickly adapt to industry trends & use your vast software knowledge & web development skills to keep your job opportunities varied and fresh. 

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