R, Python or SAS – Which is the Best Tool for Data Science Learning

We Love Comparisons!

Sometimes comparison provides us the right path for what to choose and from where to start. If talking about the career in the technical world, people always compare two to three technologies to select the one. DataFlair always tries to provide you a perfect career guide for starting your career. Today, I bought a new comparison of R, Python, and SAS for Data Science. By the end of the article, you will find which tool should be learned first for learning Data Science.  So, let’s start the comparison of R vs Python vs SAS.

Before moving on I highly recommend you to check the purpose of Data Science.

R, Python or SAS

Comparison of R, Python, and SAS

Here is a brief overview of the top data science tool i.e. R, Python, and SAS. This comparison will give you the best advice for beginning your career in data science.

1. R for Data Science

R is a popular programming language that is used for statistical modeling. It is useful for performing analysis on large scale data and visualizing information. R is a must know the language for a data scientist, as it contains the core statistical packages. R can also offer a steep learning curve to the beginners who are newbies in data science. The availability of mass packages and its open-source support has made it a popular choice for data science, analytics and data mining.

There is a multitude of packages in R, which provide extensive support for various statistical undertakings, ranging from biostatistics to astrophysics. Some of the popular packages of R are-

  • Dplyr

For data wrangling and management, dplyr is an ideal tool. Dplyr is an easy to use package that uses a declarative syntax to carry out its operations in wrangling data. With dplyr, you can select, modify, filter, mutate and perform several other operations.

  • Tidyr

Tidyr is an important data science tool for cleaning your data. It possesses two properties –

  1. Every column is treated as a variable.
  2. Every row is an observation

Using Tidyr, we can use three main tools – gather(), spread(), separate() to organize data into rows and columns.

  • Ggplot2

Ggplot2 is an interactive visualization library that allows you to make aesthetic plots that are also interactive.

Python for Data Science

Python is the most popular choice for programming language not just by data scientists, but also by software developers. It is a versatile language that is supported by a large number of libraries that allow you to work on several fields like data-wrangling, data filtering, data transformation, predictive analytics, machine learning, etc. It also allows you to develop your own web applications through which you can host interactive graphs for the users. Python also provides an interface for various types of databases.

Some of the important libraries of Python are –

  • Numpy

Numpy is a python library that is mostly used for scientific computing. It consists of powerful features and can perform computationally heavy tasks like linear algebra. With NumPy, you can perform operations on multi-dimensional matrices, quickly and efficiently.

  • Matplotlib

Matplotlib is another essential plotting library that gives you a wide range of aesthetic graphs. With matplotlib, you can perform image plots, contour plots, scatter plots, line plot etc.

  • Pandas

Pandas is an important library in Python using which you can manipulate data and implement various functions like filtering, sorting, merging, joining, pivoting and reshaping the data. It provides you with an important data structure called dataframe that allows you to organize data efficiently.

  • Tensorflow

TensorFlow is an advanced machine learning library that was developed by Google. Using TensorFlow, you can implement powerful neural networks, perform complex mathematical operations and make use of the lightning fast GPU processing. With further advancements in TPU, its processing speed has highly increased.

Do you want to explore more? Check how important is Python for Data Scientist

SAS for Data Science

SAS stands for Statistical Analytical System. It is a tool developed for advanced analytics and complex statistical operations. It is used by large scale organizations and professionals due to its high reliability. SAS performs statistical modeling through base SAS which is the main programming language that runs the SAS environment. It is a closed-source proprietary tool that offers a wide variety of statistical capabilities to perform complex modeling.

However, SAS is not a tool that is suited for beginners and independent data science enthusiasts. This is because SAS is tailored to meet industrial demands. It is expensive software that only large scale corporations can afford. However, SAS offers support and is known for its stability and efficiency. Due to this reason, despite the presence of alternative open-source tools, SAS is preferred over the others.

Check how SAS is used by Data Scientists

R, Python or SAS – What should You choose for Data Science

For aspiring Data Scientists, the plethora of tools can make it difficult for you to make the right choice. We discussed three most popular tools – R, Python, and SAS. However, what is the right tool for you as a beginner in Data Science? In this section, we will address this query and give you the right answer based on your needs and expectations. If you don’t want to read a detailed answer to this question, I have provided a short answer at the end of this article.

1. Choosing the right learning curve

While R will take some time for you to gain proficiency, Python provides an easier learning curve that is best suited for beginners who are not only newbies in data science but also in programming. Since Python is a versatile language, you can use it for developing web-applications as well. When it comes to SAS, it is an environment for programming that is designed for statisticians with little focus on complicated syntax. This means that it is easy to learn SAS. However, it is easy in order to implement complex statistical thinking efficiently and with ease.

2. Cost and expenses of the tools

Both Python and R are open-source. Anyone can use them without any need to purchase licenses. However, with SAS it is an entirely different story. SAS is a closed-source proprietary tool that is highly expensive. The costs of it are so high such that only big companies can afford to purchase this tool. Also, many more attributes and features of SAS can be unlocked through payment of expensive upgrades. Therefore, if you are a newbie in Data Science, learning SAS may not be an ideal choice from the cost perspective.

You must check the guide specially designed for Data Science Beginners by DataFlair

3. Libraries and Support tools

Both Python and R enjoy a wide range of packages. Python is famous for its wide variety of packages on machine learning. It also provides versatile packages of web-development, GUI programming and much more. R is limited only to packages of statistical modeling. However, the visualization packages of R such as ggplot2, Lattice, RGIS are much more diverse and visually aesthetic. SAS on the other hand, provides a wide variety of Business Intelligence, Statistical, and analytics tools. However, it still lags behind in more advanced tools of machine learning and data visualization.

4. What do Industries require?

Industries have long trusted SAS as their primary tool for data analytics and business intelligence. This is due to high reliance, sophistication and stability that SAS provides to its clients. However, gradually, the trend is shifting to Python, R and other open-source libraries that provide far more powerful features than SAS. While SAS may be ideal for large scale industries who have not adapted open-source as their primary tool, it is still not flexible as other free alternatives. Prior to 2015, SAS used to dominate the data science industry, however, by 2017, it became a minority to Python and R.

5. Tool for the right need

In the end, the choice of learning Python, R and SAS depend on their usage and where you need to apply them. For beginners who want to learn a programming language while enjoying a wide variety of libraries, Python is an ideal language. For seasoned statisticians, R is an ideal language. Both of these languages provide extensive open-source support and you can customize their packages on your own. For data scientists, seeking careers in the field of natural language processing, visual computing, and big data, Python and R are the ideal programming languages. However, for statisticians seeking employment in companies that specialize in business intelligence, SAS is the right choice.

The best ever online training to start your Python learning by yourself

Short Answer – R, Python or SAS?

In short that Python is more suited for beginners who want to have an in-depth knowledge of data science. R is best suited for beginners in data science who have experience in statistics, as R will also introduce them to several aspects of a programming language. Nevertheless, R is a must have tool for aspiring data scientists even if you start with Python. SAS is customized for business requirements and is used heavily by large scale companies. This makes SAS a specific language for business intelligence needs. Also, the high costs make it an unaffordable tool for many. Therefore, we conclude Python and R to be the best tools for aspiring data scientists.

Got your answer? or Still in doubt? You can freely share your feedback and ask your query through the comment section. We would love to hear from you. See you in our next tutorial – How Data Science is used in different companies

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