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How to Choose Data Analytics Specialization: Python, R, SAS, Excel or SQL?

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With 2.5 Quintillion Bytes of data created every day, it is undoubtedly true that data science is just going to grow big!

“There will be a need for 181,000 people with deep analytical skills in the US alone by 2018. (International Data Corporation)”

Data science is an emerging and growing field where tools & technologies keep on evolving. So, when you will look around, research about the data analytics tools and programs available, you will be confused as to what to choose. And why not? There is a plethora of choices created by education providers.

Undoubtedly, the thought of becoming a data scientist will trigger a lot of questions and confusion. One of the most commonly expected questions is “If I am going to become a data scientist, which programming language/tool should I choose out of R, Python, SAS, SQL, and Excel?

Data Analytics Specialization: Which Skill to Choose?

Since data science and analysis requires efforts and practice in mastering it, therefore, a careful analysis of which skill you should acquire is an important one.

This detailed analysis will help you make the best choice for you without any bias.

data-analyst-specialist-skills

So let’s get started…

R

  • If you are an experienced and trained data scientist, you will be asked how well you know R.
  • The development and usage of R is on rise since 2007, challenging almost 40 years of monopoly by SAS language. Being an open source and free to use tool, most of the data scientist are now leveraging R.
  • It has a huge number of statistical, graphical, and analytical packages (almost>12000). This makes it important for you to learn the basics of R to be a successful data scientist.
  • However, learning R is not enough to be a data scientist. This is because many Big Data production systems use Python and with R alone, you can’t survive with the data!

Note: R has a limitation that if you use it without parallel processing you can only process data equal to the RAM you have. That means if you have 4 GB RAM on your laptop, you can not process more than 4gb data unless you use special paid packages, programming techniques or parallel processing.

Data Analytics Course by Digital Vidya

Python

  • Python is a general purpose programming languages with many key advantages over R. For example, readability of code, suitability in production environments, and more.
  • This is another free and open source alternative available for you.  
  • When compared with R, Python lacks many statistical packages. Although there are some limitations, Python still provides you with pandas data frame package, scikit-learn machine learning, and statsmodels package. This ensures a suitable array of options for data scientists for conducting a descriptive and predictive analysis.
  • Its usage in analytics is rising tremendously, therefore, knowing how to code in Python will fireproof your career and cover any risks from knowing R alone.

SAS

  • The most important language in data science is SAS. This is a statistical language dominating the data world today.
  • It is easy to learn when compared to R and Python.
  • Being the godfather of analytics, it has been in businesses since 1976 contributing and financing statistical computing to a great extent.  It also has great documentation and customer support meaning enterprises using SAS rarely switch, while it is newer companies that begin with R and Python.
  • SAS is one of the hottest job interview qualifying skills right now. This means if you have SAS language certification in your resume, the probability of you getting hired is high. (SAS celebrated 40th year of record revenue – US$3.16 billion in 2015)
  • Learning SAS could be expensive and it is used mostly by big enterprises, so having at least one open source language (Python or R) other than SAS is crucial. Using SAS University Edition, you as a student get SAS language for learning for free just like Python and R.

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SQL

  • Acronym for Structured Query Language, it is one of the easiest data analysis languages to learn.
  • SQL is also used from within SAS (Proc SQL), R (sqaldf package), and Python (pandasql and dataframe. query).
  • With SQL, you can select data from tables meeting conditions.
  • Since it is easy to use operators on it and do group by analysis, this programming skill is very popular among data scientists.

Excel

  • Knowing how to use Excel to analyze data is on rise.
  • You can analyze data (1,048,576 rows by 16,384) interactively with functions, graphs, and limited statistics.
  • So if you are going to apply in a company where presenting data or something with financial domain, Excel is a much-needed skill.

You will be catered to the basics of SQL and Excel besides complete comprehension of SAS, R, and Python in our Data Analytics Course.

Final Take On Which Data Analytics Specialization You Should Choose

  • Since SQL and Excel are basic skills, you cannot afford not to know that.
  • Then Python, R or SAS is an option for you according to your needs.

data-analytics-skills-for-specialist

Ideally, you should be able to crunch data in SAS, graph it in R, and build a machine learning model in Python. Choose the tool based on the data, the project, and your budget rather than the advertising of some course.

Conclusion

It is important to remember that it takes a learning curve and time to memorize the basic syntax of any programming language for data science, and you can only learn a couple of things at a time.

I know, you are willing to work hard on a programming language (given the fact it helps you in your career growth and solving real-time problems). With the detailed analysis above, you will be able to choose the best one that fits your needs and requirements.

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  • There is 1 comment


    • 4 months ago

      Shrinivas   /   Reply

      Thanks for publishing the useful post.

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