Analytics has many applications in business, from identifying underperforming production units to zeroing in on your best customers.  Analytics is the art of processing data so as to derive transformative insights from it–insights that can help you understand your organisation better, and help you make informed and timely decisions. If you are looking to step into the analytics sector, you need to build expertise on a few skills, namely knowledge of statistical concepts and their business application, and also a knowledge of the analytics methodology.

A key skill that every data scientist needs is a working knowledge of specialized analytics tools that can perform statistical analysis on large amounts of data. This is where programmers have an advantage over others when it comes to learning analytics and Big Data.

Analytics tools can be broadly classified into two kinds:

  1. Graphic User Interface (GUI) based tools such as Excel, SAS E-miner, IBM Modeler (aka SPSS Clementine), etc.
  2. Programming based tools such as SAS, R, Python, etc.

GUI vs Programming
Most analysts and decision-makers would prefer to use a GUI to perform statistical analysis. GUI-based tools like Excel & SPSS are very user friendly.  However, they are not very good at handling large amounts of data. A few tools that have managed to combine user friendliness with large data handling capabilities (like SAS E-Miner & IBM Modeler) are extremely expensive.  Annual license fees for such tools could be several times the salary of the data scientist using the tools!

Programming tools win out!
It is for this reason that possibly the three most popular analytics tools in the industry today – SAS, R and Python – are all programming tools that involve writing code. Many powerful analytics programming tools are open-source and therefore easily accessible, like R and Python (SAS is a paid tool).

08The Programming Advantage
With their background in computing and a familiarity with programming languages, programmers find it easy to pick-up languages like SAS, R & Python. In order to grasp underlying patterns, data scientists have to be able to decipher what each incoherent mass of data is saying. It is this very skill of spotting underlying patterns that programmers have honed over their coding careers and this helps programmers-turned-data scientists recognize a problem and ask the right questions.

The logical approach to developing a statistical algorithm is identical to the one for developing a computing algorithm, giving programmers a strong footing when learning analytics & big data tools and techniques.

Programming in Big Data
With the ever-growing popularity of Big Data, programmers are becoming increasingly sought after in the world of analytics.  Currently, there are numerous big data tools and platforms that are popular in the industry; this makes it imperative to know how to use more than just one tool. To be able to edge out the competition, you have to become a multi-tool-kitted analyst.  If you have heard of Hadoop, PIG, Hive, Spark, Scala, Mahout, MongoDB and others, then you know what I am talking about.

Again, in the current scenario, programmers have a strong advantage in the field of Big Data. Their background allows them to pick-up different tools and technologies faster than non-programmers. No wonder Big Data is dominated by programmers and ex-IT professionals!

No programming?  No dice!
Programmers have it easy when transitioning into the fields of analytics and Big Data. While this field was dominated by statisticians and mathematicians 10 years ago, currently it’s the programmers who rule the roost.

In fact, many companies like Mu-Sigma and LatentView Analytics prefer to hire engineers for their data science roles. Cognizant and Infosys are doing mass re-skilling of their programmers – turning them into data scientists and Big Data analysts. Want to be a hotshot data scientist? You better pack the heat!

The Jigsaw Edge
At Jigsaw Academy, we have helped thousands of programmers and IT professionals make the switch to analytics and Big Data.  On an average, those who make the switch see a 25-35% increase in their pay checks, not to mention a more challenging and diverse career path than the one they’ve left behind.

Ashish Jain was a programmer who switched from IT to analytics and has seen his salary more than double in the last three-and-a-half years.  You can read his success story here.

The  Big Data Specialization offered by Jigsaw Academy is the most popular learning path among programmers.  This path covers essential topics in both analytics and Big Data.

The Data Science with R course teaches you analytics skills as well as R, the most widely used analytics tool in the industry.

The Big Data course teaches you the most popular Big Data tools and technologies.
Together, analytics and Big Data make an irresistible combination that will open numerous career doors for you. If there was ever a time to make the switch, it’s now!

You can learn more about the best learning path for programmers here.


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