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.
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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!

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).

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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).

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.

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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! 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.

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

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.

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.

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

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!

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