We often mention big data analytics as if it were one small discipline with a bunch of tools and a handful of applications. But the truth is far from it. The humongous amount of data that the whole human race (and beyond) is creating through its connected devices and endless online activities is loosely termed as big data. Incidentally the whole milieu of tools that are used to make some sense out of all this data is also called big data. So, the terms and their applications are a little confusing, hence we will not waste too many words trying to explain them.
What you need to know is that companies around the world are using data to drive business. They use consumer data to conduct behavioral analysis which in turn helps them reach potential customers with a personalized and more convincing marketing strategy. Companies use trend analysis to modify their products and services, manage their warehouses, or optimize the logistics. This is how businesses are conducted, and this is also why big data analytics professionals are more in demand than ever. Without further ado let us quickly look at some tools which can get you started on the big data road.
Yes, the same MS excel which you completely ignored (well, I did) during the high school lessons. If big data institutes are offering specialized courses on Excel there must be a good reason; there are many of them actually.
- Ms Excel is an excellent tool for entry level analytics. (which usually works fine for both small business problems)
- The advanced features like pivot tables and various filters can offer a great deal of insights.
- A decent tool for data visualization.
Hadoop is not one tool but rather a software suit which had solutions to a number of big data problems. Big data analytics courses usually start with a brief overview of Hadoop and its different components. The key parts of Hadoop that you may learn during the course of your training are HDFS (for data storage), Mapreduce for data processing, Pig or Hive for query based data processing, Spark for in memory data processing.
Although Python is not among the basic requirements for starting a career in big data analytics, it is a good idea to learn it. You will eventually try your hand at coding your way through data to find patterns in data. And Python is arguably the best tool for the purpose.
It stands for structured query language. It is used to make specific queries from data bases. It is a key tool for database management, data mining, and data architecture. It helps you become self sufficient as an analyst.
Tableau is probably one of the most popular data visualization tools. It affords you a degree of data analysis too (though it is not for heavy analytics). It can import data from various sources and turns your analysis into a story that the stakeholders can understand. It is an absolute necessity.
This in no way is an exhaustive list but it does give you some direction, hopefully. May you have a pleasant journey.