If you are reading this article then I would guess you are still having second thoughts about the big data analytics courses. You do not know what to learn and where to start. Or maybe you are not a complete newbie. You have learned your statistics and have good reason to believe you can make it big in the field of analytics, and now you just need a gentle push towards the right course. This is for you. It is a short list; it leaves out much more than it includes. But that is the point – pin-pointing you in the right direction.
This tool tops an astounding number of indexes in this segment. However, it does not come as a surprise to me. Tableau deserves the accolades.
Data analysts lose half their credibility without a convincing and compelling display of their findings. This is where Tableau comes in. It is a ready-made repertoire of innumerable interesting charts, tables, and functions, accumulated to assist the analysts in data visualization.
The quality of the visualization often determines the sincerity with which the analysis is accepted. The interactive dashboards and interesting graphical display offered by Tableau make it possible for analysts to reach their goal of becoming story-tellers through presentations.
Yes, is that not obvious? Python has been soaring on the popularity index. It is an object-oriented, interpreted, general-purpose programming language. However, what leads to its overwhelming popularity is how well suited it is for analytical usage.
Python has a smooth learning curve and an easily comprehensible syntax which should make it possible for the analysts who come from outside a STEM background to catch up quickly. Apart from that the libraries like NumPy, SciPy, Pandas, Matplotlib, and Scikit-learn, make creating statistical models and programming machine learning algorithms easier.
Since it is an open-source language, it stays pretty much up to date, and there is a whole community of users across the globe constantly updating information about its applications. So, there is usually helpful when you get stuck.
This is one of the most popular undertakings of Apache. A spark is a tool used for in-memory, clustered data processing. The data is processed almost in real-time. The speed and accuracy offered by Spark have put it on the map.
Spark has high-level APIs for a bunch of important languages. It has an interface programming which is highly error-tolerant. It also allows you to cleanse and transform data.
Spark has adapted to the needs of the industry and cemented its place in the industry as an ever-relevant tool.
These are tools that appear the most in data analytics job searches. These are also the tools that will get you hired as a fresh candidate.