Big Data Analytics is defined as analyzing huge and varied blocks of information for the purposes of finding trends, patterns, correlations and customer preferences. The amount of data can vary in size from terabytes to zettabytes. The term “Big Data” was first coined in 2005 by O’Reilly Media, although the use of data to uncover valuable information has been an age-old practice. It is just the amount of data being generated today and the skills needed to analyze such vast information that have evolved.
People in Mesopotamia used to keep records of the growth of livestock and crops. Then, in 1663, in London, data on mortality rates was examined and recorded by John Graunt. In the 1950s, basic analytics was being used by businesses to uncover trends and insights.
The Need for Big Data Analytics
Big Data Analytics can provide value to a company in the following ways:
- It can provide real time monitoring and forecasting of data that can have an impact on the operations and performance of a business.
- It provides the ability to locate, extract, analyze, blend and change data with various tools.
- It helps in identifying vital information, which can improve the quality as well as the speed of decision making.
- The development of new products becomes much easier with the analysis of big data. It enables businesses to determine the needs and satisfaction levels of customers.
- The analysis of large and complex data also gives companies information regarding new revenue opportunities and how to improve customer satisfaction.
What is Required to Handle Big Data?
There are a number of skills that a big data analyst must have, which you can acquire through the best big data analytics course. Some of these skills include:
1. Programming Skills
Knowing how to program using multiple languages is essential for becoming a Big Data analyst. Some of the important languages here include Java, R, Python, MATLAB and C++. While these are some of the essential languages, the more you know, the better it is for your career.
2. Quantitative Skills
The knowledge of statistics as well as general mathematics plays a vital role in being good at analysis. Having a good understanding of quantitative data analysis techniques, such as inferential statistics and descriptive statistics, enable analysts in generalizing data, summarizing results, make predictions and find patterns.
3. Computational Frameworks
It is important to have knowledge of and familiarity with frameworks such as Hadoop, Apache Storm, Apache Flink, Apache Samza, Apache Spark, and the classic MapReduce. These are very effective analytics tools.
4. Data Visualization
With the help of analysis, large quantities of unstructured and structured data can be presented visually. This helps in presenting the data in a more understandable and simple manner. There are various tools available for this, which big data analysts must know, such as Plotly, Tableau, and Data Wrapper.
Apart from these skills, having knowledge about business, problem solving and data interpretation also prove to be useful for an analyst. However, make sure you choose the right course for big data analyst certification.