Becoming a Analytics Professional is nothing unlike what Holmes would do. His skills are the very skills that are used in data analysis; after all, what were clues to Holmes are data to the modern data analyst.

But before delving into the skills, let’s have a quick look at the difference between data science and big data.

Big data represents the application of the tools borrowed from data science to large datasets.

Given that working with data sets and data science tools would constitute a key element in data analysis, professionals with a great IT background are preferred. They are skilled at handling information and programming. Of course, anyone can become a big data analyst. The skills needed for the same include the following:

 

 

  1. Skillset I: Programming

Coding is a core skill in big data analysis.

A big data analyst will have to conduct multiple statistical and numerical analysis with data sets. The most commonly used programming languages include C++, Java, R, and Python among others. But the more you know, the better it is.

Additionally, prior experience with these languages and programming is also important. This will helps in improving your efforts at analysis.

Understanding the databases is essential; analysts interact with the databases through statements and queries. Tools such as HIVE, SQL, R, and Scala are commonly used and you should be comfortable with them.

  1. Skillset II: Quantitative Skills

Data analysis does not end with programming.

It requires excellent quantitative skills to for numerical analysis. Some important concepts include linear and matrix algebra, statistics, probability, and multivariable calculus.

As core skills required in big data analysis, these quantitative concepts enable other important concepts associated with big data including machine learning and neural networks.

  1. Skillset III: Multiple Technologies

In order for a successful career as a big data analyst, it is essential that you are open to learning multiple technologies.

We are not talking about programming alone. The extent of technologies available today is quite huge. They include the software, hardware, tools and platforms. Some basic tools include Microsoft Excel, R, and SQL while at the level of the enterprise, you would be needing MATLAB, Cognos, SPSS, and SAS. It is equally important to learn Hadoop, Linux, Scala, Python, and HIVE.

Of course, the technologies that you would be expected to use would depend on your organization and the environment. But, these are some of the basic skills we are talking about.

When you keep your learning profile versatile, your work profile will be versatile as well.

  1. Skillset IV: Business and its Outcomes

The data that you will be analysing should be helpful for the business. Therefore, it is required of an analyst to have a good understanding of the industry, the business and the domain in which they are operating.

Understanding the domain and the industry is essential to be able to get the maximum results and impact from the big data.

Big data analysis can help find relevant opportunities and challenges that the business may experience in the offing. A simple example is the iPad. When introduced, the digital publishing industry could not see the potential of this simple device. It would take them a while to see the scale of transformation that it could bring eventually transforming the digital publishing industry forever.

But the understanding not only to take the business forward. A big data analyst is also responsible for communicating the big data to the stakeholders involved. Big data analysts make complicated trends emerging from the data into simple conversations helping departments and businesses to make their decisions faster.

  1. Skillset V: Data Interpretation

Data interpretation skills are more of a deviant in the list.

These skills are a unique combination of art and science i.e. they require the logical and precise mathematics and the ability to be creative, innovate, and remain curious.

Most employees do not understand their companies’ own data and worse still, they do not rely on the holistic interpretation of the data. This way of doing things is both dangerous and self-defeating.

Instead, when big data analysts are allowed to interpret the data, they call upon the employees to explore the data that is available around them and question it. Eventually, this leads to a learning culture within the organization while also changing the course of the business.

In conclusion, becoming a big data analyst requires the skills mentioned above and the uniqueness that they bring to the work.