How to be a superstar Big Data analyst

Big Data is one of the hottest new IT trends with numerous opportunities. Organizations are keen on hiring Big Data engineers, analysts, and professionals to take advantage of the booming market. The different tools and technologies surrounding Big Data have generated enormous buzz amongst various sectors and promise aspirants a lucrative career. The question is, how does one go about acquiring the proper skills to grab the role of a Big Data analyst? Check out some important factors below:

Brush up on your programming


If you’re serious about cementing a career in Big Data, you need to learn how to code. Without this skill, you won’t be able to make it very far as analysts are required to conduct statistical and numerical analysis with massive sets of data. A few of the languages worth investing money and time in are Java, Python, C++, R. While it is not necessary to know all the programming languages out there, you benefit from understanding as much as you can about each.

The thing is, if you’re well-versed in a specific language, you should not have trouble picking up the rest over time. Ideally, you should start learning Python and then gradually move onto the others. What’s more, hands-on experience with programming languages aids you in the learning process. Also, you need to get into the mindset of a programmer and make informed decisions if you want to be recognized as a professional Big Data analyst.

Your preferred position as a Big Data analyst would also require you to interact with databases using statements and queries. So, you should be comfortable working with instructional languages, Big Data tools, and databases. Tools like HIVE, Scala, and SQL are something you should know well.

Work on your quantitative skills

When you start working as a Big Data analyst, you’ll notice that most of your tasks involve programming. If you feel overwhelmed by it all, it’s best to use your quantitative skills to answer the question. First, you must grow your knowledge of linear and matrix algebra and multivariable calculus. Having an understanding of statistics and probability won’t hurt either. Building a strong foundation in numerical analysis is necessary for every decent Big Data analyst. Why? Because they come in handy when working on complex concepts like machine learning and neural networks.

Start using multiple technologies

Programming is a key analytic skill but it is also quite versatile. You need to learn different technologies to move ahead in your career. However, keep in mind that technologies are not always limited to just programming. There are a wide array of technologies on offer, and you must understand as many of them as you possibly can. This spans different platforms, software, hardware, and tools, including the basic ones like Microsoft Excel. If you take a look at the enterprise level, MATLAB, SAS, Cognos, and SPSS are just as important to pick up as Scala, HIVE, Linux, Hadoop, and Python.

Depending on the environment you work in, you will have to use the correct technologies. The requirements of your project and company also dictate the technologies needed for the role. So, being versatile is instrumental to your long-term success.

Understanding the business and outcomes

You would never succeed in analyzing data or providing insights correctly if you failed to apply them to a business setting. Every Big Data analyst is required to have a thorough understanding of the domain and business they operate in. This kind of domain expertise magnifies the impact of the insights offered by the Big Data analyst.

By building up their skill level, Big Data analysts can help identify proper opportunities and problems based on their expertise of business and the IT industry at large. The more domain expertise a Big Data analyst has, the more effectively they are able to communicate with various stakeholders. If you have an idea that you think would benefit the company in the long run, having a grasp of the domain makes these conversations flow a lot more smoothly and carries added weight.

Interpret data correctly

Big Data Analyst

We have outlined a number of skills above, but the outlier is definitely the interpretation of data. This is the single most important skill that is used to bridge the gap between science and art. If you’re planning to become the next big name in Big Data analysis, you must be able to combine the sterility and precision of mathematics and hard science with artistic curiosity, creativity, and ingenuity.

Most of the companies have a large number of employees who fail to understand the data generated by their own firm. In fact, many of them don’t even have a clear grasp of where all of their data is. They mostly rely on preconfigured dashboards and reports to get an idea about the insights. But this approach has a lot of problems. Not only does it fail to offer a holistic view of the analysis and data procurement systems, it even resolves the problem of fragmented data systems. When the organization starts to grow organically, various silos merge, leading to a confusing and jumbled mess.

But a Big Data analyst possesses the knowledge and expertise to ask the right kind of questions and explore the raw data in a systematic, uncomplicated manner. This leads to discoveries that can alter the course of business for the organization and steer them in the right direction by helping them make informed decisions.

To be a real superstar Big Data analyst, don’t forget the soft skills

A Big Data analyst is a hot commodity in the IT sector right now, and so, if you’ve made up your mind about pursuing this profession seriously, you need to master all the five essential skills that have been discussed above. People who are already a part of the IT domain tend to have an advantage when it comes to learning new programming technologies and languages. However, it’s not impossible; all they need to do is put in some more effort into learning computing technologies and skills. And don’t forget this: The inclusion of softer skills like domain expertise and business experience often level the playing field.

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