Top 5 Must-Have Skills to Become a Big Data Specialist

Big data is one of the latest and in-demand technical skills today as companies increasingly produce a massive amount of data every day. And this data is still stimulating to generate large velocity, volume, and variety. Python, C++, and Java, Machine Learning and AI experience, quantitative analysis skills, data mining, and SQL/NoSQL databases and algorithm development, are most in-demand technical skills across the tech arena.

Big data has the ability to draw germane information from the enormous volume of data – both structured and unstructured – being processed every minute.

Let's look at the top 5 must-have skills needed for being a big data specialist.

Business Knowledge

A big data scientist has a good knowledge of the domain where his/her company is working on, to keep the analysis focused, to authenticate, sort, relate, assess the data effectively. Analytics, Machine Learning, IoT, RPA (Robotic Process Automation), and AI are disruptive technologies to businesses. These technologies could have an impact on established business processes that have to be redesigned, which require users must be retrained.

Knowledge of Quantitative Aptitude and Statistics

Being a big data acumen, an aspiring candidate must have knowledge of Statistics and linear algebra. Having understood the pulse of the business, statistics is one must-have skill for a data scientist. It is a basic building block of data science and knowledge of core concepts, such as summary statistics, random variables, probability distribution, Hypothesis testing framework, are essential. Besides this, the knowledge of all tools and technologies in the big data field, the ability of problem-solving and creativity can also assist a beginner to perform their tasks well.

Maintain Data Quality and Adhere to Compliance and Governance

Poor data quality is one of the major reasons behind most of the big data project failures. However, several IT and tech businesses are aware of it, but they also know cleaning up the data is a tedious task that gets in the way of other projects. So, big data champions always insist on quality data. They also needed to pencil in project time for compliance and governance conformance and QA checkout. It is also significant for big data professionals to be familiar with a wide array of tools and technologies that the industry uses.

Understanding of Computational Framework and Programming

A good understanding and familiarity with computational frameworks and knowledge of Object-Oriented Languages and the fundamental knowledge of data structures and algorithms can be helpful and go a long way. Framework knowledge such as Apache Spark, Apache Storm, Apache Samza, Apache Flink and the classic MapReduce and Hadoop, help in big data processing that can be streamed to a great extent. The background in mathematics and to be familiar with sorting algorithms, data types, etc, will also help greatly.

Follow-Up on Big Data Projects

To gain experience with big data projects, following up on implemented big data projects is one of the best ways. This will enable a data science professional to perceive what is going well and what can be improved. So, he/she can apply this knowledge in future projects. Furthermore, this process of following up on projects after implementation shows customers that the company cares about their systems and work environment, which will open the path for great user cooperation and collaboration in the next project together.