Key Skill Sets for the Next Gen Big Data Analysts

Dream. Dare. Do – that is Suyati’s work principle in a nutshell.

Apr
05
2016
  • Author:
  • Uma Chellappa

key skill sets of big data analysts

The Big Data wave is not diminishing anytime soon. However, the challenge is not about gathering data anymore. It lies in converting the volumes to useful information. Industries are quickly realizing that data has no meaning if the right algorithms are not applied. Even as organizations get hit by the huge volumes of data every single moment, they grapple with the inability to turn all of these into meaningful insights and gain therefrom.

The need of the hour is efficient data analysts who are able to separate the wheat from the chaff. It would be helpful to understand what skill sets are required for a person to be a competent data analyst.

What does an organization look for in a data analyst?

A data analyst needs to primarily have a passion for mathematics and statistics, but it is equally important that the person has an innate ability to dwell deeper on a problem and think beyond the obvious. So, while technical skills are an obvious necessity, strong soft skills cannot be undermined in an aspiring data analyst.

Technical skills

Skills relevant to data mining and data structure

A data analyst needs to have a good understanding of the data structure to be able to mine the data and construct a data infrastructure that will help to develop the analytical programs. Setting up databases requires the knowledge of programming languages. An analyst is expected to know certain querying languages and has a thorough knowledge of data tools.

  • Hadoop: Proficiency in Hadoop components like the Hive, Pig, YARN, MapReduce will ensure that you’re always in demand in the Big Data job market. This is because these tools have built-in real-time analytics that can be applied to large-scale data. Critical analysis becomes simpler and faster with the help of these.
  • NoSQL: Familiarity with NoSQL databases over SQL databases is recommended. An agile and scalable industry needs databases like MongoDB that score over the SQL databases. The number crunches that take place in Hadoop are often sourced from NoSQL databases and are also used as the destination points after insights have been gleaned.

Statistics and knowledge of algorithms

Analytics can be safely equated to statistics and algorithms. An analyst needs to master core concepts like mathematics, logic, modeling, and designing complex algorithms. Algorithms are the future of digital business. It essentially deals with manipulating the data, setting up your own models based on the logic derived from data and forecasting behavior. Rules of probability and calculus play a significant role. Hence, a degree in mathematics, statistics or quantitative reasoning makes for a strong base. Command over tools like Matlab and R will propel your worth many times over.

Machine learning and data visualization

Machine learning involves establishing predictable patterns from large sets of data. As and when newer data comes in, a new algorithm is set to update the old and apply actions. Comprehending large sets of data can be overwhelming and sometimes you need visualization tools like Raw, Dygraphs or Tableau to simply portray a part of data visually as graphs, patterns or trends to give you a better insight about the entire data.

Programming languages

Cracking Big Data is never a single stage activity. While it might seem that reasoning, analytics, and statistics have less to do with the core programming skills, the disciplined art of writing complex codes to arrive at solutions for data-driven problems forms the basis of complex number crunching. According to this source, computer programmers are seeing a 337% increase in programming jobs requiring an analytics background. Knowledge of languages like Python, Ruby, Java, CSS, PHP and also the knowledge of using built-in structures and applied concepts will help you score over the rest of the candidates.

Non-technical or soft skills 

An analyst is never a purely technical person. He has to have the right mix of technical knowledge and certain essential soft skills like being able to think creatively, having a curious bend of mind, asking the right questions, and the ability to think out-of-the-box.

Furthermore, it is imperative to understand the ethical impact of big data on the society at large; the right to privacy, security laws and their relevance to doing business is a part of the larger picture.

A job as a big data analyst can be stressful and one needs to be able to think and communicate effectively while translating the numbers into business goals. A person with good communication skills and someone who is undeterred by long working hours stands a better chance in the industry.

As we can see, the skill-sets are varied and it might be unlikely to find all of it in one person. Teradata, a data warehousing company, opines that a practical approach will be to build a team that combines all the necessary skill-sets.

Are you an aspiring big data analyst or an organization seeking to hire efficient analysts? Let us know if this article helped you.

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