Introduction

With the advent of Big Data into almost every industry, there is an increase in job roles in the IT sector related to Big Data. From data engineers to, data analysts to data scientists. While data engineer and data scientists are relatively new roles, that Big Data might have spawned, data analyst role ha been around for decades.

The Big data analyst role that Big Data has spawned hasn’t changed much except exposure to tools that are used in the Big Data space. So, if you are a data analyst already and want to make it big in the Big data industry, you have to pick up skills in the right tools to be able to exploit the big data boom. Big data analyst role sits somewhere between a big data engineer and a big data scientist, a case true in large enterprises.

In the small to medium scale enterprises, the role is taken over by either data engineers or data scientists. You could also have a situation where data analysts are performing functions that are expected of a data scientist, it depends on the business. Let’s talk about the Data analyst role in this article and explore the skills needed to become a successful data analyst.

  1. Big Data Analyst
  2. Skills
  3. Skillset summary
  4. Data Analyst Education Requirements
  5. Scope

1. Big Data Analyst

You might by now know what is Big Data and its use cases, so we will not step into that area. The results that Big Data can throw up needs someone who can make sense of it, find useful information, maybe clean it further, perform activities like data munging and wrangling and present findings to the management team or make it available to a data scientist team so that can use advanced statistical and mathematical tools for predictive modelling or machine learning and AI.

For the models to be accurate and the machine learning to be efficient, the data that it is based on should be of high quality. The data analyst ensures the data that reaches these advanced systems for deep learning and inferences are complete with high accuracy and quality.

Big Data analysts can be expected to conduct research, mine for data and present findings. Problem solving skills, versatility and the ability to think critically, challenging the status quo using creative reasoning skills make up the general skill sets of a data analyst on the non-technical side. On the technical side, a data analyst is expected to have deep quantitative, data mining skills, data interpretation skills, be proficient in programming in a couple of languages, possess strong presentation skills that include both oral and written communication and have working knowledge on the latest tools in the big data scene. Lets explore what is big data analyst role all about.

2. Skills

A Big Data analyst is primarily a data analyst, who is comfortable with Big Data technology. Let’s take a deeper look at the Big Data Analyst skill set and understand, how to become a Big Data analyst.

  • Programming

A traditional data analyst is not expected to be a programmer, but should be able to script together some program around data tools like Excel and Tableau. Big Data analysts, will need be proficient at programming, with many tools in this space requiring hands on skills at programming, like MapReduce and Spark. Languages that a Big Data Analyst is expected to be proficient in one or more of R, Python, C++, Java, Ruby, Hive, SQL, SPSS, SAS, Weka, MATLAB, Scala, Julia among others.

  • Database experience

As mentioned earlier, Big Data Analysts are data analysts at the core, and experience with relational and non-relational database systems is a hard requirement. Experience in popular database systems like MySQL, MS SQL Server, will be an advantage for picking up skills in Big Data databases like HBase, MongoDB, Teradata and distributed file systems like HDFS.

  • Big Data Frameworks

A Big Data analyst is obviously expected to know the common frameworks in use within Big Data, in and out. For a data analyst trying to get on a firm ground in the Big Data industry, some working knowledge of Big Data frameworks like Apache Hadoop, Apache Spark, Apache Storm, Apache Flink and MapReduce is essential.

Quantitative Aptitude and Statistics

Fundamental to an analyst role is good knowledge of Statistics and Mathematics (Linear Algebra). Although data analysts do not directly get involved in statistical modelling, but knowing these subjects help the analyst with what the Data Scientists might need when analysing and preparing the data for them to use.

  • Domain Knowledge

Domain or business knowledge will drive business improvements, without this knowledge, a big data analyst will not be able to make complete sense of the data ecosystem in and around the business. It is akin to having a mechanic work on a machine without knowing what the machine is used for. Knowing the data flows and work flows in the ecosystem will help derive better value.

3. Skillset summary

A big data analyst should be proficient in,

Database management systems– Relational Database Management systems, Non-Relational Databases Management systems, data warehousing concepts, SQL, No SQL

Big Data Architecture– Apache Hadoop, Apache Spark, MapReduce, Hive, Sqoop, Pig, HBase, YARN, Flume.

Programming Languages– Python, R, Java, JavaScript, Ruby, SPSS.

Data Visualizations– Python, Tableau, MATLAB, Lumify

ETL tools– Talend,

Fundamentals– Business Statistics, Mathematics.

4. Data Analyst Education Requirements

A bachelors in Computer Science will place you on the right track for a big data analyst role. Even if you aren’t one, you need to have a Bachelors degree in Mathematics or Statistics at least to be able to grasp the concepts better. You can still pickup the skills for a Big Data Analyst role, without these educational qualifications with the right aptitude and the right guidance on skill upgradation courses.

  • Prerequisites

There are certain prerequisites for big data analytics. A data analyst is expected to have strong fundamentals in computer science, statistics and mathematics and if working in Big Data Domain is expected to also have a well-rounded understanding of the Big Data Architecture, the tools that are used within this architecture. Strong communication skills to be able to present a story on findings and collaborating with teams on both the data engineering side and the data science side.

5. Scope

Scope of Big Data Analytics will only increase, as Big Data analytics will become a standard across the industry with an ever-increasing number of business wanting to explore the benefits of Machine Learning and AI. Machine Learning and AI thrive on Big Data, and for the Big Data infrastructure to be useful the data that is churned needs to be of top quality. This is where a data analyst is needed.

The role fits well in between the data engineer who maintains the data infrastructure and the data scientist who will use the data processed by the data analytics team to build accurate business models for predictive analytics and Machine Learning.  The need of big data analytics is driven by the need for Machine Learning and Artificial Intelligence technologies to stand out in the marketplace. The Bottom line is, Big Data Analytics is a top priority of most of the medium to large scale businesses.

Conclusion

As you can see there is a whole bunch of varied skills that are required of a Big Data Analyst. If you are interested to tread this challenging career path and figure out how to learn big data analytics, you need a more targeted approach and hand holding by experts.  Join big data analyst training courses at Jigsaw Academy.

Big data analysts are at the vanguard of the journey towards an ever more data-centric world. Being powerful intellectual resources, companies are going the extra mile to hire and retain them. You too can come on board, and take this journey with our Big Data Specialization course.

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