Big Data As An Academic Discipline

The accelerated speed with which businesses across all verticals worldwide are embracing the Big Data analytics to fulfill their data eccentric goals, there has been an enormous rise in the number of job roles available in this discipline. Since the massive amounts of data generated every second is crucial for businesses in devising strategies and taking decisions for work process optimization, increasing footprints, enlarging customer base and boost profits, enterprises are now and in future, will be in greater need of Big Data professionals. Moreover, these Big Data jobs are becoming popular as they are known as premium jobs that pay handsome salaries to the professionals.

To manage the rising Big Data requirements, colleges and universities have come up with advanced courses or programs on analytics. The McKinsey Co. report suggests a mega shortage of analytical talent ranging up to 1,90,000 people by 2018. While there have been already various analytics courses but advanced analytic courses encompass wider analytical subjects such as data mining, data science, business intelligence, machine learning and statistics, data engineering, data analysis, business analysis, etc. Hot and trending topics include analytical tools such as sensor, social media, cloud, purchase transactions, mobile and other sources of Big Data including predictive modelling.

Since collecting, preserving and making analysis using a combination of various tools gives a business crucial actionable insights, the use of Big Data technology has widened various avenues of analytics that have triggered huge scope for education and career in some of these prominent analytical areas:-

1. Data Science

It is basically an interdisciplinary field that deals with processes and systems to extract knowledgeable and valuable insights from heaps of structured and unstructured data. The techniques and theories used by data science are largely drawn from many fields such as statistics, operations research, information science, computer science, machine learning, mathematics, visualisation, predictive analytics, data warehousing, etc. Covering all industries and fields such as digital analytics, marketing, search technology, fraud, energy, healthcare, astronomy, social networks, finance, security, mobile, weather forecasts, etc., the data science has created various lucrative job titles like data scientist, senior analyst, director of analytics and likewise.

Data scientists are expected to have great business acumen and in-depth domain expertise along with knowledge of technical skills like Hive, HBase, Pig, Hadoop, Cassandra, etc. and programming languages like Python and R programming.

2. Computer Science

Finding similarities with data science, computer science engulfs concepts and techniques like MapReduce and Hadoop implementations, complex algorithmic and computational applications, data plumbing, etc., for tackling problems arising in encryption, random number generation, internet topology mapping, steganography and data compression.

3. Machine Learning

It is a computer science discipline, which is a part of data science and is closely associated with data mining. Largely based on designing algorithms, machine learning also lays emphasis on prototyping algorithms and designing automated systems that automatically update, refine and discover new rules and patterns on regular basis. The machine learning development involves usage of Python programming language and the core algorithms comprise of clustering and supervised data classification, rule systems and scoring techniques.

4. Data Mining

This discipline deals with designing algorithms for extracting deeper insights from large and mostly unstructured data. Some of the popular data mining techniques include clustering, pattern recognition, supervised classification, feature selection and few other statistics. Through implementation of robust and data driven techniques, data mining acts as a subset of data science to employ statistical methods in analysing patterns and classifying data.  

5. Predictive Modelling

Industries take up predictive modelling projects that aim at designing applications, which predicts future through analysis of past and real time data. Predictive modelling techniques have found their roots in statistical science and play a crucial role in deciding various marketing strategies and designing more customer centric approach.

6. Data Engineering

This is an integral part of computer science that deals with powering systems for allowing different types of data to get easily processed and reach seamlessly to the end users. Data engineering is basically performed by software developers or designers. Data warehousing is a sub-domain of data engineering and is now being largely replaced by NoSQL, NewSQL and graph databases.  

7. Business Intelligence

It involves metric selection, dashboard creation, competitive intelligence, producing and scheduling of data reports while working with data architects for designing database schema. Business analysts are responsible for carrying out these activities and they have an MBA degree with advanced knowledge of statistics and basic understanding of analytics. They work with data scientists to ensure excellence in decision science, extraction of insights, business consulting, data visualising, KPI and ROI optimisation. 

8. Data Analysis

It implies designing several applications such as attribution modelling, advertising mix modelling, sales forecasts, cross-selling optimisation, user segmentation, fraud detection, cost of acquisition, churn analysis, etc. Data analysts must have strong data analytical skills but have quite narrow understanding of the Big Data architecture or working of data science techniques. They may hold senior positions in the IT infrastructure but they don’t have a significant role in extracting insights, presenting them or making strategic conclusions.

With such extensive scope of Big Data analytics in academics, pursuing a career in this direction unleashes an ocean of opportunities that can further groom the professionals while backing them with good financial condition too. Here are 4 pertinent reasons that can establish strong reasoning as to why getting enrolled in Big Data analytical courses can prove the best career decision:-

1. Incredible Job Opportunities To Meet The Skill Gap

Despite the demand for Big Data professionals is on the surge, there is huge gap on the supply side. It has been seen that still large number of vacancies globally in various Big Data analytics profession is not filled. A McKinsey Global Institute study reveals that by 2018, US will be facing a shortage of 190000 data scientists and 1.5 million managers and analysts, who play a crucial role in decision making process. India at present marks the highest concentration of Big Data professionals but still the demand for such analytical talent is on the higher side.

2. Increasing Demand for Analytics Professionals

Researches have revealed that the demand for Big Data management and analytics professionals have surged remarkably in the past few years and various job portals have witnessed growing trend in job opportunities in this sector. The steady increase in the job opportunities implies the requirements of technology professionals who are well versed with Big Data and analytics as increased number of organisations are exploiting analytics for competitively boosting their market footprints and revenue. 

3. Scope Of Earning Great Income

Big Data is known to create job roles that pay big bucks and the strong and rising demand for data analytics skills is even boosting the salary packages of Big Data qualified professionals. A look at the figures in increase in salaries in Australia, UK and India shows notable positive growth in salary trends for most of the Big Data analytics job titles.

4. Big Data Is Growing Exponentially

As per the ‘Peer Research – Big Data Analytics’ survey, Big Data has been found to be the top most priorities of organisations worldwide as it is believed to exponentially boost the performance of businesses in almost all verticals. Sophisticated data analytics can be easily performed on increasingly large sets of data through newer technologies like Big Data. With Big Data proving its credibility in improving the competitiveness quotient of enterprises, rising number of businesses is implementing diverse analytics tools leverage the massive amounts of data generated.

The importance of analytics is increasing in businesses as they stimulate better decision making. It helps in driving profitable business strategies while boosting the effectiveness of decisions. The pertinence of Big Data analytics has sought a huge growth owing to its excellence in analysing the humongous increase in unstructured and semi-structured data such as weblogs, social media feeds, photos, emails, videos, etc. With so much to offer to businesses, lucrative job roles are being created for harnessing the best of the petabytes of data generated every second.