The Data Industry – A Brief Overview

The data industry is projected to grow by leaps and bounds over the next decade. Massive amounts of data are being generated every day with a quintillion bytes being the safe estimate. Data professionals and statisticians are of paramount requirement in this fast-paced, data-driven world. They perform many tasks ranging from identification of data sources to analysis of data. Additionally, they find trends and patterns in the existing data at hand, however, the real set of duties would depend from organisation to organisation. Since data is relevant in almost every field now, the statistical requirements would also understandably change with the various sectors.

Candidates aspiring to step into this industry would be expected to have a fair knowledge about the statistical software in use, being proficient in one increases the job prospects manifold. It is nevertheless advisable that the potential employees narrow down the types of companies they wish to work for, say, for example, biostatistical organisations, and hone their skills accordingly.

The most popular programming software utilised for statistical analysis is STATA, SAS, R and Python.

STATA

In the words of StataCorp, Stata is “a complete, integrated statistical software package that provides everything you need for data analysis, data management, and graphics”. This software comes in handy while storing and managing large sets of data and is menu-driven. It is available for Windows, Mac and Linux systems. Stata is one of the leading econometric software packages sold in the market today. Such is its importance, that many universities have incorporated this in their coursework to make their students jobs ready. Over 1400 openings posted on Indeed put forward Stata as a precondition for selection. Facebook, Amazon and Mathematica are some of the many companies that require STATA as one of the qualifications for statistical and econometrics related positions.

Python

Being an incredibly versatile programming language, Python is immensely popular. It is accessible for most people as it is easy to learn and write. Organisations ranging from Google to Spotify, all use Python in their development teams. Recently, Python has become synonymous with Data Science. In contrast to other programming languages, such as R, Python excels when it comes to scalability. It is also considerably faster than STATA and is equipped with numerous data science libraries. Python’s growing popularity has in part stemmed from its well-known community. Finding a solution to a challenging problem has never been easier because of its tight-knit community.

SAS

This is a command-driven software package that proves to be useful for statistical analysis as well as data visualization. SAS has been leading the commercial analytics space and provides great technical support. The software is quite expensive, making it beyond reach for many individuals. However, private organisations hold a very large market share of SAS. It is relevant in the corporate world to a large extent.

Educational Qualifications and Online Courses

Employers typically look for statistics, economics, maths, computer science or engineering students for data-related jobs with more preferences given to candidates with post-graduate degree holders. The key skills in demand include proficiency in statistical software, model building and deployment, data preparation, data mining and impeccable analytical skills. People looking to upskill themselves or diversify into a different career path to attain a higher pay bracket should give the data industry a shot. Coursera, Udemy, LinkedIn and various other platforms provide affordable courses in data science, programming and analytics for this purpose. A career in data is a rewarding one, and also ensures maximum job satisfaction. This is a highly recommended profession in today’s time.

Big Data and IoT Explained

How Big Data Influences Your IoT Solution

Technology has been advancing and lives are getting improved everyday now. Businesses are doing everything to exceed the expectations of their customers and IoT is the next promising step towards the same. Internet of Things, IoT in short, is a platform that collects and analyses data from our regular use appliances with the aid of the internet and gives out information to both the manufacturer and user. This information could be about the servicing that is required or a part that has become dysfunctional and needs to be replaced. The huge amount of data that is generated by these sensor equipped machines is called the Big Data (no surprise there, hopefully).

Big Data has always been present but in earlier times, it was simple and could be easily recorded on Excel spreadsheets and analysed as such. The type of data wasn’t as complex back then and it could be easily filled into the cells of spreadsheets. Now, however, the format of data that is being transmitted is not very fixed and it could be in forms like audio, video and pictures. This data cannot be collected and analysed by traditional programs. New softwares (Cloud) are getting developed that can help separate the valuable information and recognise the trends or patterns if there are any. Examples of the same can be seen in apps like Netflix and Amazon Prime when they give you recommendations on the basis of your previous watch.

The big data is characterised by 4 Vs: volume, velocity, variety and veracity. The volume of data could be trillions of gigabytes and has to be stored at multiple locations. The velocity of transmission and collection of data currently, is unprecedented. ‘Variety’ refers to the format of data that can be both structured and unstructured but equally important. Veracity or the accuracy of the set of data that has been generated by a source and needs to be verified in case of redundancy or just to check if the data is suitable for analysis by a particular software. The role of data analyst becomes important with this discussion and it will be the most sought after profession eventually.

IoT and Big Data are very similar but yet very distinct at the same time. IoT seeks to analyse the data when it is transmitted and the data then contributes to the Big Data. A company can use both the technologies at the same time. For example, a sensor in a car could emit a signal that the car is in need of servicing and the owner might get a notification reminder for the same. The data of the times all the cars of the same company required servicing can be stored and used for predictive analysis for newly manufactured cars. 

The integration of the two technologies can help the both consumer and seller in the long run. The businesses will make more profits as they will become more efficient in catering to the needs of their customer and the overall costs will get reduced as well. The customer might be hesitant at first, for the idea of their appliances tracking their usage behavior seems like invasion of privacy, but it will save their time and money in maintenance and replacement.