With the advancement in technical evolution, a wellspring of opportunities are seen created for budding data scientists. Take a look around, I am sure you will find data everywhere! According to sources, over 2.5 Quintilian bytes of data is created on a daily basis. This is approximately 1.7 Mb of data being generated every second on the planet. So imagine how much you require to analyze!
By now you must have realized the fact that we are unable to meet the demand for data scientists. Nevertheless, that demand continues to rise – as do data scientists’ salaries.
I call it the sexiest jobs of the 21st century due to its involvement across a range of industries. Take any business into account, they require to perform critical research such as business analytic, market analyses, sales forecasting and anticipating labor demands. Between 2015 and 2017, big data implementation grew 17%, with adoption rising to 53%.
As an early adopter, you may not know the best tactics for leveraging data or have sufficient talent to maximize the use of existing information, even though they are benefiting from the advantages of technology.
Welcome to the data-driven world!
Many of you including software developers, data scientists, and big data analytic experts would agree benefits of increasing the democratization of data are many. Moreover, accessing both structured and unstructured information has become easier than ever, any guesses how? Social networking- no brainier! The ubiquitous presence of search engines and numerous bunch of platforms have the potential to offer a lot of useful information that can transform any business. And maybe that’s why more and more private companies, government agencies, institutions and organizations of all kinds are looking for programming experts like Python developers. Slowly and steadily the trend is getting hot on heals.
Further below I would like to mention certain trends which are more likely to rise by 2020.
1. Hyper-Automation
It seems that the data science pipeline is becoming more automated, have you wondered why? Well, doesn’t it seem obvious that most of a Data Scientist’s expensive time is spent behind cleaning big chunks of big data? To top it all, enterprises such as IBM have started offering automation and tooling for data cleaning.
Day by day the rising adoption of Robotic Process Automation (RPA), has been evolving across a wide range of industries. By infusing intelligence in automation through data science and analytics, the era of hyper-automation seems to be expanding its territory like never before. Moreover, this combination will help enterprises in evaluating risks and control mechanisms associated with hyper-automation.
2. Data privacy and security
One of the most sensitive and sensible topics in the tech industry include the term privacy and security. With the evolving techs, companies aim to evolve at a fanatic speed which often results in them losing the trust of their customers especially in regards to privacy or security issues. Over a span of years, data security and privacy have become a major concern – all thanks to the magnified public hacks. But have you ever wondered how did we end up being vulnerable? Who is responsible when it comes to securing the data? The worst thing is other than entrepreneurs, consumers are becoming more and more fearful or conscious of whom they give their email address and phone number out to.
As we know the entire Data Science process is fueled by data that cannot be considered anonymous at all. When in the wrong hands, it can fuel catastrophes and upset everyday people’s privacy and livelihood. In addition to this, data isn’t just any number it is something that describes real people and their crucial information.
3. Natural Language Processing
NLP is a well-known term among the software development realm- all because of the huge breakthroughs it offered in Deep Learning research. Initially started as an analysis of purely raw numbers as it was one of the easiest ways to handle things and store them in spreadsheets. And now it is processed where it would need to be categorized or somehow converted into numbers.
Imagine compressing a paragraph of text to a single number. Doing this manually might not work at it best because chances are that we might miss out due to the lack of ability to represent that information as numbers. By integrating, NLP and deep learning, you can now extract information from large bodies of text incredibly quickly. Data Science as a whole is growing and so are we!
