There are close to seven thousand devices connected to the internet right now and colossal volumes of data close to 2.5 million terabytes are generated in as less as 24 hours. It is also estimated by 2020, as more devices get developed and connected to the internet, there is a projection of around 30 million terabytes of data being collected every day. There is an increase in data collection abilities and there is also an exponential increase in computational powers. whether it is known as Machine Learning, Predictive Analysis, Artificial Intelligence, Statistics, Data Analytics or Data Science, the field of data science and analytics is on the rise owing to the mentioned explanations.
There are many ideas and varied understanding of the field of data science and all that it does, some are facts while others are perceptions.
Some facts remain true like….
- The Harvard business review has termed any job in Data Science as the sexiest job in the 21st century.
- Data science holds the path to uncover potential business data to help and set the businesses back on track
- There is a huge demand in the field of data science and analytics, and human resources is always struggling to fill in the gaps
- Yes, data science and analytics is a process through which data is sliced and diced so that you can process and analyse the data chunk for meaningful insight
- Data science and analytics have become an integral part of our lives in ways that we might not see, from searching a route in GPS, to shopping or a casual search on the internet, all generate data which is then processed for optimized performance. Simple just notice how many times the searches on shopping apps or even entertainment sites come up with the accurate recommendations. All this is a result of optimised performance due to data science and analytical capabilities.
Starting a career in data science does not require you to get a PH. D in science, but just knowing about the fundamentals of analytics, academics in statistics, or knowledge of data science tools, a good business acumen and strategic mindset which is inquisitive in finding patterns along with excellent communication skills is more than enough to embark in the field. Of course, you can upgrade the skills through a battery of effective certification available from reputed institutes. Universally recognised languages and programming technologies like SAS, R or Python coding Hadoop platform etc…, will help you settle and excel in this field.
Data science and analytics as a field is so vast and so distinct at the same time, that it becomes impossible to know everything that encompasses it. Every business will have a different approach to data science, it is always essential to also know the fact which will give an authentic picture of the field.
Like, Data is never clean but just an accumulated collection of random information, there are several levels of hypothesis and theories that need to be tested to get the correct suitable context, which can then be used to enhance business performance.
As a data scientist or a data analyst, you will spend most of your time cleaning and processing data for model consumption.
It is key to note that data science or Big Data eventually is a mere collection of tools and not a one-stop shop for solutions. Creating an analytical problem design, possessing an acute sense of scrutinising data, applying modelling best practices are irreplaceable and person dependent. Data science is an amalgamation of tools and techniques, put to use in a sequence of hypothesis testing. So in this field, one needs to build competencies, as tools might change but your machine learning capabilities and experience will persist with time.