If we have data, let’s look at data. If all we have are opinions, let’s go with mine, said Jim Barksdale, former Netscape CEO.
We have data and we have it in abundance. The world has just realized the importance of looking at it closely.
Data overload or information overload is the monster that came out of a swift surge in technological development. A monster that can be tamed and used effectively. And this exactly is the role of a Data science professional, taming the monster to help organizations make timely, strategic and relevant decisions
Fortune magazine calls Data Scientist ¨the nerdy-cool job that companies are scrambling to fill¨.
IBM Predicts that demand For Data Science professionals will Soar 28% by 2020. The report also says that Data Science jobs remain open an average of 45 days, five days longer than the market average which is disrupting the job market1.
McKinsey predicted in 2011 that, by 2018 there will be a gap of 50% in the required skill set against the demand2. Research in this field nowadays points towards the same.
And it is simple economics that the monetary value for the skill is directly proportionate to the scarcity of talent.
Data Science is undergoing deep changes in how it is practiced and successfully deployed to solve business problems and create strategic advantage. This points towards major changes in how data science professionals will do their work. The landscape of Data Science career is about to get very exciting. Here is how.
● Output of efforts by Data Science professionals will directly address customer needs. There cannot be a better motivation to work. For example, it is Data Science that helps Lyft, an on-demand transportation company based in California understand which customers they are most in danger of losing before they lose them. This preemptive analysis helps them take immediate customer retention actions.
● Data analysis will no longer be a dry subject. The world of Data Science has become very stimulating through weaving of interesting stories via deep data extrapolation. The change in the realm of travel is an example for this. Travel Industry is changing itself to adapt to people’s need for transformational travel. Travel companies & hoteliers collect data from social media platforms and weave it together to anticipate changes in preferences and create unique and personalized travel experiences.
● The field is very dynamic and challenging and keeps you on your toes. With democratization of data and surge of technological development, what will keep you on top of the game is constant vigilance, courage to accept challenges and openness to follow the unconventional road.
● And, the spotlight is on Data Science.
Aren’t these reasons enough to take the plunge?
A Birds eye view of the Data Science career landscape
It is therefore imperative to know what you want to do when you search for a position in Data Science, as Data Scientist is a very broad and ambiguous term! Here is a peek in to the space.
The possibilities within the space of Data Science are plentiful and they are expanding rapidly. Each of the role requires specific set of competencies which in turn entails deep knowledge & training in multiple tools & techniques.
Electricity is not 24×7. Daily wage is about $1.
We feel naked and panic when we leave home without our phones, but this is the reality of people in the remote areas of the world. Any solution needs to acknowledge and accommodate these constraints and the realities.
Nothing to take away from Google and Facebook, but there has to be a lot more on the ground than any number of balloons or drones in the stratosphere.
The most searched queries in Google about Data Science are:
● What is the qualification required to enter the Data Science space?How closely related is Data Science, Machine learning & AI?
● How important is data visualization?
● What skills are most important for a career in Data Science?
● What are the top tools & technology that one should have a good grasp on?
● How are different industries using Data Science?
● What are the most coveted Data Science competencies?
● What are the different job roles available in the Data Science space?
● How is each role different from the other?
● How to choose the right role for me?
We will address each of these queries in detail in the following blogs. Keep watching this space!!
● The Quant Crunch, IBM (https://www-01.ibm.com/common/ssi/cgi-bin/ssialias?htmlfid=IML14576USEN&)