Dakshinamurthy V. Kolluru is the Founder & President of INSOFE (International School of Engineering), an organization that champions training & certification, consulting, research and product development in Data Science and Big Data Analytics. His expertise lies in simplifying complex ideas and communicating them clearly and excitingly.
He has worked extensively in Data Science with majority of his work pertaining to mathematical algorithms and pattern extraction. He has helped set up several Data Science COEs. Augmented by his people skills, Dakshinamurthy proactively steered INSOFE into being the globally acclaimed School of Applied Engineering it is today.
We recently asked him for his Big Data expertise. Here’s what he shared:
Can you tell us about the mission behind INSOFE? How are you hoping to impact the world of Big Data?
INSOFE was founded with the vision of initiating an educational revolution in applied engineering learning and establishing a world class educational institution in India. With an early vision of the Data Science movement, INSOFE curated an industry relevant program with a curriculum that feeds from real-time business problems.
INSOFE programs were never about making students merely employable. They are about creating world-class specialist engineers who can help their companies zoom ahead in the technology space and race. We are engaged in several product development and research activities in the areas of IOTs, smart cities, healthcare and manufacturing which help bring in a change at a broader level and support smart data-driven decision making.
What types of training is essential for anyone interested in a career in Data Science today?
Data Science is the most sought-after career today and the demand for highly skilled data scientists is growing at a blazing speed.
Today, a data scientist requires a combination of software hacking skills that involve collecting data from a variety of sources, then putting together the data to be analyzed, along with machine learning and statistical skills. Together these enable them to run models on the data, and with some visualization skills, the data scientist can present the results in a way that is simple, yet effective, for the decision maker to understand the outputs and implement changes accordingly.
What are companies looking for in data engineers today? What is the demand for these skills?
The main objective here is to move from intuition-based decisions to data-driven decisions and support business owners and managers in complex decision making. Data scientists play around with huge sets of data, analyzing trends and crunching numbers.
A day in the life of a data scientist includes gathering a huge amount of data, and then segregating and analyzing the data using several techniques to find patterns or derive solutions. Apart from basic coding and math background, an aptitude for analytical thinking, inquisitiveness, intellectual curiosity and business acumen would make that data scientist a strong asset to an organization.
Who within an organization should consider getting advanced training in data analytics and visualization?
Every manager involved in decision making at all levels can benefit from training in data analytics and visualization. And a team of data scientists within an organization looking to upskill can take up an advanced training program.
What are the most innovative ways you’ve observed these skills being applied in a professional setting? What organizations are using data in the most interesting ways?
Several major players and start-ups have been benefiting extensively from the use of Data Science. Some interesting examples include:
- PayPal using Big Data analytics to detect fraud,
- retail companies customizing individual customer preferences and building products to meet them,
- and the pharma industry building better medicines at a much faster pace.
What Big Data topics should data professionals be following today? Why are they important?
When it comes to software hacking, developments in Big Data management, high-performance computing and governance play an important role. In Mathematical models, Deep Learning and Bayesian Learning show some enormous progress. And in Visualization, understanding the way organizations are using Data Science and visualizing the results to reach out to employees gives a good know-how of the way things work.
Related: Deep Data is the New Big Data
What Data Science research are you following today?
We are actively involved in research and product development mainly pertaining to the areas of Internet of Things (IoT) and computer vision and their applications in urban planning, smart cities and healthcare.
Building smart social systems that provide smart decision support to policy makers and Deep Learning in manufacturing are some of the other research interests that we are engaged in and keep ourselves constantly updated to be on par with the industry. The fundamental change that you can bring in the society and contribute through such research projects is where our interest lies.
What are your predictions for the future of how organizations use Big Data? Where do you see the most potential for its application?
Data Science is coming to be known as the electricity of this century. So, it is not a question of how it is to be used but about how to do without it.
With the complex business problems that we see today and the increasing demand for data scientists, it is a well-known fact that Data Science has high potential for its application in every sector. There is no area which has gone untouched by Data Science – It is pervasive and ubiquitous