Interview with Harish Subramanian, Program Director, PGP- Big Data Analytics by GLIM

Introduction

Big data is being generated all around us. Every social media exchange, every digital process, every connected device and machine are generating data to be used by various companies.

Companies today are using Big Data for deepening customer engagement, optimizing operations, preventing threats & fraud. In the past two years, companies like IBM, GE, Amazon, Uber have come up with hundred of job positions for big data engineers & data scientists. To make sense of this data it requires optimal resources & at par analytical skills.

Harish Subramanian

To ensure a successful career in Big data it is important to acquire relevant skills. One way to do that is take up a comprehensive program which acquaints you with knowledge & experience. Recently, GLIM (Great Lakes Institute of Management) started offering a Big Data Analytics program in addition to its current program. To know more about the program, I spoke to Harish Subramanian, Program Director.

Here are excerpts of my conversation with Harish Subramanian.

KJ: Recently you have launched a Post graduate program in Big Data Analytics. Can you tell us briefly about the program?

Mr Subramanian: In the last decade, we have seen a quantum leap in storage, processing, computation, and sensing technologies. With this, Big Data technologies have gone from being the tools of a select few researchers to an industry standard essential to understanding anything about the world we live in.

The Great Lakes Post – Graduate Program in Big Data Analytics is a rigorous program that will help participants understand and apply the tools and techniques fundamental to handling the challenges of a Big Data world. New tools are developing every day, and data science professionals need to keep up with these trends and create a state-of-the-art solution by navigating the complex maze of tools available to them. At the same time, we help build the ability to make appropriate choices of data sources and possible analysis techniques – a task that is getting to be a larger challenge with each passing day.

KJ: Why do you think there’s a need for a specialized program in Big Data Analytics?

Mr Subramanian: We’ve spent years collaborating with a host of industry experts, data scientists and recruiting managers, and the consensus is that there is a burning need for cross-disciplinary technology professionals in the data analytics space – those that are able to not only use the pertinent tools, but also comprehend the trade-offs in model selection, implementation and data capture. And data analysis is certainly a team sport. A good data analysis team is like a well-choreographed dance group made up of data architects, engineers, analysts and data scientists (with ample support from IT and business leaders).

In this program, we’re building just this intersection of skills our emphasis is on developing professionals who can work with disparate data sources, analyze them, draw valuable conclusions and communicate the insight in a compelling way.

KJ: Who is the ideal candidate for this program?

Mr Subramanian: This program is aimed at technically minded problem solvers who are looking to build a career in big data technologies, data science and advanced analytics. In terms of skills and background, the ideal candidate will have a good understanding of how data is sourced and managed, have programming abilities that will allow them to write and test models effectively, and understand the basics of statistics. More broadly, though, the ideal candidate for the program is a mid-career technology professional who wants to build on their existing expertise with technology environments to build a cross-disciplinary career in Data Analytics in the Big Data environment.

KJ: What will the participants gain from this program?

Mr Subramanian: Through this program, participants will become conversant in the statistical foundations upon which the field of analytics is built, a variety of big data technologies to handle complex data, the most pertinent machine learning techniques needed to make sense of all this data, and some compelling visualization techniques that help communicate this insight effectively.

At the end of this program:

• Participants will be comfortable working on Big Data storage, processing, analysis techniques, visualization, and applications.
• They will be able to choose the appropriate technology solution to a complex problem.
• They will be comfortable analyzing complex and large data using a range of Machine Learning and advanced analytical techniques.
• They will be able to synthesize a deluge of data into lucid visualizations using a set of powerful tools.
• Through practical assignments and mentored projects, they will develop fluency in the tools and techniques necessary to make sense of complex, large aggregations of data.

KJ: How is this program different from others that candidates might be considering?

Mr Subramanian: At the heart of it, we have built a program that will allow participants to see the whole picture when it comes to the use of vast amounts of messy data to draw business insights. We don’t focus narrowly on a set of ‘hot tools’ or on the mathematical models alone because the context is important to a successful data science team. We also want to give our participants enough of a foundation to build the kind of career they want.

KJ: Can you tell us more about the curriculum?

Mr Subramanian: The program consists of about 200 hours of classroom sessions that include hands-on exploration of the tools and techniques, and an equivalent amount of time that participants will spend on assignments, assessments, projects and other content.

The classroom sessions spread over 12-weekend intensive sessions, covering the following areas:

•  Statistical foundations of Data Science – Descriptive, Inferential and Predictive
• Analytical tools – Python, R and libraries for data science and visualization
• Big Data Technologies – Hadoop, Sqoop, Hive, Pig and the Spark ecosystem
• Machine Learning on Big Data – Supervised, Unsupervised, recommenders, graph computation and ensemble techniques
• Visualization and applications – visualization for data exploration, analysis and reporting/display

Each of these areas will be covered through a series of practical examples, hands-on lab sessions and a structured series of projects.

KJ: What kind of practical exposure would be offered to students as part of this program?

Mr Subramanian: We believe very strongly in showing, not telling. So, every module is interactive and rich with practical examples, real-world datasets and handy tips. Since industry experts and practitioners play a major part in this program, participants will learn what works and what doesn’t in practice.

Additionally, all participants will build their own portfolio of mini-projects and end with a capstone project. We intend for everyone who has gone through this program to be ready to hit the ground running from their very first day on the job.

KJ: How will this program be delivered/taught? Is there any industry participation in the course?

Mr Subramanian: This 1-year blended program gives participants an immersive experience – combining hands-on programming experience on technologies they will encounter in their Big Data Analytics careers with real-world context from leading industry practitioners. The program also hinges on physical classroom sessions as the fundamental learning environment as these give us the opportunity to be as targeted and interactive as necessary. These are supported by online assessments and content. Additionally, participants have unlimited access to our Big Data Lab on the cloud – which has a range of preconfigured Big Data tools including Hadoop, Apache Spark and a host of related functional tools.

Industry experts act as faculty, deliver seminars and guide participants through their projects. In fact, they’re involved in every aspect of our program and have been since inception.

KJ: Are there any prerequisites for candidates looking to do this program?

Mr Subramanian: Yes, we expect that applicants have at least 2 years of professional experience post-graduation and that this experience should be in a technical role. Since this is a hands-on program, we expect that candidates have programmed in languages such as Python, Java, C++ or R. Even if they haven’t programmed recently, it would help if they are comfortable with programming environments and are willing to put in the work to do some pre-work to get up to speed. We also require that candidates are familiar with college level mathematics and statistics.

KJ: When does the next batch begin and how can our community members enroll for this course?

Mr Subramanian: The batch begins on the 25th of March. We conduct rolling admissions and decisions are made soon after each deadline, so the earlier someone applies, the greater their chances of securing their spot in the program. Interested candidates can go to our website to learn more or to apply.

KJ: Thanks, Mr Subramanian for taking out the time for this interview.

Learn, compete, hack and get hired!

Kunal Jain

Kunal Jain is the Founder and CEO of Analytics Vidhya, one of the world's leading communities of Al professionals. With over 17 years of experience in the field, Kunal has been instrumental in shaping the global Al landscape. His expertise spans diverse markets, from developed economies like the UK to emerging ones like India, where he has successfully led and delivered complex data-driven solutions. As a recognized thought leader, Kunal has empowered countless individuals to realize their Al ambitions through his visionary approach to Al education and community building. Before founding Analytics Vidhya, Kunal earned both his undergraduate and postgraduate degrees from IIT Bombay and held key roles at Capital One and Aviva Life Insurance across multiple geographies. His passion lies at the intersection of analytics, Al, and fostering a thriving community of data science professionals.