Data Science in Practice

Masterclass on ‘Data Science in Practice’ where Prof. Sashikumaar Ganesan and Prof. Deepak Subramani from the prestigious IISc, share a glimpse of the evolving domain of Data Science. 

Data Science is everywhere – from fraud and risk detection, internet search, targeted advertising, product recommendations, advanced image and speech recognition, predictive modelling to product development and more. 

Data Science is exploding 

The global Data Science platform market is expected to hit $25.94 billion by 2027. It will expand at a compound annual growth rate (CAGR) of 26.9 percent from 2020 to 2027, as per GrandView Research. Today, Data Science platforms can empower Data Scientists to design techniques, reveal insights from information, and a lot more. The focus has become stressed because the COVID-19 (Coronavirus Disease) pandemic has also affected the Data Science industry. The models earlier used for forecasting or segmentation are failing. This could be because of rapid changes in online traffic or shopping patterns, as stated in this report. 

As companies revisit their data strategy and map short, medium, and long-term data-driven plans to make informed decisions, there would be a need for a deeper understanding of data models. There will undoubtedly be a massive demand for the right people to design and operate these models.

Building Data Science capabilities

It is an excellent time to sharpen one’s Data Science proficiency and be ready for all the new defining opportunities ahead with Data Science. As the pandemic has reminded us well, every industry would be impacted with the help of data. There is a significant need to invest in the right capabilities, which would traverse all kinds of domains. So no matter which vertical you are in, it is advisable that you build up your ability to leverage data through intelligent and effective models. Without this expertise, it would be hard to leverage what the future brings forth. For example, one needs to know why and how to avoid over-fitting, regularization, over-learning excessively based on existing data, etc.

The world of Computational Data Science

Computational Data Science is the study and practice of Data Science that requires modern high-end computational infrastructure.

IISc Faculty Masterclass on ‘Data Science in Practice’ where Prof. Sashikumaar Ganesan and Prof. Deepak Subramani from IISc shares a glimpse of the evolving domain of Data Science

Also this space forks into supervised and unsupervised learning.This can go deeper into clustering, anomaly detection, density estimation, and dimensionality reduction in unsupervised categories as well. And similarly into the workings of instance-based and model-based decision structures.

Simply use a tree with as many leaves as several training points. Restrict the decision tree’s degrees of freedom. Use some hyper-parameters to regularize and avoid overfitting.  There is a full assignment in the Computational Data Science Programme to learn how to do it. We have to also learn about issues like – decision trees produce orthogonal decision boundaries. Also, rotating data makes the tree convoluted. Decision trees are sensitive to slight variations in training data. That’s where random forests can overcome this instability by ensemble learning. 

That’s why this program is built on basics and the building of mathematical models. The program is designed with many assignments, one-to-one interactions, cohorts, and doubt-clearing sessions with unique slots with professors. Structured projects, hands-on experience for theory as well practice are being focused on here. The emphasis is on intuitive understanding as well.

All classes are live. As to the program’s eligibility, any graduate with comfort in basic mathematics and coding language with some work experience would be a good fit for the program. Team leads, architects, software developers, exceptional engineering graduates from various sectors like IT, healthcare, banking – can easily join. It is an advanced program that will give candidates an added advantage, he iterated.

The practice, needs, and constraints of making a model are difficult but of enormous advantage today. These aspects were unlocked well in this Masterclass on ‘Data Science in Practice.’

TalentSprint has been covering many aspects as an interactive series in the emerging technology space like AI, ML, Cyber Security, Data Science, FinTech, IoT, etc., through such discussions and master classes. As a result, current and aspiring professionals get a direct peek into the breadth and depth of the DeepTech programs.


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