A Journey of Skilled Transformation from Mechanical Engineering to Data Analytics – Sushanth Sugunan’s MAIB Experience
A Mechanical Engineer by profession, Sushanth Sugunan works as a Lead Data Analyst and works closely with the sales team to develop proof of concept solutions to sell their AI solution.
With nearly 11 years of industry experience, including a decade of working in the energy sector, specifically power generation utility, he has developed skills and expertise in construction, operations, and data analysis in the energy utility. He has also been working as a teaching professional while pursuing a Master of Artificial Intelligence in Business at SP Jain.
What led Sushanth to transition from mechanical engineering to data analytics? How did SP Jain’s Master of Artificial Intelligence in Business program equip him with the necessary skill set for a successful career in this field? Let’s find out.
Can you tell us a bit about yourself?
Though I come from a mechanical engineering background, I work as a Lead Data Analyst in an organisation. My areas of interest include building solutions to solve in-house problems, working alongside stakeholders, and bringing in transformative ideas. There are also some tools that I am hands-on with, like Power BI, Python, R, SQL, and SAS JMP.
Additionally, engaging in the training and development of working professionals and students in areas of data science and analytics for about 1.5 years has sharpened my skills in this domain. Parallel to my journey as a teaching professional, my postgraduate degree – Master of Artificial Intelligence in Business at SP Jain, is nearing completion. During the academic track, I got an opportunity to work on a range of datasets – from financial data to customer behaviour data and successfully provide valuable insights to stakeholders.
Would you like to take us through your MAIB journey with SP Jain?
Coming from a non-coding and non-IT academic background, I embarked on a fantastic roller-coaster ride of transforming into a skilled data analytics professional. Throughout this journey, I prioritised improving my coding skills, data analytics expertise, and knowledge of machine learning without neglecting my management subjects.
Both management and data science subjects are well-integrated in the program. While management subjects in the course curriculum are well-taught, the data science ones bridge the connection to solving real-world business problems using ML algorithms.
Gaining opportunities for internship roles and research projects like – the development of a state-of-the-art recommendation system platform, and working closely with professors have enriched my problem-solving and lateral thinking abilities. With the support of the SP Jain faculty, I also got the opportunity to teach students and working professionals in the fields of data science and analytics during the course tenure.
Would you like to share a bit about your academic projects with us?
Speaking about my academic projects, I have developed an admission and placement prediction model with an HTML interface. It utilised a neural network and classification approach and was built using Python and Visual Studio. I used Django, a high-level Python web framework to create the interface.
I have also worked on an academic project that involved developing a recommendation system for H&M’s fashion e-commerce platform. It utilised a two-tower architecture approach, built with TensorFlow using Python and Visual Studio. The recommendation system included both collaborative and item-based filtering techniques to provide personalised recommendations to users. My project is available on GitHub here. (hyperlink ‘here’ with GitHub – sushanthsugunan/H-M_Recommendation)
Moreover, I have worked on a credit card fraud analytics project as an academic assignment using anomaly detection analysis. I developed it using Python, Visual Studio, Streamlit, and GitHub. The fraud prediction model included class imbalance handling techniques and was created using a variety of machine learning classification models, including logistic regression, ensemble methodologies, and neural networks. It is available on GitHub here. (hyperlink ‘here’ with https://github.com/sushanthsugunan/Fraud_analytics.git)
Furthermore, I have worked on developing a credit risk model. The model included the development of probability of default (PD), loss given default (LGD), and exposure at default (EAD) models, with the calculation of cumulative expected loss. The project utilised Python, Visual Studio, Streamlit, and GitHub, and the classification models in the project involved logistic regression, ensemble methodologies, and neural networks. It also included class imbalance handling and threshold tuning algorithms.
Finally, I am currently working on a research project in which I am developing a state-of-the-art recommendation system using the NVIDIA Melin framework for large e-commerce players.
Do you wish to take the first step towards a successful career in the field of AI and data analytics? Know more about SP Jain’s MAIB program here.
