Session on Predictive Modelling

The Department of Statistics & Data Science successfully conducted an expert session on “Predictive Modelling” for the students of VI B.Sc. Data Science and Economics & Analytics (DS & EA). The session was delivered by Mr. Yogesh P. Murumkar, CEO and Corporate Trainer, Bharat Software Solutions, a seasoned industry professional with extensive experience in data analytics, machine learning, and corporate training. The program aimed to bridge the gap between theoretical knowledge and industry-oriented applications in predictive analytics.

The session commenced with a brief introduction to the speaker and an overview of the importance of predictive modelling in today’s data-driven world. Mr. Yogesh explained how predictive modelling plays a crucial role in decision-making across various domains such as finance, healthcare, marketing, supply chain management, and business intelligence. He emphasized that predictive analytics has become an indispensable tool for organizations seeking to gain competitive advantage by anticipating future trends and outcomes.

During the technical part of the session, the speaker elaborated on the fundamental concepts of predictive modelling, including data preprocessing, feature selection, model building, training, validation, and evaluation. He explained commonly used predictive techniques such as linear regression, logistic regression, decision trees, and basic machine learning models, highlighting their practical relevance rather than focusing solely on mathematical formulations. Real-world examples were used extensively to help students understand how these models are applied to solve business problems.

Mr. Yogesh also discussed the importance of data quality and exploratory data analysis (EDA) before applying predictive models. He demonstrated how improper data handling can lead to inaccurate predictions and poor model performance. Special emphasis was given to understanding assumptions, avoiding overfitting, and selecting appropriate evaluation metrics such as accuracy, precision, recall, and RMSE, depending on the problem context.

An important segment of the session focused on industry expectations from data science graduates. The speaker guided students on the skills required to succeed in analytics roles, including programming proficiency, problem-solving skills, domain knowledge, and effective communication of insights. He also shared insights into how predictive modelling is used in real-time corporate projects and how students can prepare themselves through internships, hands-on projects, and continuous learning.

The session was interactive and engaging, with students actively participating by asking questions related to career opportunities, project selection, and the practical challenges faced while implementing predictive models. Mr. Yogesh addressed these queries patiently and provided valuable guidance based on his industry experience. His practical approach and real-life case studies helped students gain clarity on how classroom concepts translate into real-world applications.

The program concluded with a vote of thanks from the Department, acknowledging the speaker for his valuable time and insightful session. Overall, the session was highly informative and beneficial for the VI B.Sc. DS & EA students, enhancing their understanding of predictive modelling and motivating them to pursue industry-relevant skills. The department believes that such expert sessions significantly contribute to improving students’ employability and readiness for the data science profession.