Pre-Conference Workshop 2023
Predictive and Prescriptive Analytics using Open source software(s).
In a digitally transformed and data-driven world, data and analytics combine to provide a competitive advantage to businesses. Given the volume of available data, organizations need a good way to analyze it, and that's where predictive and prescriptive analytics can be useful. Using all four types of analytics namely Descriptive, Diagnostic, Predictive, and Prescriptive analytics helps create a complete picture of the story that data tells. Describing trends and patterns and understanding why the patterns occur is the most common approach to understanding business problems. But it is important to dig deeper to make informed predictions about the patterns observed and finally present actionable solutions to meet organizational goals. While predictive analytics helps predict the future by analyzing and identifying patterns in historical data, prescriptive analytics goes beyond explanations and predictions to recommend the best course of action moving forward.
This workshop will be addressing key features of predictive and prescriptive analytics such as predictive modeling, decision analysis, and optimization. Further, applications of predictive and prescriptive techniques across industries will be discussed. The workshop will be a two-and-a-half day workshop scheduled for the 20th - 22nd of March 2023. 20th and 21st will be offline while the sessions on the 22nd will be online.
Workshop Modules
Two and a half days hands on workshop for Faculty, Practitioners, Research Scholars and MBA Students
Software: Python, Google Colab and other open source tools
Duration: Each module if for 90 minutes.
Dates: 20th, 21st, and 22nd March 2023
Day 1: (Offline)
Introduction - Analytics & Data Science & Machine Learning
Data Pre-Processing & Exploratory Data Analysis
Data Visualisation and Dash-boarding
ML - Regression - Operations Cost
Day 2: (Offline)
ML - Classification - Customer Retention
ML - Anomaly Detection - Financial Fraud
ML - Natural Language Processing (NLP) - Customer Service
Optimization - Linear & Nonlinear Programming - Manufacturing
Day 3: 2 Sessions (Online)
Discrete Event Simulation - Capacity Planning
Causal Modelling - Prescriptive Analytics
Expected Outcome
It will help researchers understand data-informed decision-making and create better models and provide efficient solutions to their research problems. It will help industry practitioners understand the last mile solution like increasing efficiency, improving productivity, mitigating risk, and enhancing customer loyalty. The combined knowledge of predictive and prescriptive techniques will help academicians and practitioners make smart, game-changing decisions to achieve better outcomes.
Workshop Facilitator
Sri Vallabha Deevi
Sri Vallabha is a Principal Data Scientist with Tiger Analytics. As a part of this role, he leads teams solving business problems in various industries like retail, transportation and manufacturing. In the past projects at Tiger Analytics he worked on developing Deep Learning models for video analytics and Machine Learning models for anomaly detection & price prediction, Recommendation Systems for retail clients and Natural Language Processing for information processing. His current projects are focussed on Reinforcement Learning and Statistical Testing for Marketing and Promotions.
Prior to this, he worked as a Post Doctoral Fellow at IIT Madras in statistical analysis of liquid sprays. His doctoral thesis was on Large Eddy Simulation of Sprays, a branch of Computational Fluid Dynamics. His areas of expertise are machine learning, statistical analysis, reduced order modelling and simulation of physical systems & scientific computing. He holds a B.Tech in Mechanical Engineering from IIT Madras and a Ph.D. in Aerospace Engineering from Indian Institute of Science, Bangalore.