Online Development Program on Business Forecasting: Time Series Data Analysis

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Online Development Program on Business Forecasting: Time Series Data Analysis

Duration and Schedule

• 15 hrs (10 sessions of 1.5 hrs)
• The programme will be conducted (tentatively) between October 02-06, 2023. There will be two sessions per day from 6:00 pm to 7.30 pm and 7:45 pm to 9:15 pm.

Introduction

This program is designed to develop capacity on analytics for professionals in academia i.e. and suited for early career industry professional who wants to develop basic capacity on analytics. The program would give the participants an edge to perform and implement more effectively the analytics at their respective professions.

Objective

The objective of this online Programme is to provide a framework and necessary data-analytic tools required to process large numeric time-series datasets, inference from which can be used for data-driven decision making. The Programme will focus on examples from various management domains. Apart from learning data-analytic tools, the participants will be exposed to a statistical software, R for data analysis. While knowledge of preparing scripts in R will be disseminated as a part of the overall objective of the Programme, it will not attempt to be at a specialist level.

Why Should You Attend?

Participants will benefit from this Programme if they encounter some or any of the following situation(s) at work:

• If you are interested and want to equip with data analytic skills for time series data.
• If you are interested in redesigning your ongoing research to make them more useful for business decision-making.

If you want to develop/refresh your understanding about basic statistical acumen, some relevant data analytic tools, and their applications.

Contents

Topics to be covered.

• Introduction to Business Forecasting, Overview of Forecasting Perspective,
• Forecasting for Management Decisions  
• Data Collection and Analysis in Forecasting
• Measuring Forecast Accuracy, Metrics for evaluating model performance
• Decomposition of Time Series data; Moving Averages and Exponential smoothing
• Advanced methods in exponential smoothing
• Forecasting with Simple Regression & Multiple Regression methods
• The Box–Jenkins Method of Forecasting.
• Auto Regressive Models: Forecasting with Autoregressive (AR) Models, Forecasting with Moving Average (MA) Models,
• Autoregressive Integrated Moving Average (ARIMA) Models
• Examples and Use cases

Programme Delivery Mode & Software Requirement:

• Zoom Software: It will be in Device-to-Device (D2D) mode on the Zoom platform. The participants are required to login for attending the sessions from their computer. A stable 4G/Broadband internet connection will be required.
• R Statistical software: Participants will be exposed to a statistical software, R, which can be freely downloadable from https://www.r-project.org.

Who Should Attend?

Highly motivated,

• Academia:
  • Faculty, Research scholars
• Industry:
  • working in industry or pursuing for capacity building in analytics

Certification

The participants will be awarded a Certificate of Participation in the Online Development Program on Analytics.

Registration:

Registration has been closed.

The candidates are requested to register and pay online with all required details. Here’s the registration fees for Indian and foreign participants. The fees once received for the program cannot be transferred to other programs and are nonrefundable.

The final date of registration is September 29^th , 2023.

On receipt of payment of registration fees and duly filled registration form, the participants will receive a confirmation and invitation email by September 30^th , 2023.

Registrations will continue a first come-first-serve basis (based on the above-mentioned steps and fulfilment of criterion) until September 29^th , 2023. or until the maximum intake (as determined by IIMV) is met, whichever happens earlier.

Program Director

Prof. Shivshanker Singh Patel ( e-mail: shivshanker@iimv.ac.in) Ph.D. (IISc Ban- galore)

Chairperson of Inter-disciplinary decision science & analytics lab and faculty member in the Decision Sciences Area in IIMV. At IIMV, he has been teaching courses on Data Science, and Operations Research in the PGP, PGPEx, PGPDGM and doctoral Programme. His research interests lie in the domain of system modelling analysis, forecasting, and optimization, applied to scarce resource management and logistics management. He was associated with corporate while working with Mphasis- NextLabs, Mahindra and Mahindra Automotive Ltd. and Vedanta.