Data Analysis and Forecasting - Practical Application

Course Provider

Centre for Professional and Continuing Education (PaCE@NTU)

Certification

Continuing Education and Training Certificate

Introduction

A significant part of today’s business processes and business decisions are driven by data and data analyses.  Besides the obvious need for high quality data that can be crunched and yield analytics and insights that can be trusted for use in decision making, those dealing with data – practically everyone – will also need the skills to source, wrangle, process, manage and communicate data analytics.

A major part of data analysis is forecasting.  In virtually every decision made in the organization, executives will today consider some kind of forecast.  Sound predictions of demands and trends are no longer luxury items, but a necessity.  Executives at all levels have to deal with issues like coping with seasonality, sudden changes in demand levels, price-cutting manoeuvres of the competition, strikes, and large swings of the economy, and many more.

Forecasting can help deal with these issues and challenges; however, it is important to understand the general principles of forecasting, what it can and cannot do, and which techniques are suited to the various needs of the moment.


Course Availability

  • Date(s): 13 to 15 Sep 2022

    Time: 9:00AM to 5:00PM

    Venue: NTU@one-north campus, Executive Centre (Buona Vista)

    Registration Closing Date: 31 Aug 2022

  • Date(s): 22 to 24 Nov 2022

    Time: 9:00AM to 5:00PM

    Venue: NTU@one-north campus, Executive Centre (Buona Vista)

    Registration Closing Date: 09 Nov 2022

The objective of this course is to raise the baseline knowledge and comprehension of data analysis and forecasting techniques by understanding the general principles and using Microsoft Excel as the main analytic tool.  Upon the completion of this short course, participants will have a good foundation of the:

• Various data analysis tools that will be use in one’s work in any industry.
• General principles of forecasting and how to best use different approach and techniques for different situations.
• General approach to managing the data flow in the organization.
• Basic skills set of a data analyst and its application into one’s daily work.

As data, data analysis and forecasting becomes an essential part of any business or organization, it is important for both individuals and organizations to have these highly portable skills.

Training Approach

Through lectures, case examples and hands-on workout sessions, participants will develop a good understanding or how to apply the learnings from this course in their organization.

Day 1 – The Data Analysis Toolkit

Participants will learn about the more important analytic tools that in used in data analysis and forecasting.  They will learn how to use these tools using Microsoft Excel and will work with real and current financial and other data sets. 

Topics/Area of Focus
• Correlation Analysis
• Trend Analysis
• Qualitative and Quantitative analysis
• Histograms 
• Scatter charts
• Benchmarking techniques
• Exponential smoothing and forecasting approaches
• Regression Analysis
Hands-on Workout Session
• Participants (working in teams) will apply the tools taught using real and current financial data (more than 5,000 data points).
• The teams will be making a simple presentation/discussion on what to use in different scenarios and situations.
 

Day 2 – Forecasting Approaches and Techniques

Participants will learn about three types of forecasting – (1) Qualitative Techniques, (2) Time-Series Analysis, and (3) Causal Models.  Participants will also learn about the differences between the three approaches and techniques, what to apply, its complexities and challenges. 

Topics/Area of Focus
• Qualitative Techniques – using qualitative data (e.g., survey, expert opinions, feedback, etc.) to forecast. It may or may not take the past into consideration and can be useful to establish a starting point when data does not exist.
• Time-Series Analysis – focuses on patterns, pattern changes with a high reliance on historical data.
• Causal Models – the use of relationships between system elements like internal data from different parts of the business or external data about competitors and markets.  Historical data is important and relevant as we seek to find a predictive model to help with forecasting.
 
Day 3 – Application of Data Analysis and Forecasting Techniques

Participants will learn how to approach the use of data analysis and forecasting in the context of the value chain and stages of a product or service life cycle.  In particular, the focus will be on (1) Sales, (2) Marketing, and (3) Product/Service development.
 
Topics/Area of Focus
• Application in Sales and Marketing.
• Participants will work on and present a simple sales and marketing proposal based on a case scenario and data.
• Application in Product/Service Development.
• Participants will work on and present their approach (based on qualitative and quantitative data) to developing a new product or service for the case and data provided.

This course is for: 

• Anyone dealing with data and data analysis, and using forecasting as part of their work.
• Anyone with an interest to raise their baseline understanding of how to use MS Excel for data analysis
• Anyone who wants to build up and incorporate data analyst skills

Pre-Requisites

It would be advantages to have done Math or Math-related subjects in the past.  The key learnings will hit home especially if one has or is working with data.  As we will be using MS Excel a lot, it is recommended that participants bring their notebooks to class with MS Excel version 2016 and above, or Office 365 installed.

Standard Course Fee: S$2,889

Course fee payable after SSG funding, if eligible under various schemes

Cat-A SSG Funded Courses

S$866.70

Enhanced Training Support for SMEs (ETSS)

S$326.70

Mid-Career Enhanced Subsidy (MCES)

S$326.70

  • All fees stated are inclusive of 7% GST
  • NTU/NIE alumni may utilise their $1,600 Alumni Course Credits. Click  here for more information. 
Read more about funding
Francis Tay

Francis Tay

Mr. Francis Tay is the Founder & CEO of NextGen Ventures Pte Ltd. He also also the Director of Singapura Management Pte Ltd. Currently, Francis is a lecturer at Nanyang Technological University (NTU) Wee Kim Wee School for Communications and Information (WKWSCI), teaching Business Intelligence and Information Entrepreneurship in the Master of Knowledge Management course. His courses are also taken up by students reading for their Master of Information Systems, Master of Information Studies and Master of Mass Communications. He has also taught Organizational Leadership and Information Sources and Services in the Master of Information Studies course. He has been teaching at NTU for more than 10 years. Francis has authored three books “Latent Factors”, “Turning Good Ideas into Great Businesses” and “Picking Winners”. The first two books focus on business advantages and financial models and the third book is on market intelligence and building up market and sector models for benchmarking analysis. His market intelligence and benchmarking portal – www.profitstrail.com - was developed as a real-time update for “Picking Winners”. He is concurrently a Director at Singapura Management Pte Ltd, a corporate services company providing bookkeeping, tax and other services to local and foreign SMEs. He founded NextGen Ventures Pte Ltd, an equity investment company that focuses on technology and Internet-related businesses. He has held senior positions in government and government-linked organizations. Francis obtained his Honours degree from NUS, majoring in Economics and Computer Science & Applications, and his Master of Science in Management (Sloan Fellowship) from the London Business School in the UK. Francis served 5 years on the National Youth Council’s Singapore Youth Awards (SYA) Entrepreneurship committee and has provided mentorship to companies and others under various business programmes at universities and start-up accelerators.