Making Numerical Predictions for Time Series Data - Part 1/3

Using Excel to make Numerical Predictions on Time Series data

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Predictive analytics is the branch of the advanced analytics which is used to make predictions about unknown future events. Predictive analytics uses many techniques from data mining, statistics, modelling, machine learning, and artificial intelligence to analyse current data to make predictions about future.

One class of Predictive Analytics is to make prediction on Time Series Data. Studying historical data, collected over a period of time, can help in building models using which future can be predicted. For example, from historical data on Temperatures in a City, we can make decent predictions of what the Temperature could be in a future date. Or for that matter, from data collected over a reasonably long period of time regarding various life style aspects of a Diabetic patient, we can predict what should be the volume of Insulin to inject on a given date in future. One example to consider from the Business world could be to predict the Volume of In-Roamers in a Telecom Network in any given period of time in the future from the historical details of In-Roamers in the Network.

The applications are just innumerable as these are applicable in every sphere of business and life.

In this course, we go through various aspects of building Predictive Analytics Models. We start with simple techniques and gradually study very advanced and contemporary techniques. We cover using Descriptive Statistics, Moving Averages, Regressions, Machine Learning and Neural Networks.

This course is a series of 3 parts.

  • In Part 1, we use Excel to make Numerical Predictions from Time Series Data.

We start by using Excel for 2 reasons.

  1. Excel is easy use and thus we can understand complex concepts through exercises that are easy to replicate and thus become easy to understand.
  2. Excel is expected to be available with everyone taking this course.
  • In Part 2, we use R Programming to make Numerical Predictions from Time Series Data.
  • In Part 3, we use Python Programming to make Numerical Predictions from Time Series Data.

The course uses simple data sets to explain the concepts and the theory aspects. As we go through the various techniques, we compare the various techniques. We also understand the circumstances where a particular technique should be applied. We will also use some publicly available data sets to apply the techniques that we will discuss in the course.

From time to time, we will add bonus videos of our real time work on industrial data on which we will apply the Predictive Analytics techniques to create Models for making predictions.


Your Instructor


PARTHA MAJUMDAR
PARTHA MAJUMDAR

Partha started his career in 1989 as a programmer. In his first assignment, he was involved in development of a Cricket Tournament management system as a part of the team from Centre for Development of Telematics (C-DOT) requested by the Prime Minister of India, Mr. Rajiv Gandhi. Since then Partha has developed Tea Garden automation solution, Hospital Management solution, Travel Management solution, Manufacturing Resource Planning (MRP II) solution, Insurance Management solution and Tax automation solution (for Government of Thailand).

Partha got involved in Telecom solution with project from Total Access Communications, Bangkok in 1996. Partha developed the completed solution architecture and designed & developed the complete infrastructure services and primitives on top of which the end-to-end Customer Care and Billing solution was developed between 1996-1998.

Partha has worked for companies including Amdocs, Portal, Siemens and has developed key components of their solutions. For Siemens, Partha developed the complete BSS suite.

Partha worked with Mobily, Saudi Arabia as the Enterprise Architect and has first-hand of experience of work inside a Telecom Operator.

Partha started his own company, Majumdar Consultancy Pvt Ltd, in 2014. He partnered with a Dubai based businessman to open SI Solutions India Pvt Ltd in 2016. In 2019, he joined Tools and Solutions, Saudi Arabia as Director - Professional Services to establish the Professional Services business.

Partha has recently developed a Remote Control, which can be controlled from a Web Site. The Remote Control can in turn control any device. The Remote Control to be controlled needs having Infra-Red sensing capabilities. The Remote Control is controlled through DragonBoard 410C through an Android Program.

Partha has been working on fine tuning the algorithm for an Access Control System through Face Recognition. The program has been developed using Convolutional Neural Network (CNN).

Partha has also developed a software which tries to predict the Stock Market. The solution has been developed using Recurrent Neural Network (RNN). The solution presently predict with an accuracy of 77%.


Course Curriculum


  Welcome to the Course
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  Introduction to Time Series Data
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  Power Regression
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  Logarithmic Regression
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  Non-Linear Regression
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  Course Closing
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Frequently Asked Questions


When does the course start and finish?
The course starts now and never ends! It is a completely self-paced online course - you decide when you start and when you finish.
How long do I have access to the course?
How does lifetime access sound? After enrolling, you have unlimited access to this course for as long as you like - across any and all devices you own.
What if I am unhappy with the course?
We would never want you to be unhappy! If you are unsatisfied with your purchase, contact us in the first 30 days and we will give you a full refund.
Congratulations on having completed the course - Making Numerical Predictions for Time Series Data - Part 1 of 3!!!

I look forward to each of you contributing to the domain of making Numerical Predictions which all of us can benefit from.


Good Luck!!!


Happy Learning!!!

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