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

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

Master Basics To Advance Tools For Predictive Analytics On Time Series Data Using Descriptive Statistics Moving Averages Regressions Machine Learning Neural Networks!


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.

Excel is easy use and thus we can understand complex concepts through exercises that are easy to replicate and thus become easy to understand.
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.

Basic knowledge
Basic Knowledge of Statistics
Basic Knowledge of Algebra
Basic Knowledge of Logarithm
Basic Knowledge of Excel

Course Information

Basic Knowledge of Statistics
Basic Knowledge of Algebra
Basic Knowledge of Logarithm
Basic Knowledge of Excel

What will you learn
Predicting using Descriptive Statistics, Moving Averages, Centred Moving Averages, Weighted Moving Averages
Predicting using Linear Regression
Predicting using Exponential Regression
Predicting using Power Regression
Predicting using Logarithmic Regression
Predicting using Polynomial Regression
Using Excel to make Predictions
Using Data Analysis Tool Pak from Excel
Using LINEST(), LOGEST(), GROWTH(), TREND() functions in Excel

Research Scholars
Developers curious about Data Sciences
Learners curious about Predictive Analytics

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Course Specifications

Find various Data Analyst courses including a diploma in data analytics

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Adult education is the non-credential activity of gaining skills and improved education. 

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Online education is electronically supported learning that relies on the Internet for teacher/student interaction. 

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A short course is a learning programme that gives you combined content or specific skills training in a short period of time. Short courses often lean towards the more practical side of things and have less theory than a university course – this gives you a more hands-on experience within your field of interest.

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Course duration is 24 hours.

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