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Statistical Methods for Decision Making
Boost your knowledge through this Statistical Methods for Decision Making course and embark on your Data Science career. Grasp the concepts of sampling, distribution hypothesis testing, error types, and ANOVA techniques for free.
What you learn in Statistical Methods for Decision Making ?
About this Free Certificate Course
The Statistical Methods for Decision Making course aims to give you the knowledge to understand sampling, normal distributions, hypothesis testing, and its different types, type 1 and type 2 errors, chi-square testing, and ANOVA. The course will fix the concepts in your mind through demonstrated examples and solved samples for the aforesaid concepts. You will have to take up the quiz/assessment at the end of the course to test your skills and evaluate your gains to secure the certificate.
Upon completing this free, self-paced, intermediate's guide to Statistical Methods for Decision Making, you can embark on your Data Science and Business Analytics career with a professional Post Graduate certificate and learn various concepts with millions of aspirants worldwide!
Course Outline
You will learn and understand edge detection and sharpening in the sampling technique of Computer Vision with example to represent an image in this section.
This section explains the central theorem and then later looks at how samples and data are distributed. We shall also look at how supervised learning classifies the data later in this section, after which we will understand future predictions for EV sales.
We shall understand how to assume characteristics of a population and later understand Null and Alternative hypotheses concepts in this section.
This section discusses errors rejecting and not rejecting null hypotheses at the right instances. We shall look into the causes of why the actions are not performed right and also understand how to bring them back in the flow.
We shall understand single/dual/multiple samples, one/two-tailed, and mean-variance, or proportion tests in this section. This section picks up multiple examples stating null and alternative hypotheses to understand the concept better.
Our course instructor
Dr. P K Viswanathan
Professor, Analytics & Operations
Dr. P K Viswanathan, currently serves as a professor of analytics at Great Lakes Institute of Management. He teaches subjects such as business statistics, operations research, business analytics, predictive analytics, ML analytics, spreadsheet modeling and others. In the industrial tenure spanning over 15 years, he has held senior management positions in Ballarpur Industries (BILT) of the Thapar Group and the JK Industries of the JK Organisation. Apart from executing corporate consultancy assignments, Dr. PK Viswanathan has also designed and conducted training programs for many leading organizations in India. He has degrees in MSc (Madras), MBA (FMS, Delhi), MS (Manitoba, Canada), PHD (Madras).
Noteworthy achievements:
- Ranked 12th in the "20 Most Prominent Analytics & Data Science Academicians In India: 2018".
- Current Academic Position: Professor of Analytics, Great Lakes Institute of Management.
- Prominent Credentials: He has authored a total of four books, three of which are on Business Statistics and one on Marketing Research published by the British Open University Business School, UK.
- Research Interest: Analytics, ML, AI.
- Patents: He has original research publications exclusively on analytics where he has developed modeling and demonstrated their decision support capabilities. These are: Modelling Credit Default in Microfinance — An Indian Case Study, PK Viswanathan, SK Shanthi, Modelling Asset Allocation and Liability Composition for Indian Banks.
- Teaching Experience: He has been teaching analytics for more than two decades but has been into active and intense teaching since analytics started witnessing a meteoric growth with the advent of R and Python.
- Ph.D. in the application of Operations Research from Madras University.
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2.0 Hours
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Frequently Asked Questions
What are the prerequisites to learning these Statistical Methods for Decision-Making courses?
This is an intermediate-level course. So to start learning Statistical Methods for Decision Making, you will need to have a basic understanding of what Data Science, Business Analytics and Machine Learning are and why they are used. You will also need to have a good grasp of probability and statistics concepts.
How long does it take to complete this free Statistical Methods for Decision Making course?
It is a 2-hour course but is self-paced however. Once you enroll, you can take your own time to complete the course.
Will I have lifetime access to the free course?
Yes, once you enroll in the course, you will have lifetime access. You can log in and learn whenever you can.
What are my next learning options after this Statistical Methods for Decision Making course?
Once you have a good understanding of the commanding systems to make statistical decisions, you can learn different algorithms to model and train the system to work with no or very less human intervention. With this knowledge, you can also enroll in any of the well-reputed Data Science courses and gain a professional badge for the subject.
Why learn Statistical Methods for Decision Making?
Statistical methods involve procedural techniques to work with data science and machine learning problems, application development, and business analytics purposes. It also reduces the amount of time spent on commanding the system and reduces the risk of errors.
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Statistical Methods for Decision Making Course
In today's data-driven world, it's important to have a strong understanding of statistical methods to make informed decisions. This course on Statistical Methods for Decision Making provides a comprehensive overview of the key statistical concepts and techniques used for decision making. The course is designed for individuals with varying levels of statistical experience and will equip participants with the skills to apply statistical methods in real-world situations.
How this course helps:
This course is designed to provide participants with a solid foundation in statistical methods and their applications. Upon completion of this course, participants will have a thorough understanding of the statistical concepts and techniques used in decision making, including hypothesis testing, type I and type II error, chi-square test, ANOVA and more.
Course includes:
Hypothesis Testing: This section covers the basics of hypothesis testing, including the null and alternative hypotheses, p-value, and hypothesis testing procedures.
Type I and Type II Error: This section covers the concepts of type I and type II error, and how they relate to hypothesis testing. Participants will learn how to calculate and interpret these errors.
Chi-Square Test: This section focuses on the chi-square test, a commonly used statistical method for testing hypotheses about categorical data.
ANOVA (Analysis of Variance): This section covers ANOVA, a statistical method used for testing hypotheses about the means of two or more groups. Participants will learn how to perform ANOVA and interpret its results.
In conclusion, this course on Statistical Methods for Decision Making provides a comprehensive introduction to the key statistical concepts and techniques used for decision making. Whether you are a beginner or have some experience with statistics, this course will help you develop your skills and prepare you for a successful career in data science.
