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.

Instructor:

Dr. P K Viswanathan
4.44
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Intermediate

Level

3.0 Hrs

Learning hours

61.2K+
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Statistical Methods for Decision Making

3.0 Learning Hours . Intermediate

Skills you’ll Learn

About this 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!

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Course Outline

Sampling

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. 

 

 

Normal Distribution

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.

Hypothesis Testing

We shall understand how to assume characteristics of a population and later understand Null and Alternative hypotheses concepts in this section.

Type 1 and Type 2 errors

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.

Types of hypothesis tests

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.

Confidence Intervals

At the beginning of this section, we shall understand what confidence intervals are and then continue with learning to represent “reject null hypothesis”, “fail to reject the null hypothesis”, and tailing depending upon the alternate hypothesis.

Examples of Hypothesis testing

We shall understand working with hypothesis testing by solving various examples, analyzing and representing them in this section.

Chi-Square test

This section begins with defining what the Chi-Square test is and then continues with briefing when it is used. We shall also understand the properties associated with the Chi-Square test later in this section.

ANOVA

Analysis of Variance explains how not the mean of all the population is equal. This section shall help us discover the mean that is unequal amongst the given population and work with that data to derive desired inferences.

Our course instructor

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Dr. P K Viswanathan

Professor, Analytics & Operations

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120.6K+ Learners
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5 Courses

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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What our learners say about the course

Find out how our platform helped our learners to upskill in their career.

4.44
Course Rating
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Ratings & Reviews of this Course

Reviewer Profile

5.0

Engaging Content, Practical Applications, and Insightful Discussions
I appreciate the course's engaging content, practical applications, and insightful discussions. It offers real-world examples and interactive activities that enhance understanding. The well-structured lessons and supportive community make learning enjoyable and effective.
Reviewer Profile

5.0

I Learned Real-Life Statistical Business Applications from This Amazing Course
The best part of this course is the real-life examples from a business perspective. These examples made me learn the critical statistical methods easily. Loved the instructor's teaching style.
Reviewer Profile

5.0

Teaching Techniques by the Professor
The teaching technique is very nice and impactful. I understand very well and was able to pass the quiz test with good marks. So, thank you.
Reviewer Profile

4.0

I Enjoyed the Interactive Discussions and Practical Applications of the Concepts
What I liked most was how the course encouraged critical thinking and real-world problem-solving. The diverse perspectives from fellow participants enriched my understanding, and the hands-on activities made the material more engaging. Overall, it was a valuable learning experience that deepened my knowledge and skills in the subject.

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Statistical Methods for Decision Making

3.0 Learning Hours . Intermediate

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.

What are Statistical Methods for Decision Making used for?

Statistical methods involve hypothesis testing, single variable linear regression, and multiple regression methods to infer any decision. Any of these techniques can be used to suit the best for a given problem statement, and each of these methods adds unique functionality depending upon the need in the problem statement. These techniques can be used to make wise decisions to reduce rework and the errors that follow.

What jobs demand that you learn Statistical Methods for Decision Making?

Top profiles like Data Scientists, Data Analysts, Business Analysts, and Machine Learning Engineers are in massive demand, requiring you to learn Statistical Methods for Decision-Making concepts.

Will I get a certificate after completing this Statistical Methods for Decision Making course?

Yes, you will get a certificate of completion after completing all the modules and cracking the assessment. The assessment tests your knowledge of the subject and badges your skills.

What knowledge and skills will I gain upon completing this course?

The free Statistical Methods for Decision Making course explains the essential concepts in detail, which include sampling, normal distributions, hypothesis testing, and its different types, type 1 and type 2 errors, chi-square testing, and ANOVA concepts. With this knowledge, you can basket decision-making skills and apply statistical methods to solve business analytics and data science problems.

How much does this Statistical Methods for Decision Making course cost?

The Statistical Methods for Decision Making is a free course, and you can enroll and learn it online at your convenience.

Is there a limit on how many times I can take this Statistical Methods for Decision Making course?

Once you enroll in the Statistical Methods for Decision Making course, you have lifetime access to it. So, you can log in anytime and learn it for free online.

Can I sign up for multiple courses from Great Learning Academy at the same time?

Yes, you can enroll in as many courses as you want from Great Learning Academy. There is no limit to the number of courses you can enroll in at once, but since the courses offered by Great Learning Academy are free, we suggest you learn one by one to get the best out of the subject.

Why choose Great Learning Academy for this Statistical Methods for Decision Making course?

Great Learning is a global ed-tech platform dedicated to the development of competent professionals. Great Learning Academy is an initiative by Great Learning that offers in-demand free online courses to help people advance in their jobs. More than 4 million learners from 140 countries have benefited from Great Learning Academy's free online courses with certificates. It is a one-stop place for all of a learner's goals.

Great Learning Academy provides Statistical Methods for Decision Making course for free online. The course is not only self-paced but also helps you comprehend several topics that fall under the subject with solved problems and demonstrated examples. The course is carefully designed, keeping in mind to cater to both beginners and professionals, and is delivered by industry experts.

Who is eligible to take this Statistical Methods for Decision Making course?

Anybody with basic knowledge of computer science and interested in learning Data Science can take up the course. But it would help you if you have a good understanding of Data Science, Business Analytics and Machine Learning concepts.

What are the steps to enroll in this course?

Enrolling in any of the Great Learning Academy’s courses is just a one step process. Sign-up for the course you are interested in learning through your E-mail ID and start learning them for free online.

Will I receive any career services with Statistical Methods for Decision Making program?

Statistical Methods for Decision Making course is designed to give you a base knowledge of Data Science and Business Analytics. You will have to learn other concepts in the subject before you are ready to be a professional in the field. You can enroll in the Data Science Post Graduate program to avail the career services from industry experts.

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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.
 

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