Overview
Welcome to the course “Predictive Analytics”!
Predictive Analytics is emerging as a competitive strategy across many sectors of business. It searches for patterns found in historical and transactional data to understand the factors associated with business/organizational problems. At an abstract level, "Predictive analytics can be seen as a set of tools with capability to predict future".
Professor U. Dinesh Kumar.
Hi,
Welcome to the course, "Predictive Analytics".
I will be discussing the course contents and the learning objectives of this course.
In the first section, I will be discussing "Introduction to Business Analytics."
The learning objectives of the first session are to understand business analytics, it's importance and how different companies are using business analytics to solve real life problems.
During second and third sections of the course, I will be discussing simple and multiple linear regression.
I will be using a case let, "Die Another Day Hospital", in which the hospital is interested in predicting the treatment cost of a patient at the time of admission.
During sections four and five, I will be discussing classifications problems and how techniques such as logistic regression, decision trees, and Naive Bayes algorithm.
In section four, I will be using Challenger Crash Data and German Credit Data from University of California, Irvine Machine Learning Database to demonstrate the use of logistic regression to solve classification problems.
In section five, I will be using decision trees to develop business rules that can be used in classification problem using German Credit Data.
I will be also discussing Naive Bayes algorithm to classify unstructured data using social media comments from a Bollywood movie.
In section six, I will be discussing forecasting techniques such as, moving average, exponential smoothing, and autoregressive models.
In this section, I will be using data on a demand for continental breakfast at "Die Another Day Hospital".
To understand the concepts better, we have created two fictitious characters, Dude and Nerd.
They will be asking questions during the lecture.
Dude and Nerd will also be part of the discussion forum.
I hope you will enjoy this course.
Also, I would like to advise you to brush up concepts such as, normal distribution and hypothesis testing, before starting this course.
Objective
At the end of this course, you will be able to:
Understand the emergence of predictive analytics as a competitive strategy.
Analyze data using statistical techniques.
Understand relationships between the underlying business processes of an organization.
Develop predictive models using Microsoft Excel / Statistical Package for the Social Sciences (SPSS) / R / Statistical Analysis System (SAS).
Interpret the model output to make business decisions.
Predictive Analytics Teaser:
Librarian: Hi
Student: Hello
Librarian: Are you looking for Econometrics?
Student: Yes
Librarian: Author?
Student: It's Damodar Modi
Librarian: There is no such author
You mean to say Damodhar Gujarathi?
Student: Yeah, that one!
Librarian: In that case, you have to check column 2 rack 4 and one copy left.
Student: Wow, great!
Thanks.
(After collecting all the books the student approaches the front desk)
Librarian: May I help you?
Student: Wow! You again! Well, I would like to borrow these books
Librarian: Your username
Student: Uh, It's Vigneshsr.
Librarian: No
Student: Well, it's srv.
Librarian: Is it chotabheem?
Librarian: Fine, Registration Number?
Student: It's BI89.
Librarian: Your username is vignesh89.
Student: Wow, I am in!
Librarian: Based on your limit, you are not supposed to take more than 3 books.
Student: Oh is it?
Then I will go for
Librarian: I suggest you take a book by professor Dinesh because you are likely to read that book.
Student: Well, Certainly.
Librarian: I suggest you take a book on econometrics, because your assignment deadline is nearing.
Student: Oh, is it?
Thanks for reminding.
Librarian: Fine you got two books left - one on music and one on stats.
Student: Ofcourse, I will go for music.
Librarian: Good, but i suggest that you spend more on academics because if you continue to spend more on extracurricular you are likely to fail.
Student: Oh, is it?
Then I go for stats.
Librarian: Can you confirm these three.
Pay Rs 38.
Student: Why?
Librarian: Because you submitted the book after due date.
Librarian: Thank You and Congratulations!
Student: For what?
Librarian: Your results are out and you have cleared.
Student: Wow, that's great news!
How does he know everything about me?
Welcome to Predictive Analytics.
Welcome to the course “Predictive Analytics”!
Predictive Analytics is emerging as a competitive strategy across many sectors of business. It searches for patterns found in historical and transactional data to understand the factors associated with business/organizational problems. At an abstract level, "Predictive analytics can be seen as a set of tools with capability to predict future".
Professor U. Dinesh Kumar.
Hi,
Welcome to the course, "Predictive Analytics".
I will be discussing the course contents and the learning objectives of this course.
In the first section, I will be discussing "Introduction to Business Analytics."
The learning objectives of the first session are to understand business analytics, it's importance and how different companies are using business analytics to solve real life problems.
During second and third sections of the course, I will be discussing simple and multiple linear regression.
I will be using a case let, "Die Another Day Hospital", in which the hospital is interested in predicting the treatment cost of a patient at the time of admission.
During sections four and five, I will be discussing classifications problems and how techniques such as logistic regression, decision trees, and Naive Bayes algorithm.
In section four, I will be using Challenger Crash Data and German Credit Data from University of California, Irvine Machine Learning Database to demonstrate the use of logistic regression to solve classification problems.
In section five, I will be using decision trees to develop business rules that can be used in classification problem using German Credit Data.
I will be also discussing Naive Bayes algorithm to classify unstructured data using social media comments from a Bollywood movie.
In section six, I will be discussing forecasting techniques such as, moving average, exponential smoothing, and autoregressive models.
In this section, I will be using data on a demand for continental breakfast at "Die Another Day Hospital".
To understand the concepts better, we have created two fictitious characters, Dude and Nerd.
They will be asking questions during the lecture.
Dude and Nerd will also be part of the discussion forum.
I hope you will enjoy this course.
Also, I would like to advise you to brush up concepts such as, normal distribution and hypothesis testing, before starting this course.
Objective
At the end of this course, you will be able to:
Understand the emergence of predictive analytics as a competitive strategy.
Analyze data using statistical techniques.
Understand relationships between the underlying business processes of an organization.
Develop predictive models using Microsoft Excel / Statistical Package for the Social Sciences (SPSS) / R / Statistical Analysis System (SAS).
Interpret the model output to make business decisions.
Predictive Analytics Teaser:
Librarian: Hi
Student: Hello
Librarian: Are you looking for Econometrics?
Student: Yes
Librarian: Author?
Student: It's Damodar Modi
Librarian: There is no such author
You mean to say Damodhar Gujarathi?
Student: Yeah, that one!
Librarian: In that case, you have to check column 2 rack 4 and one copy left.
Student: Wow, great!
Thanks.
(After collecting all the books the student approaches the front desk)
Librarian: May I help you?
Student: Wow! You again! Well, I would like to borrow these books
Librarian: Your username
Student: Uh, It's Vigneshsr.
Librarian: No
Student: Well, it's srv.
Librarian: Is it chotabheem?
Librarian: Fine, Registration Number?
Student: It's BI89.
Librarian: Your username is vignesh89.
Student: Wow, I am in!
Librarian: Based on your limit, you are not supposed to take more than 3 books.
Student: Oh is it?
Then I will go for
Librarian: I suggest you take a book by professor Dinesh because you are likely to read that book.
Student: Well, Certainly.
Librarian: I suggest you take a book on econometrics, because your assignment deadline is nearing.
Student: Oh, is it?
Thanks for reminding.
Librarian: Fine you got two books left - one on music and one on stats.
Student: Ofcourse, I will go for music.
Librarian: Good, but i suggest that you spend more on academics because if you continue to spend more on extracurricular you are likely to fail.
Student: Oh, is it?
Then I go for stats.
Librarian: Can you confirm these three.
Pay Rs 38.
Student: Why?
Librarian: Because you submitted the book after due date.
Librarian: Thank You and Congratulations!
Student: For what?
Librarian: Your results are out and you have cleared.
Student: Wow, that's great news!
How does he know everything about me?
Welcome to Predictive Analytics.
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