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Data Mining for Business Analytics

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Data Mining for Business Analytics, available at Free, with 8 lectures, 6 quizzes, and has 730 subscribers.

You will learn about Understand in-depth Data Mining concepts for Business. Understand Big Data concepts, Visualizations and reporting for Business Analytics. Gain hands on Knowledge with Machine Learning Methods Learn how Machine Learning is Applied in Business to Derive Insights and Present Results. This course is ideal for individuals who are Business students or professionals that want to learn or apply business analytics in their practice. It is particularly useful for Business students or professionals that want to learn or apply business analytics in their practice.

Enroll now: Data Mining for Business Analytics

Summary

Title: Data Mining for Business Analytics

Price: Free

Number of Lectures: 8

Number of Quizzes: 6

Number of Published Lectures: 8

Number of Published Quizzes: 6

Number of Curriculum Items: 14

Number of Published Curriculum Objects: 14

Original Price: Free

Quality Status: approved

Status: Live

What You Will Learn

  • Understand in-depth Data Mining concepts for Business.
  • Understand Big Data concepts, Visualizations and reporting for Business Analytics.
  • Gain hands on Knowledge with Machine Learning Methods
  • Learn how Machine Learning is Applied in Business to Derive Insights and Present Results.

Who Should Attend

  • Business students or professionals that want to learn or apply business analytics in their practice.

Target Audiences

  • Business students or professionals that want to learn or apply business analytics in their practice.

The course content is dedicated to the applications of machine learning and data mining to business analysis. The intent is to give a high-level overview of the potential of machine learning and data mining in different areas and, more specifically, the area of business analytics. The main sections are:

1. Core ideas of the data mining process – this section covers the definitions of the concepts of data mining, big data, business analytics and business intelligence.

2. Basics of exploratory data analysis – this section covers EDA with R.

3. Visualizing complex data sets – in this part the main visualizations used in business analytics and advanced data analysis are discussed in detail.

4. Housing valuation with multiple linear regression – this section covers the basic steps of the data mining process using housing valuation example.

5. Store discounts with random forest and natural language processing – in this section, the topic of machine learning methods, such as, decision trees and random forest, is examined. An example of store discounts is given to illustrate the application of natural language processing and random forest to text-rich data.

6. Market basket analysis with unsupervised machine learning – in this section unsupervised machine learning methods, such as, Apriori and associative rules are examined in detail. An example with market basket analysis is given to illustrate the application of Apriori and associative rules.

Course Curriculum

Chapter 1: Core Ideas in Data Mining for Business, Exploratory Analysis and Visualizations

Lecture 1: Welcome to the Course

Lecture 2: Core Ideas in the Data Mining Process for Business Analytics

Lecture 3: Basics of Matrices and the EDA Step in the Data Mining Process

Lecture 4: Visualizing Complex Data Sets

Chapter 2: Machine Learning Models for Business Analysis

Lecture 1: Housing Valuation – Fitting and Evaluating a Machine Learning Model

Lecture 2: Analyzing Store Discounts – Trees and Forests with Natural Language Toolkit

Lecture 3: Market Basket Analysis with Apriori and Associative Rules

Lecture 4: Conclusion

Instructors

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Frequently Asked Questions


How long do I have access to the course materials?


You can view and review the lecture materials indefinitely, like an on-demand channel.


Can I take my courses with me wherever I go?


Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!

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