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Data Mining for Business Analytics
Rating: 4.1 out of 5(37 ratings)
1,409 students

Data Mining for Business Analytics

Applications of Machine Learning and Data Mining to Business Analytics
Created byDesi Miteva Nikolova
Last updated 7/2024
English
English [Auto],

What you'll 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.

Course content

2 sections • 8 lectures • 33m total length

  • Welcome to the Course0:28

    Explore how machine learning and data mining apply to business analytics, define the data mining process, and review exploratory data analysis and visualization with star discounts and market basket analysis.

  • Core Ideas in the Data Mining Process for Business Analytics9:02

    Explore what business analytics, business intelligence, data mining, and big data mean and how the data mining process—from data preparation to deployment, including supervised and unsupervised learning—drives data-driven decisions.

  • Quiz 1
  • Basics of Matrices and the EDA Step in the Data Mining Process4:03

    Explore the basics of matrices and the EDA step in data mining, focusing on sampling, data quality, filling missing values, and creating binary dummy variables.

  • Quiz 2
  • Visualizing Complex Data Sets4:27

    Explore big data visualizations through basic charts, multidimensional visuals, heat maps, scatter plot matrices, network graphs, and geographic maps, highlighting outliers, binning, and data transformation.

  • Quiz 3

Requirements

  • Some basic understanding of R programming language.

Description

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.

Who this course is for:

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

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