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Data mining is the way of examining different types of large databases to generate new information. Knowing to mine data can benefit your organization in gathering essential data to improve the performance of its operation.

Taking this [course_title] will help you utilize database available on the internet. You will be provided with methods on figuring the algorithms for data mining for you to easily gather the necessary data your organization needs.

Assessment

This course does not involve any written exams. Students need to answer 5 assignment questions to complete the course, the answers will be in the form of written work in pdf or word. Students can write the answers in their own time. Each answer needs to be 200 words (1 Page). Once the answers are submitted, the tutor will check and assess the work.

Certification

Edukite courses are free to study. To successfully complete a course you must submit all the assignment of the course as part of the assessment. Upon successful completion of a course, you can choose to make your achievement formal by obtaining your Certificate at a cost of £49.

Having an Official Edukite Certification is a great way to celebrate and share your success. You can:

  • Add the certificate to your CV or resume and brighten up your career
  • Show it to prove your success

 

 

Course Credit: MIT

Course Curriculum

Data Mining Overview – Part 1 01:15:00
Data Mining Overview – Part 2 00:30:00
Classification and Bayes Rule, Naïve Bayes 01:15:00
Classification Trees 01:30:00
Discriminant Analysis Example 2 Fisher’s Iris data 01:00:00
Logistic Regression Case – Part 1 01:30:00
Logistic Regression Case – Part 2 00:45:00
Neural Nets 01:00:00
Multiple Regression Review 01:30:00
Multiple Linear Regression in Data Mining 01:15:00
Comparison of Data Mining Techniques 00:10:00
K-Means Clustering, Hierarchical Clustering 00:45:00
Principal Components 00:30:00
Association Rules (Market Basket Analysis) 00:45:00
Assessment
Submit Your Assignment 00:00:00
Certification 00:00:00

Course Reviews

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