Data Mining with RapidMiner

Data Mining with RapidMiner

Why learn Data Analysis and Data Science?

According to SAS, the five reasons are

1. Gain problem solving skills

The ability to think analytically and approach problems in the right way is a skill that is very useful in the professional world and everyday life.

2. High demand

Data Analysts and Data Scientists are valuable. With a looming skill shortage as more and more businesses and sectors work on data, the value is going to increase.

3. Analytics is everywhere

Data is everywhere. All company has data and need to get insights from the data. Many organizations want to capitalize on data to improve their processes. It's a hugely exciting time to start a career in analytics.

4. It's only becoming more important

With the abundance of data available for all of us today, the opportunity to find and get insights from data for companies to make decisions has never been greater. The value of data analysts will go up, creating even better job opportunities.

5. A range of related skills

The great thing about being an analyst is that the field encompasses many fields such as computer science, business, and maths.  Data analysts and Data Scientists also need to know how to communicate complex information to those without expertise.

The Internet of Things is Data Science + Engineering. By learning data science, you can also go into the Internet of Things and Smart Cities.

This is the bite-size course to learn Data Mining using RapidmIner. This course uses CRISP-DM data mining process.

You will learn RapidMiner to do data understanding, data preparation, modeling, and Evaluation. You will be able to train your own prediction models with Naive Bayes, decision tree, knn, neural network, and linear regression, and evaluate your models very soon after learning the course.

You can take the course as following and you can take an exam at EMHAcademy to get SVBook Advance Certificate in Data Science using DSTK, Excel, and RapidMiner:

- Introduction to Data and Text Mining using DSTK 3

- Data Mining with RapidMiner

- Learn Microsoft Excel Basics Fast

- Learn Data analysis using Microsoft Excel Basics Fast.

Content

1. Getting Started

2. Getting Started 2

3. Data Mining Process

4. Download Data Set

5. Read CSV

6. Data Understanding: Statistics

7. Data Understanding: Scatterplot

8. Data Understanding: Line

9. Data Understanding: Bar

10. Data Understanding: Histogram

11. Data Understanding: BoxPLot

12. Data Understanding: Pie

13. Data Understanding: Scatterplot Matrix

14. Data Preparation: Normalization

15. Data Preparation: Replace Missing Values

16. Data Preparation: Remove Duplicates

17. Data Preparation: Detect Outlier

18. Modeling: Simple Linear Regression

19. Modeling: Simple Linear Regression using RapidMiner

20. Modeling: KMeans CLustering

21. Modeling: KMeans Clustering using RapidmIner

22. Modeling: Agglomeration CLustering

23. Modeling: Agglomeration Clustering using RapidmIner

24. Modeling: Decision Tree ID3 Algorithm

25. Modeling: Decision Tree ID3 Algorithm using RapdimIner

26. Modeling: Decision Tree ID3 Algorithm using RapidMiner

27. Evaluation: Decision Tree ID3 Algorithm using RapidmIner

28. Modeling: KNN Classification

29. Modeling: KNN CLassification using RapidmIner

30. Evaluation: KNN Classification using RapidmIner

31. Modeling Naive Bayes Classification

32. Modeling: Naive Bayes Classification using RapidmIner

33. Evaluation: Naive Bayes Classification using RapidMIner

34. Modeling: Neural Network Classification

35. Modeling: Neural Network Classification using RapidmIner

36. Evaluation: Neural Network Classification using RapidmIner

37. What Algorithm to USe?

38. Model Evaluation

39. k fold cross-validation using RapdimIner

Data Mining with RapidMiner

Url: View Details

What you will learn

• Data Mining using RapidMIner

Rating: 2.9

Level: Beginner Level

Duration: 1.5 hours

Instructor: Goh Ming Hui