Data Mining with RapidMiner
What you'll learn
- Data Mining using RapidMIner
- Basic COmputer Knowledge
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.
Getting Started 2
Data Mining Process
Download Data Set
Data Understanding: Statistics
Data Understanding: Scatterplot
Data Understanding: Line
Data Understanding: Bar
Data Understanding: Histogram
Data Understanding: BoxPLot
Data Understanding: Pie
Data Understanding: Scatterplot Matrix
Data Preparation: Normalization
Data Preparation: Replace Missing Values
Data Preparation: Remove Duplicates
Data Preparation: Detect Outlier
Modeling: Simple Linear Regression
Modeling: Simple Linear Regression using RapidMiner
Modeling: KMeans CLustering
Modeling: KMeans Clustering using RapidmIner
Modeling: Agglomeration CLustering
Modeling: Agglomeration Clustering using RapidmIner
Modeling: Decision Tree ID3 Algorithm
Modeling: Decision Tree ID3 Algorithm using RapdimIner
Modeling: Decision Tree ID3 Algorithm using RapidMiner
Evaluation: Decision Tree ID3 Algorithm using RapidmIner
Modeling: KNN Classification
Modeling: KNN CLassification using RapidmIner
Evaluation: KNN Classification using RapidmIner
Modeling Naive Bayes Classification
Modeling: Naive Bayes Classification using RapidmIner
Evaluation: Naive Bayes Classification using RapidMIner
Modeling: Neural Network Classification
Modeling: Neural Network Classification using RapidmIner
Evaluation: Neural Network Classification using RapidmIner
What Algorithm to USe?
k fold cross-validation using RapdimIner
Who this course is for:
- Beginner Data Scientist or Analyst interested in RapidMiner
SVBook Pte. Ltd. started in April 2016. SVBook Pte. Ltd. offers affordable, easy to read Data Mining, Text Mining, Data Analytics, Programming books for professional and lifelong learning. SVBook Pte. Ltd. offers courses at EMHAcademy. SVBook Pte. Ltd. developed softwares over the time, and uploaded them at DSTK_Tech. SVBook Pte. Ltd. courses, books and softwares assists people to step into new fields of Data Science, Internet of Things, Smart Cities and etc. before going into more advanced courses at Coursera and EdX, which are at University Level.