Data Mining with RapidMiner

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Price: 199.99$

Why learn Data Analysis and Data Science?According to SAS, the five reasons are1. 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 Rapidm Iner. This course uses CRISP-DM data mining process. You will learn Rapid Miner 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 Rapid Miner: – Introduction to Data and Text Mining using DSTK 3- Data Mining with Rapid Miner- Learn Microsoft Excel Basics Fast- Learn Data analysis using Microsoft Excel Basics Fast. Content Getting Started Getting Started 2Data Mining Process Download Data Set Read CSVData Understanding: Statistics Data Understanding: Scatterplot Data Understanding: Line Data Understanding: Bar Data Understanding: Histogram Data Understanding: Box PLot 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 Rapid Miner Modeling: KMeans CLustering Modeling: KMeans Clustering using Rapidm Iner Modeling: Agglomeration CLustering Modeling: Agglomeration Clustering using Rapidm Iner Modeling: Decision Tree ID3 Algorithm Modeling: Decision Tree ID3 Algorithm using Rapdim Iner Modeling: Decision Tree ID3 Algorithm using Rapid Miner Evaluation: Decision Tree ID3 Algorithm using Rapidm Iner Modeling: KNN Classification Modeling: KNN CLassification using Rapidm Iner Evaluation: KNN Classification using Rapidm Iner Modeling Naive Bayes Classification Modeling: Naive Bayes Classification using Rapidm Iner Evaluation: Naive Bayes Classification using Rapid MIner Modeling: Neural Network Classification Modeling: Neural Network Classification using Rapidm Iner Evaluation: Neural Network Classification using Rapidm Iner What Algorithm to USe?Model Evaluationk fold cross-validation using Rapdim Iner