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    Data Mining with R: Go from Beginner to Advanced!

    BY
    Udemy

    Discover how to use R software for data analysis, visualization, and a variety of popular data mining techniques.

    Brochure Enquire
    Mode

    Online

    Fees

    ₹ 3499

    Quick Facts

    particular details
    Medium of instructions English
    Mode of learning Self study
    Mode of Delivery Video and Text Based

    Course overview

    Companies mine data for insights. Businesses can better understand their customers, boost sales, and cut costs by using software to find patterns in large data sets. Data mining requires data collection, storage, and processing. Data is collected and loaded into data warehouses, next, they store and manage data on in-house or cloud servers. Management, business analysts, and IT professionals organize the data. Then, the application software sorts the data based on the user's results, and the end user shares the data in a graph or table. Data Mining with R: Go from Beginner to Advanced! certification is made available by Udemy to candidates who want to learn R software can be used to analyze data, show how it looks, and do numerous data mining tasks.

    Data Mining with R: Go from Beginner to Advanced! online training Includes 12 hours of video,17 downloadable resources, and a digital certificate upon course completion.

    Data Mining with R: Go from Beginner to Advanced! online classes consist of data types and structures in R,  data and file input and output,  visualizing data, decision trees, linear modeling regression, regression and GLM, agglomerative hierarchical clustering, and cluster analysis

    The highlights

    • Full Lifetime Access
    • 12 Hours of Video
    • 17 Downloadable Resources
    • Access on Mobile and TV
    • Certificate of Completion

    Program offerings

    • Online course
    • Learning resources
    • 30-day money-back guarantee
    • Unlimited access

    Course and certificate fees

    Fees information
    ₹ 3,499
    certificate availability

    Yes

    certificate providing authority

    Udemy

    What you will learn

    Data Mining with R: Go from Beginner to Advanced! certification course, the applicant will gain knowledge about how to use the R programming language for data import and export, data exploration and visualization, and data analysis tasks, including a comprehensive set of data mining operations. The applicant will learn how to effectively use a number of popular, contemporary data mining methods and techniques in demand by industry, such as decision, classification, and regression trees, random forests, linear and logistic regression, and various cluster analysis techniques, as well as how to apply the dozens of "hands-on" cases and examples using real data and R scripts to solve new and unique data analysis and data mining problems

    The syllabus

    Data Types and Structures in R

    • Who should take and what will you get from this course ?
    • Installing R and RStudio
    • Orientation to Data Types and Structures Section
    • Materials for Data Types and Structures
    • Vectors: The Basic Default Data Structure in R
    • Matrices, Lists and Dataframes: Other Important R Data Structures
    • Manipulating Vectors in R
    • Naming Vectors in R
    • Creating Matrices in R
    • Creating Lists in R
    • Creating Lists in R (continued)
    • Creating Dataframes in R

    Data and File Input and Output

    • Orientation to Data and File Input and Output
    • Materials for Data and File Input and Output
    • Reading in Data using scan() Function
    • Reading in Data with scan() Function (continued)
    • Using readline() Function to Prompt User for Input
    • Reading in Files with read.table() and read.csv() Functions
    • Writing R Session Files to Disk (Outputting Data)
    • Data Input and Output Exercise

    Visualizing (Getting to Know) your Data

    • Solution to Data Input and Output Exercise from Section 2 (1 of 2)
    • Solution to Data Input and Output Exercise from Section 2 (2 of 2)
    • Materials for Visualizing your Data Section 3
    • Preprocessing and Visualizing Birth Data
    • Preprocessing and Visualizing Birth Data (part 2)
    • Preprocessing and Visualizing Birth Data (part 3)
    • Visualizing Alumni Donations
    • Visualizing Alumni Donations (part 2)
    • Visualizing Alumni Donations (part 3)
    • Visualizing Alumni Donations (part 4)
    • Visualizing (Getting to Know) your Data Section Exercise

    Decision Trees and Random Forests

    • Solution to Visualizing Virginia Deaths Exercise
    • Introduction to Decision Trees and Random Forests
    • Training Decision Trees with party Package
    • Training Decision Trees with party Package (part 2)
    • Bodyfat Decision Tree example with Package rpart
    • Bodyfat Decision Tree example with Package rpart (part 2)
    • Bagging and Random Forests with Section Exercise

    Linear Modeling (Regression) and Generalized Linear Modeling (GLMs)

    • Begin Decision Tree and Random Forests Exercise Solution
    • Random Forests Exercise Bagging Segment Solution
    • Random Forests Exercise Solution (part 3)
    • Materials for Regression and GLMs Section
    • Begin Regression Example
    • Continue Regression Example
    • Finish Regression Example
    • Begin Regression and GLM Slides
    • Finish Generalized Linear Modeling Slides
    • Heart Data Binomial GLM Example
    • Epidemic Data Poisson GLM Example
    • Regression and GLMs Exercises

    K-Means, K-Medoids, and Hierarchical Cluster Analysis Approaches

    • Materials and End-of-Section-6 Exercise
    • Regression and GLM Exercises Solutions (part 1)
    • Regression and GLM Exercises Solutions (part 2)
    • Regression and GLM Exercises Solutions (part 3)
    • K-Means Iris Flower Example
    • K-Means Exoplanets Example
    • K-Medoids Iris Flower Re-Analysis Example
    • Hierarchical Clustering Iris Flower Example
    • Hierarchical Clustering Pottery Example

    Density-Based and Agglomerative Hierarchical Clustering

    • Materials for Density-Based and Hierarchical Agglomerative Clustering Section
    • Density-Based and Agglomerative Clustering Introduction and Previous Exercise
    • Density-Based Clustering Example
    • Body Measurements and Agglomerative Hierarchical Clustering Example
    • Continue Body Measurements Agglomerative Clustering Example
    • Clustering Jet Fighters Example

    More Cluster Analysis Examples, Graphics, and Detecting Outliers

    • Materials and End-of-Section-8 Exercise
    • K-Means Clustering Explained in Detail
    • Clustering Crime Rates Example
    • Clustering Crime Rates Example (part 2)
    • Gastroenterologist Questionnaire Model-Based Clustering Eample
    • Graphical Approaches to Cluster Analysis Examples
    • Detecting Outliers
    • Detecting Outliers (part 2)

    K-Means TAM Residuals Cluster Analysis Software Case example

    • Crime Data Exercise Solution
    • Crime Data Exercise Solution (part 2)
    • Materials for Final Data Mining Course Section
    • K-Means Clustering PLS-POS Capability Implementation
    • K-Means Clustering PLS-POS Capability Implementation Concepts
    • Implementing K-Means Clustering for TAM Residuals Continued
    • Implementing K-Means Clustering for TAM Residuals in R Software

    Instructors

    Dr Geoffrey Hubona
    Associate Professor
    Freelancer

    B.E /B.Tech, Other Bachelors, Other Masters, Ph.D, M...

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