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Item number: 106162563

Data Science Fundamentals in R Training

Item number: 106162563

Data Science Fundamentals in R Training

159,00 192,39 Incl. tax

Order this unique E-Learning Data Science Fundamentals Training in R online, 1 year 24/7 access to rich interactive videos, progress through reporting and testing.

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R
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Data Science Fundamentals Using R E-Learning

Order this unique E-Learning Data Science Fundamentals course in R online, 1 year 24/7 access to rich interactive videos, speech, progress monitoring through reports and tests per chapter to test the knowledge directly.

R is one of the most widely used tools for data science. This path of courses covers common tasks required by data scientists who use R: data wrangling, debugging, defensive programming, and domain-specific language integration.

Course content

Introduction

Course: 11 Minutes

  • Course Introduction
  • What is Data Science?
  • Examples of Data Science
  • Sources of Data for Learning

Important R Basics

Course: 28 Minutes

  • Data Frames
  • The Structure Function str
  • Summary Statistics
  • Import JSON Data
  • Foreach Looping

Data Management

Course: 32 Minutes

  • Reshaping Data
  • Merging Data
  • Transposing Data
  • Aggregating Data
  • Basic Imputation
  • Linear Fitted Imputation
  • Categorize Continuous Variables

Data Analysis

Course: 45 Minutes

  • Modeling for Data Science
  • Linear Modeling
  • Analysis of Variance
  • The R coef Function
  • The R fitted Function
  • The R residuals Function
  • The Variance-Covariance Matrix
  • Confidence Intervals
  • Fitting Generalized Linear Models
  • Plotting Linear Models
  • T-Test
  • The TukeyHSD Test
  • The predict Function

Time Series

Course: 8 Minutes

  • Time Series
    The R forecast package

Practice: Data Science Fundamentals in R

Course: 15 Minutes

  • Exercise: Using R for Data Science

Introduction

Course: 5 Minutes

  • Course Introduction
  • Supervised and Unsupervised Learning

Clustering

Course: 34 Minutes

  • Multidimensional Scaling
  • Hierarchical Clustering
  • Hierarchical Clustering with corclust
  • K-Means Clustering
  • Selecting K for kmeans Clustering
  • Clustering Large Applications (Clara)
  • Fuzzy C-Means Clustering

Classification and Regression

Course: 1 Hour, 11 Minutes

  • Classification Trees with rpart in R
  • Regression Tree with rpart
  • Classification Trees with the tree Package
  • Regression Trees with the tree Package
  • K-Nearest Neighbor Classification
  • The randomForest Package
  • Combining Random Forests
  • Random Forests for Proximity Classification
  • Partitioning Around Medoids (PAM)
  • Naive Bayes Classifier
  • Linear Discriminant Analysis (LDA)
  • Quadratic Discriminant Analysis (QDA)
  • Mixture Discriminant Analysis (MDA)
  • Support Vector Machines (SVMs)
  • Loess Regression
  • Partial Least Squares Regression (PLS)
  • Smoothing Splines

Boosting and Bagging

Course: 11 Minutes

  • AdaBoost in R
  • Bagging

Advanced Visualizations

Course: 15 Minutes

  • Scatterplot Matrix in R
  • Overlay Density Plots
  • Scatterplot 3D Visualization in R

Practice: Machine Learning Examples in R

Course: 15 Minutes

  • Exercise: Statistical analysis in R
Language English
Qualifications of the Instructor Certified
Course Format and Length Teaching videos with subtitles, interactive elements and assignments and tests
Lesson duration 5:04 Hours
Progress monitoring Yes
Access to Material 365 days
Technical Requirements Computer or mobile device, Stable internet connections Web browsersuch as Chrome, Firefox, Safari or Edge.
Support or Assistance Helpdesk and online knowledge base 24/7
Certification Certificate of participation in PDF format
Price and costs Course price at no extra cost
Cancellation policy and money-back guarantee We assess this on a case-by-case basis
Award Winning E-learning Yes
Tip! Provide a quiet learning environment, time and motivation, audio equipment such as headphones or speakers for audio, account information such as login details to access the e-learning platform.

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