### CCNA 200-301 Pearson uCertify Network Simulator

ISBN: 978-1-61691-837-8Cisco 200-301-SIMULATOR.AB1

Start your data science journey with the R programming language. Learn how to model, structure, visualize, and transform data.

(DS-R.AJ1) / ISBN : 978-1-64459-310-3This course includes

Interactive Lessons

Gamified TestPrep

Hands-On Labs

AI Tutor (Add-on)

11
Reviews

R for Data Science is a comprehensive course that leverages the popular R-syntax for mastering the techniques of data exploration, manipulation and visualization. You’ll learn the basics for using R vectors for creating lists, matrices, arrays, and data frames. Next, you’ll learn how to deploy conditional statements, functions, classes, and debugging. You’ll discover ways to read and write with R for creating transformative visualizations using ggplot2. By the end of this course, you’ll gain the confidence to tackle complex data challenges and write your own R scripts.

- Importing data using readr, heaven and dbplyr packages
- Cleaning data using features like na.rm, filter(), and mutate ()
- Reshaping and summarizing data with group-by()
- Utilizing tidyverse suite for ‘tidy data’
- Using R’s built-in features for statistical analysis
- Ability to use the ggplot2 package for visualization and customisation
- Exploring Git for vision control and collaborative projects
- Creating reproducible reports with R markdown

13+ Interactive Lessons | 110+ Exercises | 76+ Quizzes | 113+ Flashcards | 113+ Glossary of terms

45+ Pre Assessment Questions | 45+ Post Assessment Questions |

38+ LiveLab | 37+ Video tutorials | 01:59+ Hours

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- What this course covers?
- What you need for this course?
- Who this course is for?
- Conventions

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- Cluster analysis
- Anomaly detection
- Association rules
- Questions
- Summary

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- Patterns
- Questions
- Summary

4

- Packages
- Questions
- Summary

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- Packages
- Questions
- Summary

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- Packages
- Questions
- Summary

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- Packages
- K-means clustering
- Questions
- Summary

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- Packages
- Questions
- Summary

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- Packages
- Scatter plots
- Bar charts and plots
- Questions
- Summary

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- Packages
- Generating 3D graphics
- Questions
- Summary

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- Packages
- Dataset
- Questions
- Summary

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- Automatic forecasting packages
- Questions
- Summary

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- Packages
- Questions
- Summary

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- R Studio Sandbox

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- Plotting a Graph by Performing k-means Clustering
- Calculating K-medoids Clustering
- Displaying the Hierarchical Cluster
- Plotting Graphs By Performing Expectation-Maximization
- Plotting the Density Values
- Computing the Outliers for a Set
- Calculating Anomalies
- Using the apriori Rules Library

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- Using eclat to Find Similarities in Adult Behavior
- Finding Frequent Items in a Dataset
- Evaluating Associations in a Shopping Basket
- Determining and Visualizing Sequences
- Computing LCP, LCS, and OMD

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- Manipulating Text
- Analyzing the XML Text

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- Performing Simple Regression
- Performing Multiple Regression
- Performing Multivariate Regression Analysis

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- Performing Tetrachoric Correlation

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- Estimating the Number of Clusters Using Medoids
- Performing Affinity Propagation Clustering

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- Grouping and Organizing Bivariate Data
- Plotting Points on a Map

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- Displaying a Histogram of Scatter Plots
- Creating an Enhanced Scatter Plot
- Constructing a Bar Plot
- Producing a Word Cloud

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- Generating a 3D Graphic
- Producing a 3D Scatterplot

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- Finding a Dataset
- Making a Prediction

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- Using Holt Exponential Smoothing

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- Developing a Decision Tree
- Producing a Regression Model
- Understanding Instance-Based Learning
- Performing Cluster Analysis
- Constructing a Multitude of Decision Trees

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