DATA SCIENCE WITH R
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Gitam university mba DATA SCIENCE WITH R SOLVED PAPERS AND GUESS
Product Details: Gitam university DATA SCIENCE WITH R SOLVED PAPERS AND GUESS
Pub. Date: NEW EDITION APPLICABLE FOR Current EXAM
Publisher: MEHTA SOLUTIONS
Edition Description: 2018-19
RATING OF BOOK: EXCELLENT
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If you find yourself getting fed up and frustrated with other Gitam University book solutions now mehta solutions brings top solutions for Gitam university mba DATA SCIENCE WITH R SOLVED PAPERS AND GUESS book contains previous year solved papers plus faculty important questions and answers specially for Gitam University .questions and answers are specially design specially for Gitam University students .
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DATA SCIENCE WITH R
UNIT–I: Elements of R: Concept of R, Installing R, IDE of R, Mathematical Operators and Vectors, Assigning Variables, Special Numbers, Logical Vectors, Classes, Different types of numbers, Changing classes, Examining Variables, The workplace, Elements in R – Vectors, Matrices and Arrays, Lists, Conversion between vectors and lists, Combining lists, Data Frames
UNIT–II: Functions, Strings and Factors and Flow Controls: Environments, Functions, Strings, Factors, Flow Controls - Conditional – if and else, Vectorized if, Multiple Selection, Loops – repeat loops, while loops, for loops, Advanced looping – replication, looping over lists, looping over arrays, Multiple – Input Apply, Instant vectorization, Split-Apply Combine
UNIT–III: Packages and Visualization: Loading packages, search path, libraries and installed packages, installing packages, maintaining packages, Visualization – The three plotting systems, Scatterplots – base graphics, lattice graphics, ggplots, Line Plots, Histograms, Box Plots, Bar Charts, Other plotting packages and systems.
UNIT–IV: Computing Statistics and Exploratory Data Analysis with R: Summarizing data, Calculating relative frequencies, Tabulating Factors and creating contingency tables, Testing categorical variables for independence, Calculating Quantiles of a dataset, Converting data into z-scores, t-test, testing sample proportions, testing normality, comparing means of two samples, testing correlation for significance, Variations, Missing Values, Covariation, Patterns and Models
UNIT–V: Machine Learning and Model Building with R: Introduction – Types of machine learning algorithm, supervised learning algorithms –Linear regression in R, Logistic Regression in R Unsupervised Learning in
R -Clustering with R, Recommendation Algorithms, Steps to generate recommendations in R, Model Building: Model basics, Type of Models, Visualizing models – Predictions, Residuals, Model Building,
Communicating results – Basics of R Markdown
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