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ANDHRA PRADESH
ALL UNIVERSITIES (DEGREE)
V & VI SEMESTER
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SYLLABUS & STUDY NOTES
SUBJECT : BIGDATA ANALYTICS USING R
SYLLABUS :
Unit – 1: Introduction to Big data
Data, classification Of Digital Data--structured, unstructured, semi-structured data, characteristics of data, evaluation of big data, definition and challenges of big data , what is big data and why to use big data ?, business intelligence Vs big data.
Unit – 2: Big data Analytics
What is and isn’t big data analytics? Why hype around big data analytics? Classification of analytics, top challenges facing big data, importance of big data analytics, technologies needed to meet challenges of big data.
Unit – 3: Introduction to R and getting started with R
What is R? Why R? , advantages of R over other programming languages, Data types in R-logical, numeric, integer, character, double, complex, raw, coercion, ls() command, expressions, variables and functions, control structures, Array, Matrix, Vectors, R packages.
Unit – 4: Exploring data in R
Data frames-data frame access, ordering data frames, R functions for data frames dim(), nrow(), ncol(), str(), summary(), names(), head(), tail(), edit() .Load data frames—reading from .CSV files, sub setting data frames, reading from tab separated value files, reading from tables.
Unit – 5: Data Visualization using R
Reading and getting data into R (External Data): XML files, Web Data, JSON files, Databases, Excel files.
Working with R Charts and Graphs: Histograms, Bar Charts, Line Graphs, Scatterplots, Pie Charts.
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ANDHRA PRADESH
ALL UNIVERSITIES (DEGREE)
V & VI SEMESTER
Telegram group Join Now
What'sapp group Join Now
SYLLABUS & STUDY NOTES
SUBJECT : BIGDATA ANALYTICS USING R
SYLLABUS :
Unit – 1: Introduction to Big data
Data, classification Of Digital Data--structured, unstructured, semi-structured data, characteristics of data, evaluation of big data, definition and challenges of big data , what is big data and why to use big data ?, business intelligence Vs big data.
Unit – 2: Big data Analytics
What is and isn’t big data analytics? Why hype around big data analytics? Classification of analytics, top challenges facing big data, importance of big data analytics, technologies needed to meet challenges of big data.
Unit – 3: Introduction to R and getting started with R
What is R? Why R? , advantages of R over other programming languages, Data types in R-logical, numeric, integer, character, double, complex, raw, coercion, ls() command, expressions, variables and functions, control structures, Array, Matrix, Vectors, R packages.
Unit – 4: Exploring data in R
Data frames-data frame access, ordering data frames, R functions for data frames dim(), nrow(), ncol(), str(), summary(), names(), head(), tail(), edit() .Load data frames—reading from .CSV files, sub setting data frames, reading from tab separated value files, reading from tables.
Unit – 5: Data Visualization using R
Reading and getting data into R (External Data): XML files, Web Data, JSON files, Databases, Excel files.
Working with R Charts and Graphs: Histograms, Bar Charts, Line Graphs, Scatterplots, Pie Charts.
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