Data Analysis Using R

  • Bachelor Degree/Graduation
  • 4 Months

What You will Learn

  • Introduction to R Programming, R Syntax and Data Structures.
  • Installing R and R Studio, Manipulating R Data Frames ,Analyzing Real Datasets using R, Load Excel Files with R.
  • Install External Libraries to Power up R, Subset Data, Manipulating R Vectors, Arrays and Matrixes, Plotting Data Using R.
  • Organizing your Code in R, Summarizing Data with R, Compute Basic Statistics about a Dataset, Aggregate and Sort Data.
Codestack Academy

Course Format

  • Physical Tutorials
  • Assignments
  • Industrial Projects
  • Certification
Codestack Academy

Course Duration*

  • 1 hr a day
  • 3 days a Week
  • 3 Assignments
  • 1 Project

Course Content

R is a popular programming language used for statistical computing and graphical presentation.

Its most common use is to analyze and visualize data.

Why Use R?

  • Custom data collection: R allows companies to build custom data collection, clustering, and analytical models, instead of using a pre-made approach.
  • Error checking: R allows companies to build in ways to check for errors in analytical models.
  • Reusable queries: R allows companies to easily reuse existing queries and ad-hoc analyses.
  • Data preparation: R can reduce time spent on data preparation and data wrangling, allowing data scientists to focus on more complex data science initiatives.
  • Reproducible research: R can help with reproducible research.
  • Advanced visualizations: R is popular for its visualizations, which can help make data easier to comprehend.

 

  • Overview of Data Analytics.
  • Need of Data Analytics.
  • Nature of Data.
  •  Classification of Data: Structured, Semi-Structured, Unstructured, Characteristics of Data, Applications of Data Analytics.
  • Overview of R programming.
  •  Environment setup with R Studio.
  •  R Commands.
  • Variables and Data Types.
  • Control Structures.
  • Array.
  • Matrix.
  • Vectors.
  •  Factors.
  •  Functions
  • R packages.
  • Reading and getting data into R (External Data): Using CSV files, XML files, Web Data, JSON files, Databases, Excel files.
  • Working with R Charts and Graphs: Histograms,Boxplots,Bar Charts, Line Graphs, Scatterplots, Pie Charts.
  • Random Forest.
  • Decision Tree.
  • Normal and Binomial distributions.
  • Time Series Analysis.
  • Linear and Multiple Regression.
  • Logistic Regression.
  • Survival Analysis.
  • Creating data for analytics through designed experiments.
  • Creating data for analytics through active learning.
  • Creating data for analytics through reinforcement learning.

Course Mentor(s)

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Persons Mentored

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Workshops Attended

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