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Home » Technology » Big Data & Data Science » Exploratory Data Analysis with R

Course Skill Level:

Foundational

Course Duration:

3 day/s

  • Course Delivery Format:

    Live, instructor-led.

  • Course Category:

    Big Data & Data Science

  • Course Code:

    EXDAWRL21E09

Who should attend & recommended skills:

Developers with basic Python and spreadsheet software experience

Who should attend & recommended skills

  • This course is geared for Python experienced developers, analysts or others with Python skills who want to learn exploratory data analysis concepts using powerful R packages to enhance your R data analysis skills.
  • Skill-level: Foundation-level Exploratory Data Analysis with R skills for Intermediate skilled team members. This is not a basic class.
  • Python: Basic (1-2 years’ experience)
  • Spreadsheet software: Basic (1-2 years’ experience)

About this course

Exploratory Data Analysis with R will help you build not just a foundation but also expertise in the elementary ways to analyze data. You will learn how to understand your data and summarize its main characteristics. You’ll also uncover the structure of your data, and you’ll learn graphical and numerical techniques using the R language. This course covers the entire exploratory data analysis (EDA) process – data collection, generating statistics, distribution, and invalidating the hypothesis. As you progress through the course, you will learn how to set up a data analysis environment with tools such as ggplot2, knitr, and R Markdown, using tools such as DOE Scatter Plot and SML2010 for multifactor, optimization, and regression data problems. By the end of this course, you will be able to successfully carry out a preliminary investigation on any dataset, identify hidden insights, and present your results in a business context.

Skills acquired & topics covered

  • Working in a hands-on learning environment, led by our Data Analysis with R expert instructor, students will learn about and explore:
  • Speeding up your data analysis projects using powerful R packages and techniques
  • Creating multiple hands-on data analysis projects using real-world data
  • Discovering and practicing graphical exploratory analysis techniques across domains
  • Powerful R techniques to speed up your data analysis projects
  • Importing, cleaning, and exploring data using powerful R packages
  • Practicing graphical exploratory analysis techniques
  • Creating informative data analysis reports using ggplot2
  • Identifying and cleaning missing and erroneous data
  • Exploring data analysis techniques to analyze multi-factor datasets

Course breakdown / modules

  • Technical requirements
  • The benefits of EDA across vertical markets
  • Manipulating data
  • Installing the required R packages and tools

  • Technical requirements
  • Converting rectangular data into R with the readr R package
  • Reading in Excel data with the readxl R package
  • Reading in JSON data with the jsonlite R package
  • Getting data into R from web APIs using the httr R package
  • Getting data into R by scraping the web using the rvest package
  • Importing data into R from relational databases using the DBI R package

  • Technical requirements
  • About the dataset
  • Reshaping and tidying up erroneous data
  • Manipulating and mutating data
  • Selecting and filtering data
  • Cleaning and manipulating time series data

  • Technical requirements
  • Advanced graphics grammar of ggplot2
  • Installing ggplot2
  • Scatter plots
  • Histogram plots
  • Density plots
  • Probability plots
  • Box plots
  • Residual plots

  • Technical requirements
  • Installing R Markdown
  • Reproducible data analysis reports with knitr
  • Exporting and customizing reports

  • Technical requirements
  • Reading the dataset
  • Cleaning and tidying up the data
  • Understanding the structure of the data
  • Hypothesis tests
  • Tietjen-Moore test
  • Parsimonious models
  • Probability plots
  • The Shapiro-Wilk test

  • Technical requirements
  • Introducing and reading the dataset
  • Cleaning the dataset
  • Mapping and understanding structure
  • Hypothesis test
  • Grubbs’ test and checking outliers
  • Parsimonious models
  • Bartlett’s test
  • Data visualization

  • Technical requirements
  • Introducing and reading a dataset
  • Cleaning the data
  • Mapping and understanding the structure
  • Hypothesis test
  • Parsimonious model
  • Levene’s test
  • Data visualization

  • Technical requirements
  • Introducing and reading the dataset
  • Cleaning the dataset
  • Mapping and understanding data structure
  • Hypothesis test
  • Grubbs test and checking outliers
  • Parsimonious model
  • Multi-factor variance analysis
  • Exploring graphically the dataset

  • Technical requirements
  • Introducing and reading a dataset
  • Cleaning the dataset
  • Mapping and understanding the data structure
  • Hypothesis test
  • Grubbs’ test and checking outliers
  • Parsimonious model
  • Exploration using graphics

  • Technical requirements
  • What to learn next
  • Why R?
  • The data analysis workflow
  • Building a data science portfolio
  • Datasets in R
  • Getting help with exploratory data analysis

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