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Exploratory Data Analysis with R

  • Course Code: Data Analysis / BI - Exploratory Data Analysis with R
  • Course Dates: Contact us to schedule.
  • Course Category: Big Data & Data Science Duration: 3 Days Audience: This course is geared for Python experienced developers, analysts or others who wants to Learn exploratory data analysis concepts using powerful R packages to enhance your R data analysis skills.

Course Snapshot 

  • Duration: 3 days 
  • Skill-level: Foundation-level Exploratory Data Analysis with R skills for Intermediate skilled team members. This is not a basic class. 
  • Targeted Audience: This course is geared for Python experienced developers, analysts or others who wants to Learn exploratory data analysis concepts using powerful R packages to enhance your R data analysis skills.  
  • Hands-on Learning: This course is approximately 50% hands-on lab to 50% lecture ratio, combining engaging lecture, demos, group activities and discussions with machine-based student labs and exercises. Student machines are required. 
  • Delivery Format: This course is available for onsite private classroom presentation, or remote instructor led delivery, or CBT/WBT (by request). 
  • Customizable: This course may be tailored to target your specific training skills objectives, tools of choice and learning goals. 

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. 

Working in a hands-on learning environment, led by our Data Analysis with R expert instructor, students will learn about and explore: 

  • Speed up your data analysis projects using powerful R packages and techniques 
  • Create multiple hands-on data analysis projects using real-world data 
  • Discover and practice graphical exploratory analysis techniques across domains 

Topics Covered: This is a high-level list of topics covered in this course. Please see the detailed Agenda below 

  • Learn powerful R techniques to speed up your data analysis projects 
  • Import, clean, and explore data using powerful R packages 
  • Practice graphical exploratory analysis techniques 
  • Create informative data analysis reports using ggplot2 
  • Identify and clean missing and erroneous data 
  • Explore data analysis techniques to analyze multi-factor datasets 

Audience & Pre-Requisites 

This course is geared for attendees with Python skills who wish to Learn exploratory data analysis concepts using powerful R packages to enhance your R data analysis skills 

Pre-Requisites:  Students should have  

  • developers with some knowledge of Python.  
  • experienced with spreadsheet software who know the basics of Python. 

Course Agenda / Topics 

  1. Setting Up Our Data Analysis Environment 
  • Setting Up Our Data Analysis Environment 
  • Technical requirements 
  • The benefits of EDA across vertical markets 
  • Manipulating data 
  • Installing the required R packages and tools 
  1. Importing Diverse Datasets 
  • Importing Diverse Datasets 
  • 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 
  1. Examining, Cleaning, and Filtering 
  • Examining, Cleaning, and Filtering 
  • 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 
  1. Visualizing Data Graphically with ggplot2 
  • Visualizing Data Graphically with ggplot2 
  • Technical requirements 
  • Advanced graphics grammar of ggplot2 
  • Installing ggplot2 
  • Scatter plots 
  • Histogram plots 
  • Density plots 
  • Probability plots 
  • Box plots 
  • Residual plots 
  1. Creating Aesthetically Pleasing Reports with knitr and R Markdown 
  • Creating Aesthetically Pleasing Reports with knitr and R Markdown 
  • Technical requirements 
  • Installing R Markdown 
  • Reproducible data analysis reports with knitr 
  • Exporting and customizing reports 
  1. Univariate and Control Datasets 
  • Univariate and Control Datasets 
  • 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 
  1. Time Series Datasets 
  • Time Series Datasets 
  • 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 
  1. Multivariate Datasets 
  • Multivariate Datasets 
  • Technical requirements 
  • Introducing and reading a dataset 
  • Cleaning the data 
  • Mapping and understanding the structure 
  • Hypothesis test 
  • Parsimonious model 
  • Levene’s test 
  • Data visualization 
  1. Multi-Factor Datasets 
  • Multi-Factor Datasets 
  • 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 
  1. Handling Optimization and Regression Data Problems 
  • Handling Optimization and Regression Data Problems 
  • 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 
  1. Next Steps 
  • Next Steps 
  • 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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