Discovering Evidence of dirty data through data visualization
sahana
ITS836_Week4_Slides.pptxDr. Awny Alnusair University of the Cumberlands
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Putting the Data Analytics Lifecycle into Practice
The Data Analytics Lifecycle consists of the following six phases:
Discovery
Data Preparation
Model Planning
Model building
Communicate Results
Operationalize
To begin analyzing the data, you will need a tool that allows you to look closely at the data – That is “R”
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KDnuggest Poll - 2019
KDnuggets Poll is a survey of data science and machine learning software. It asks programmers what languages they use on a regular basis in their work
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The R Project for Statistical Computing
First of all you need to get R installed on your computer
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Up and Running with R
Once R is installed, you can test the installation by opening the R Console
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RStudio – an IDE for R
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Entering Data into R
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R Packages
https:// cloud.r-project.org/web/packages/index.html
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Things you’r expected to know about R ….
R Data Types and structures
Basic descriptive statistics, dirty data ..
Data Visualization and relationships between multiple variables
Generic Functions
Dealing with sample datasets that are available for you
Statistical Methods for Model Building and Evaluation
Hypothesis Testing - Welche’s t-test, Confidence intervals, Wilcoxon rank-sum test, type I and II errors, and ANOVA
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