Course Outline
Data analysis is crucial to accurately predict the performance of an application. The course begins by getting you started with R, including basic programming and data import, data visualization, pivoting, merging, aggregating, and joins. Once you are comfortable with the basics, you will read ahead and learn all about data visualization and graphics. You will learn data management techniques such as pivots, aggregations, and dealing with missing values. With this various case studies and examples, this course gives you the knowledge to confidently start your career in the field of data science.
Overview
This is a hands-on guide that walks you through concepts with examples using built-in R data. Every topic is enriched with a supporting example that highlights the concept, followed by activities that will gradually build into a full data science project to showcase skills learned. You will perform an end-to-end analysis, starting a data science portfolio.
Course Length
1 day
Scope
The course begins by getting you started with R, including basic programming and data import, data visualization, pivoting, merging, aggregating, and joins. Here is the list of course objectives:
» Use the basic programming concepts of R such as loading packages, arithmetic functions, data structures, and flow control
» Import data to R from various formats, such as CSV, Excel, and SQL
» Clean data by handling missing values and standardizing fields
» Perform univariate and bivariate analysis using ggplot2
» Create statistical summary and advanced plots, such as histograms, scatter plots, box plots, and interaction plots
» Apply data management techniques, such as factors, pivots, aggregation, merging, and dealing with missing values, on the example data sets
Target Audience
This course is for analysts who are looking to grow their data science skills beyond the tools they have used before, such as MS Excel and other statistical tools.
Technical Requirements
Hardware:
This course will require a computer system for the instructor and one for each student. The minimum hardware requirements are as follows:
» Processor: i3
» Memory: 2 GB RAM
» Hard disk: 10 GB
Software:
» Operating system: Windows 8 64–bit
» R and RStudio
» Browsers (Google Chrome and Mozilla Firefox - latest versions)
Course Outline
LESSON 1: INTRODUCTION TO R
LESSON 2: DATA VISUALIZATION AND GRAPHICS
LESSON 3: DATA MANAGEMENT
SKU | 035428SE |
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Weight | 0.0000 |
Coming Soon | No |
Days of Training | 1.0 |
Audience | Student |
Product Family | Partnerware |
Product Type | Digital Courseware |
Electronic | Yes |
ISBN | No |
Language | English |
Page Count | No |
Curriculum Library | No |
Year | No |
Manufacturer's Product Code | No |
Current Revision | 1.0 |
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Revision Notes | No Revision Information Available |
Original Publication Date | 2018-09-23 00:00:00 |