- Introduction
- Managing Expectations and Course Orientation
- Data Pre-Processing as Integral Part of Data Science
- Let's See an R Example of Data Pre-Processing
- Lures Example Script
Online
₹ 3,499
Quick facts
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Medium of instructions
English
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Mode of learning
Self study
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Mode of Delivery
Video and Text Based
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Course overview
Data management collects, organises, protects, and stores an organisation's data for business decisions. Data management solutions are needed to make sense of massive amounts of data as organisations create and consume it at unprecedented rates. Leading data management software ensures that accurate, current data drives decisions. The software prepares, catalogs, searches, and governs data, making it easy to find for analysis. Data management is the first step to scaling data analysis, which yields valuable customer insights and increases revenue. Effective data management allows organisation-wide access to trusted data for queries. R Data Pre-Processing & Data Management - Shape your Data! certification available by Udemy to candidates who want to learn how to prepare their data for excellent R-based analytics.
R Data Pre-Processing & Data Management - Shape your Data! online training Includes six hours of video,14 articles, five downloadable resources, and a digital certificate upon course completion.
R Data Pre-Processing & Data Management - Shape your Data! Online classes consist of introduction, data importing, data cleaning, and querying with data. table, query exercises, dplyr, integrate SQL, outlier detection, working with strings, and data management and time series
The highlights
- Full Lifetime Access
- Six Hours of Video
- 14 Articles
- Five Downloadable Resources
- Access on Mobile and TV
- Certificate of Completion
Program offerings
- Online course
- Learning resources
- 30-day money-back guarantee
- Unlimited access
Course and certificate fees
Fees information
certificate availability
certificate providing authority
What you will learn
R Data Pre-Processing & Data Management - Shape your Data! certification course, the applicant will learn about importing data into R in several ways while also being able to identify a suitable import tool. The applicant will understand how to select and implement a proper object class, convert the data into a tidy data format, and filter and query the data based on a wide range of parameters by joining 2 data tables together with dplyr 2 table verb syntax. The candidate will get to know how to use SQL code within R, basic R into SQL, working with dates and time as well as work with strings using regular expressions and detecting outliers in datasets
Who it is for
The syllabus
Introduction
Data Import and Data Structuring
- Script: Data import
- Importing Data and Snippets
- Using fread to handle big data fast
- Choosing the right class for your data
- Further R Exercises
Cleaning Your Data
- Script: Data cleaning
- tidyr - How tidy data looks like
- Wide to long data format
- Splitting columns
- Long to wide data format
Querying and Filtering Data with data.table
- Script: Querying with data.table
- What is data.table?
- Basic queries
- Queries at column level
- The by paramater for queries
- Update on recycle queries
- Keys
- Data.table exercises
- Data.table solutions
Queries and Filtering Exercises
- Query exercises INTRO
- 10 Exercises on 'data.frame'
- Data.frame Exercise Script
- Data.frame Solutions 1-4
- Data.frame Solutions 5-10
- 10 Exercises on 'data.table'
- Data.table Exercise Script
- Data.table Solutions 1-4
- Data.table Solutions 5 - 10
Using dplyr on one and multiple Datasets
- Script: dplyr
- Single Table Verbs in 'dplyr'
- Two Table Verbs - Mutating Joins
- Two Table Verbs - Filtering Joins and handling of ID mismatches
- Two Table Verbs - Set Operations
Integrate SQL into R
- Script: Integrate SQL
- Get package dbplyr
- R to SQL Translator
- Using SQL within R
- Set Up a SQLite Database in R
Detecting Outliers
- Outlier Script
- Introduction to Outlier Detection
- Detecting Outliers in Univariate Datasets
- Detecting Outliers in Multivariate Datasets
Working with Strings - Regular Expressions
- Script: Working with Strings
- Regular Expressions and Gsub
- What You Should Know about Strings in R
- The Gsub Family of Functions and Regular Expressions
- Regular Expressions Syntax
- A Great Add On Package
- Working with Strings in R: Exercise with Solution
Working with Dates and Time
- Data management and time series INTRO
- Importing a Time Series From Excel
- Section Script
- Classes POSIXt, Date and Chron
- Lubridate: Input and Time Zones
- Lubridate: Weekdays and Intervals
- Lubridate: Exercise Data Frame
- Lubridate: Calculations and Leap Years
- Lubridate: Data Handling Exercise
- Further R Exercises