Data Analytics with R Certification Training

Access Duration - 365 Days
4.5( 4 REVIEWS )
120 STUDENTS
£.00
 

What Will I Learn?

Understand concepts around Business Intelligence and Business Analytics
Explore Recommendation Systems with functions like Association Rule Mining , user-based collaborative filtering and Item-based collaborative filtering among others
Apply various supervised machine learning techniques
Perform Analysis of Variance (ANOVA)
Learn where to use algorithms - Decision Trees, Logistic Regression, Support Vector Machines, Ensemble Techniques etc

Overview

A free and open source development environment for statistical computing and graphics, R has been gaining in popularity and interest over the past year. R analytics (or R programming language) is free, open-source software used for all kinds of data science, statistics, and visualisation projects. R programming language is powerful, versatile, AND able to be integrated into BI platforms like Sisense, to help you get the most out of business-critical data.

There are multiple ways for R to be deployed today across a variety of industries and fields. One common use of R for business analytics is building custom data collection, clustering, and analytical models. Instead of opting for a pre-made approach, R data analysis allows companies to create statistics engines that can provide better, more relevant insights due to more precise data collection and storage. This course is designed to help you to tackle complex, multifaceted projects, expanding your career prospects with Data Analytics certification.

Why You Should Consider Taking this Course at Global Edulink?

Global Edulink is a leading online provider for several accrediting bodies, and provides learners the opportunity to take this exclusive course awarded by CPD. At Global Edulink, we give our fullest attention to our learners’ needs and ensure they have the necessary information required to proceed with the Course.  Learners who register will be given excellent support, discounts for future purchases and be eligible for a TOTUM Discount card and Student ID card with amazing offers and access to retail stores, the library, cinemas, gym memberships and their favourite restaurants.

  • Access Duration
  • Who is this Course for?
  • Entry Requirement
  • Method of Assessment
  • Certification
  • Awarding Body
  • Career Path & Progression
The course will be delivered directly to you, and from the date you joined the course you have 12 months of access to the online learning platform. The course is self-paced, and you can complete it in stages at any time.
Students and professionals who are interested in working in analytics industry and are keen to enhance their technical skills with exposure to cutting-edge practices
  • Learners should be over the age of 16, and have a basic understanding of English, ICT and numeracy.
  • Basic statistics knowledge
In order to complete the course successfully, learners will take an online assessment. This online test is marked automatically, so you will receive an instant grade and know whether you have passed the course.
Upon the successful completion of the course, you will be awarded the ‘Data Analytics with R Certification Training’ by CPD.
CPD is an internationally recognised qualification that will make your CV standout and encourage employers to see your motivation at expanding your skills and knowledge in an enterprise.
Once you successfully complete the course, you will gain an accredited qualification that will prove your skills and expertise in the subject matter. With this qualification you can further expand your knowledge by studying related courses on this subject, or you can go onto get a promotion or salary increment in your current job role. Below given are few of the jobs this certificate will help you in, along with the average UK salary per annum according to http://payscale.com/
  • Data Analyst - UP to £50 per Annum
  • Data Scientist- UP to £89 per Annum

Key Features

Gain an Accredited UK Qualification
Access to Excellent Quality Study Materials
Personalised Learning Experience
Support by Phone, Live Chat, and Email
Eligible for TOTUM Discount Card
UK Register of Learning Providers Reg No : 10053842

Course Curriculum

1: Introduction to Data Analytics
Introduction to terms like Business Intelligence
Business Analytics
Data
Information
how information hierarchy can be improved/introduced
understanding Business Analytics and R
knowledge about the R language
its community and ecosystem
understand the use of ‘R’ in the industry
compare R with other software in analytics
Install R and the packages useful for the course
perform basic operations in R using command line
learn the use of IDE R Studio and Various GUI
use the ‘R help’ feature in R
knowledge about the worldwide R community collaboration
2: Introduction to R Programming
The various kinds of data types in R and its appropriate uses
the built-in functions in R like: seq(), cbind (), rbind(), merge()
knowledge on the various subsetting methods
summarize data by using functions like: str(), class(), length(), nrow(), ncol()
use of functions like head(), tail(), for inspecting data
Indulge in a class activity to summarize data
dplyr package to perform SQL join in R
3: Data Manipulation in R
The various steps involved in Data Cleaning
functions used in Data Inspection
tackling the problems faced during Data Cleaning
uses of the functions like grepl(), grep(), sub()
Coerce the data
uses of the apply() functions
4: Data Import Techniques in R
Import data from spreadsheets and text files into R
import data from other statistical formats like sas7bdat and spss
packages installation used for database import
connect to RDBMS from R using ODBC and basic SQL queries in R
basics of Web Scraping
5: Exploratory Data Analysis
Understanding the Exploratory Data Analysis(EDA)
implementation of EDA on various datasets
Boxplots
whiskers of Boxplots
understanding the cor() in R
EDA functions like summarize(), llist()
multiple packages in R for data analysis
the Fancy plots like the Segment plot
HC plot in R
6: Data Visualization in R
Understanding on Data Visualization
graphical functions present in R
plot various graphs like tableplot
histogram
Boxplot
customizing Graphical Parameters to improvise plots
understanding GUIs like Deducer and R Commander
introduction to Spatial Analysis
7: Data Mining: Clustering Techniques
Introduction to Data Mining
Understanding Machine Learning
Supervised and Unsupervised Machine Learning Algorithms
K-means Clustering
8: Data Mining: Association Rule Mining & Collaborative filtering
Association Rule Mining
User Based Collaborative Filtering (UBCF)
Item Based Collaborative Filtering (IBCF)
9: Linear and Logistic Regression
Linear Regression
Logistic Regression
10: Anova and Sentiment Analysis
Anova
Sentiment Analysis
11: Data Mining: Decision Trees and Random Forest
Decision Tree
the 3 elements for classification of a Decision Tree
Entropy
Gini Index
Pruning and Information Gain
bagging of Regression and Classification Trees
concepts of Random Forest
working of Random Forest
features of Random Forest
among others
12: Project Work
Analyze census data to predict insights on the income of the people
based on the factors like: age
education
work-class
occupation using Decision Trees
Logistic Regression and Random Forest
Analyze the Sentiment of Twitter data
where the data to be analyzed is streamed live from twitter and sentiment analysis is performed on the same

Students feedback

4.5

Average rating (4)
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    S M

    Sophia Mcdonald

    January 28, 2021
    Wonderful

    Wonderful course, which helped me to understand every bit. The course was good and the pace was just the right amount for someone coming from the basic R course. It covers complex topics but breaks them down so they’re easier to understand.

    D J

    Daphne Jackson

    December 28, 2020
    Great service

    Best with the example and easy to understand the concept. Thanks for such a great learning experience. The support staff was extremely helpful and friendly. Keep up the great service

    A W

    Aaron Willis

    November 19, 2020
    Good start

    This is a good start of big journey dealing with the data on R. I got lots of principle and techniques after this course.

    P E

    Phoebe Edwards

    October 19, 2020
    Really clear

    It’s a really clear, straightforward and useful course with a lot of hands on content which may be helpful for future study and work.

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