Data Analytics with R Certification Training
Access Duration - 365 Days
4.5( 4 REVIEWS )
120 STUDENTS
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 : 10053842Course 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 | |||
Sophia Mcdonald
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.
Daphne Jackson
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
Aaron Willis
This is a good start of big journey dealing with the data on R. I got lots of principle and techniques after this course.
Phoebe Edwards
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.