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Data Analytics with R
Rated 4.0/5 based on 122 Votes customer reviews

Data Analytics with R Training

Learn and polish your skills in techniques such as Predictive Analytics, Association Rule Mining and create your own functions, objects and packages

  • 30 hours of Instructor-led training
  • Beginner to Advanced level
  • Hands-on training
Request Syllabus Schedules

Modes of Delivery

Key Features

30 hours of intensive mentoring
Engaging hands-on learning sessions
Acquire end-to-end knowledge on Data Analytics and R tools
Master core analysis concepts and apply in predictive data modeling
Enjoy the benefits of post-training assistance from our trainers
Get assistance from our trainers in implementing business analytics and mining techniques in real time projects

Description

The open source programming language R has increased in popularity in recent years, and is now universally accepted by statisticians and data miners as the number one language for data science. R uses cutting-edge technology to manipulate data and create statistical models and charts that can be used for predictive modelling. It gives quick results and because it is open source, it is supported by a worldwide community of over two million users and developers. Zeolearn’s virtual class will take you through the fundamentals of this powerful language R. From the ground up, you will learn how to prepare data for analysis and apply statistical measures to create data visualisations. By exploring the characteristics of data sets, you can analyse and achieve optimum results based on past data.

Here’s what you will learn!

  • Learn to explore and visualize data and polish your skills in techniques such as Predictive Analytics, Association Rule Mining and much more
  • Derive meaning from custom created charts that are used to represent complex data,  manipulate this data and create statistical models for predictive analysis
  • Learn to use R, not just as a statistical tool but to create your own functions, objects and packages

Is this course right for you?

Statisticians, Data Analysts, Business Analysts and professionals keen to learn more about the science and practice of data analysis using R, will benefit from this course.

What do you need to be familiar with?

  • Basic knowledge of a programming language such as Python or Java
  • A background in Mathematics will be beneficial

 

Curriculum

  1. R tools and their uses in Business Analytics
  2. Objectives
  3. Analytics
  4. Where is analytics applied?
  5. Responsibilities of a data scientist
  6. Problem definition
  7. Summarizing data
  8. Data collection
  1. Difference between R and other analytical languages
  2. Different data types in R
  3. Built in functions of R: seq(), cbind (), rbind(), merge()
  4. Subsetting methods
  5. Use of functions like str(), class(), length(), nrow(), ncol(),head(), tail()
  1. Steps involved in data cleaning
  2. Problems and solutions for Data cleaning
  3. Data inspection
  4. Use of functions grepl(), grep(), sub()
  5. Use of apply() function
  6. Coerce the data
  1. How R handles data in a variety of formats
  2. Importing data from csv files, spreadsheets and text files
  3. Import data from other statistical formats like sas7bdat and sps
  4. Packages installation used for database import
  5. Connect to RDBMS from R using ODBC and basic SQL queries in R
  6. Basics of Web Scraping
  1. Understanding the Exploratory Data Analysis(EDA)
  2. Implementation of EDA on various datasets
  3. Boxplots
  4. Understanding the cor() in R
  5. EDA functions like summarize()
  6. llist()
  7. Multiple packages in R for data analysis
  8. Segment plot HC plot in R
  1. Understanding on Data Visualization
  2. Graphical functions present in R
  3. Plot various graphs like tableplot
  4. Histogram
  5. Box Plot
  6. Customizing Graphical Parameters to improvise the plots
  7. Understanding GUIs like Deducer and R Commander
  8. Introduction to Spatial Analysis
  1. Introduction to Data Mining
  2. Understanding Machine Learning
  3. Supervised and Unsupervised Machine Learning Algorithms
  4. K-means Clustering
  1. Linear Regression
  2. Logistic Regression
  1. Decision Trees
  2. Algorithm for creating Decision Trees
  3. Greedy Approach: Entropy and Information Gain
  4. Creating a Perfect Decision Tree
  5. Classification Rules for Decision Trees
  6. Concepts of Random Forest
  7. Working of Random Forest
  8. Features of Random Forest

Frequently Asked Questions

This is the world of big data, and professionals who are well versed in data analysis and applied statistics are much sought after in companies across the globe. By discovering the important patterns in large quantities of data, companies can steer the direction of their work strategies and shape business success.R is a strongly functional programming language and environment that is used to statistically explore data sets. Increasingly popular in academia and industry circles, it has become the most important tool for statistical data science. This course gives a smooth transition into working with R, and gives you hands-on experience in working on a project that tests your knowledge and practical skills.
After completing our course, you will be able to:
  • Get a comprehensive understanding of the fundamentals of R
  • Know the steps needed to get started with R and the R Console
  • Have knowledge of Data types and Structures
  • Get an understanding of how to Explore and Visualize Data
  • Have familiarity with Programming Structures, Functions and Data Relationships
  • Get skills in techniques such as Predictive Analytics, Association Rule Mining and so on
Towards the end of the course, all participants will be required to work on a project to get hands on familiarity with the concepts learnt. You will perform predictive analyses on the provided data using R programming language. This project, which can also be a live industry project, will be reviewed by our instructors and industry experts. On successful completion, you will be awarded a certificate.
Classes are held on weekdays and weekends. You can check available schedules and choose the batch timings which are convenient for you.
You may be required to put in 10 to 12 hours of effort every week, including the live class, self study and assignments.
  • Your classes will be held online. All you need is a windows computer with good internet connection to attend your classes online. A headset with microphone is recommended.
  • You may also attend these classes from your smart phone or tablet.
Don’t worry, you can always access your class recording or opt to attend the missed session again in any other live batch.

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