Data Science with R Rated 4.5/5 based on 34 customer reviews

Data Science with R Online Training

Master R to start a career in Data Science

  • 40 hours of Instructor led Training
  • Comprehensive Statistical Learning with advanced Excel
  • In-depth hands-on sessions with R
  • Master Advanced Statistics and Predictive Modeling
  • Learn Supervised and Unsupervised Machine Learning Algorithms

Why should you learn Data with R science?

Data Scientists are among the highest paid technology professionals in the world. Businesses are increasingly relying on analytics and statistics to expand and improve their products as a result of which data analysis is being used in marketing, supply chain, human resource and other areas. While there are several programming languages that are used for Data Science, R is every statistician’s first programming choice for data analysis. Its user friendliness, flexibility and robustness have made it very popular for data analysis in the field of academics and research and in the enterprise context too. And if data visualization is what you want then R will get the job done for you. The programs written in R will help you tell stories with your data and turn numbers into useful information.

How do you get started with Data Science with R?

This interactive and comprehensive course is a great place for you to get started on R programming language and its use in Data Science. This Data Science with Rcoursecovers topics like exploratory data analysis, statistics fundamentals, hypothesis testing, regression & classification modeling techniques and machine learning algorithms. You will learn how to create R programs that will help discover and interpret relationships in complex information and solve real world problems. In a series of hands-on lab exercises you will also learn how to create R visualizations that will help analyse and handle large data sets. Enroll now and get trained for the hottest career of the decade.

What you will learn

Prerequisites

Participants are expected to have basic programming knowledge.

Who should take

  • Those Interested in the field of data science
  • Those wanting to master R in a comprehensive learning program
  • Those wanting to use R for effective analysis of large datasets
  • Software or Data Engineers interested in quantitative analysis with R

Zeolearn Experience

Learn By Doing

Immersive Hands-on training with combination of theoretical learning, hands-on exercises, group discussions, assignments and intensive Q&A sessions.

Live & Interactive

Ask questions, get clarifications, and engage in discussions with instructors and other participants.

Mentored by Industry Experts

Get Mentored by Industry practitioners having more than 10 years of experience

Reason based learning

Don’t gain just theoretical or practical knowledge. Understand the WHAT, WHY, and HOW of a subject. Simplify the subject matter and get in-depth comprehension.

Code Review by Professionals

Get reviews and timely feedback on your assignments and projects from professional developers.

Build Projects

We emphasize on learning the concepts through examples and help you in building a portfolio of projects through the course of training

Lifetime Enrolment

Free lifetime enrolment into any of the upcoming batches to help you refresh the concepts

Curriculum designed by Experts

The Curriculum goes through multiple levels of design and preparation by the experts to keep the topics/modules relevant to everyday changes in technology.

Study even from remote locations

Learn to use collaborative mediums to share opinions and improve your coding skills with assistance from the instructors and other participants.

Curriculum

Learning Objectives:

Get an overview of the world of data science. Get acquainted with various analysis and visualization tools used in data science.

Topics

  • What is Data Science?
  • Analytics Landscape
  • Life Cycle of a Data Science Project
  • Data Science Tools & Technologies

Hands-on: No hands-on

Learning Objectives:

In this module, you will get an introduction to R, and understand why it is so popular among Data Scientists. Starting with the installation of R and its components, you will load and learn about frequently used libraries. This module touches upon data structures in R, loops and control statements in R and teaches you how to write custom functions, nested functions and functions with arguments.You will learn all about loop functions available in R which are efficient and can be written with a single command.

Going further, you will explore string manipulations and regular expressions and see how functions can be extremely useful for text or unstructured data manipulations. This module also teaches how to import data from various sources in R and also how to write files from R and connect to various databases from R. You will get an overview of visualization in R with base and ggplot libraries, and grasp the Grammar of Graphics in a very structured easy-to-understand manner. The module ends with a hands-on session on a real-life case study.

Topics

  • Intro to R Programming
  • Installing and Loading Libraries
  • Data Structures in R
  • Control & Loop Statements in R
  • Functions in R
  • Loop Functions in R
  • String Manipulation & Regular Expression in R
  • Working with Data in R
  • Data Visualization in R
  • Case Study

Hands-on:

  • Know how to install R, R Studio and other libraries
  • Write R Code to understand and implement R Data Structures 
  • Write R Code to implement loop and  control structures in R
  • Write R Code to read and write data from/to R.
  •  Read data not only from CSV files but also using direct connection to various databases
  • Write R Code to implement ggplot for data visualization
  • Complex Real-Life Data Manipulation, Preparation & Exploratory Data Analysis case study

Learning Objectives:

This module explores basics like mean (expected value), median and mode. You will understand the distribution of data in terms of variance, standard deviation and interquartile range and get basic summaries about data and its measures, together with simple graphics analysis.

Through daily life examples, you will understand the basics of probability, marginal probability and its importance with respect to data science. Learn Baye’s theorem and conditional probability, and alternate and null hypothesis including Type1 error, Type2 error, power of the test, and p-value.

Topics

  • Measures of Central Tendency
  • Measures of Dispersion
  • Descriptive Statistics
  • Probability Basics
  • Marginal Probability
  • Bayes Theorem
  • Probability Distributions
  • Hypothesis Testing

Hands-on:

Formulate Hypothesis and perform Hypothesis Testing on a real production plant scenario

Learning Objectives:

This module analyses Variance and its practical use, covering strong concepts, model building, evaluating model parameters, measuring performance metrics on Test and Validation set. You will use Linear Regression with Ordinary Least Square Estimate to predict a continuous variable. Further you will learn to enhance model performance by means of various steps like feature engineering & regularization.

Along the way, you will learn about Dimensionality Reduction Technique with Principal Component Analysis and Factor Analysis, including methods to find the optimum number of components/factors using scree plot, one-eigenvalue criterion. You will be able to cement the concepts learnt through real life case studies with Linear Regression and PCA & FA.

Topics

  • ANOVA
  • Linear Regression (OLS)
  • Case Study: Linear Regression
  • Principal Component Analysis
  • Factor Analysis
  • Case Study: PCA/FA

Hands-on:

  • With attributes describing various aspect of residential homes, you are required to build a regression model to predict the property prices.  
  • Reduce Data Dimensionality for a House Attribute Dataset for more insights & better modeling

Learning Objectives:  

In this module you will explore Binomial Logistic Regression for Binomial Classification Problems, including evaluation of model parameters, model performance using various metrics like sensitivity, specificity, precision, recall, ROC Curve, AUC, KS-Statistics, and Kappa Value. You will work with a real-life case study with Binomial Logistic Regression.

Next, you will learn about KNN Algorithm for Classification Problem, including techniques that are used to find the optimum value for K. You will see a real-life case study with KNN Decision Trees, to help you understand regression & classification problems. At the end of this module you will have working knowledge on Entropy, Information Gain, Standard Deviation reduction, Gini Index, and CHAID, among others.

Topics

  • Logistic Regression
  • Case Study: Logistic Regression
  • K-Nearest Neighbor Algorithm
  • Case Study: K-Nearest Neighbor Algorithm
  • Decision Tree
  • Case Study: Decision Tree

Hands-on:

  • With various customer attributes describing customer characteristics, build a classification model to predict which customer is likely to default a credit card payment next month. This can help the bank be proactive in collecting dues.
  • Predict if a patient is likely to get any chronic kidney disease depending on the health metrics.
  • Wine comes in various kinds. With the ingredient composition known, we can build a model to predict the Wine Quality using Decision Tree (Regression Trees).

Learning Objectives:

In this module, you will understand Time Series Data and its components like Level Data, Trend Data and Seasonal Data; also work with the Exponential Smoothing Model and know when to use the same.

You will know how to use Holt's model when your data has Constant Data, Trend Data and Seasonal Data and learn how to select the right smoothing constants for each set of circumstances.Finally, you will use Autoregressive Integrated Moving Average Model for building a Time Series Model and carry out a real-life case study with ARIMA.

Topics

  • Understand Time Series Data
  • Visualizing TIme Series Components
  • Exponential Smoothing
  • Holt's Model
  • Holt-Winter's Model
  • ARIMA
  • Case Study: Time Series Modeling on Stock Price

Hands-on:

  • Write R code to Understand Time Series Data and its components like Level Data, Trend Data and Seasonal Data
  • Write R code to Use Holt's model when your data has Constant Data, Trend Data and Seasonal Data.Learn to select the right smoothing constants.
  • Write R code to Use Auto Regressive Integrated Moving Average Model for building Time Series Model
  • Dataset including features such as symbol, date, close, adj_close, volume of a stock. This data will exhibit characteristics of a time series data. We will use ARIMA to predict the stock prices.

Learning Objectives:

You will work on an industry mentor guided group project to handle a real-life project, the same way you would execute a data science project in any business problem.

Topics

  • Industry relevant capstone project under experienced industry-expert mentor

Hands-on:

Project to be selected by candidates.

Project

Predict House Price using Linear Regression

With attributes describing various aspect of residential homes, you are required to build a regression model to predict the property prices.

Predict credit card defaulter using Logistic Regression

With various customer attributes describing customer characteristics, build a classification model to predict which customer is likely to

Predict chronic kidney disease using KNN

Predict if a patient is likely to get any chronic kidney disease depending on the health metrics.

Predict quality of Wine using Decision Tree

Wine comes in various styles. With the ingredient composition known, we can build a model to predict the Wine Quality using Decision Tree (Regression Trees).

FAQs

The Course

  • Basics of R including functions, vectors, variables and core principles of programming
  • To create R programmes that will help discover and interpret relationships in complex information and solve real world problems
  • To  create R visualizations that will help analyse and handle large data sets
  • How to work with large data sets coming from varied sources

The average data scientist salary is $113,436, according to Glassdoor. The number of Data Science and Analytics job listings is projected to grow by nearly 364,000 listings to approximately 2,720,000 by 2020 according to a survey by IBM. These statistics prove that forging a career in data science is going to reap rich benefits in the near future. This course will help you understand how R can be used for data science. The workshop will equip you to build applications and work as data scientists in top companies in various sectors including pharmaceuticals, cyber security, government offices and retail.

There are no prerequisites but participants would benefit if they have elementary programming knowledge and familiarity with statistics.

This course is apt for existing and aspiring data analysts who wish to learn R for Data Science.

Workshop Experience of Data Science with R

The workshops at Zeolearn are always interactive, immersive and intensive hands-on programs. There are 3 modes of Delivery and you can select based on the requirements -

  • Online Classroom training: Learn from anywhere through the most preferred virtual live instructor led training with the help of hands-on training and interactive sessions
  • One-to-One Training: You can enrol for one-to-one Data Science with R classroom training session with our expert trainer at a preferred time. With this mode, you can customize your curriculum to suit your learning needs.
  • Team/Corporate Training: In this type of training, an Organization can nominate their entire team for online or classroom training. You can customize your curriculum to suit your learning needs and also get post-training expert’s support to implement Data Science with R concepts in the project.

We follow the below mentioned procedure for all the training programs by dividing the complete workshop experience into 3 stages i.e Pre, Workshop and Post. This is a tried and tested approach using which we have been able to upskill thousands of engineers.


Pre-training

Before the start of training program, we make sure that you are ready to understand the concepts from Day 1. Hence, as a process of preparation for the intensive workshop, we provide the following -

  • Reference articles/ Videos and e-books
  • 2-4 hrs of training on pre-requisites - to make you workshop ready
  • Pre-Workshop Assessments - to assess and benchmark
  • Environment set-up docs


During Training

The training is completely hands-on and you receive the below mentioned deliverables from Zeolearn team -

  • PPT and Code Snippets used in the class.
  • Learners Guide or E-book.
  • Projects / Case Studies.
  • Assessments / Lab exercises.
  • Quizzes and Polls.
  • Study Plans - To structure your learning.


Post Training

We don’t just impart skills but also make sure that you implement them in the project. And for that to happen, we are always in touch with you either through newsletters or webinars or next version trainings. Some of the post-training deliverables lined-up for you are -  

  • Project assistance with mentor 
  • Course Recordings  
  • Access to Alumni Network 
  • Additional workshops on advanced level concepts  
  • Regular emails/newsletters on Blogs/Tutorials and other informational content

Yes, Zeolearn has well-equipped labs with the latest version of hardware and software. We provide Cloudlabs to explore every feature of Data Science with R through hands-on exercises. Cloudlabs provides an environment that lets you build real-world scenarios and practice from anywhere across the globe.  You will have live hands-on coding sessions and will be given practice assignments to work on after the class.

At Zeolearn, we have Cloudlabs for all the major categories like Web Development, Cloud Computing, and Data Science.

Covers Exploratory Data Analysis, Linear Regression, Logistic Regression, Decision Tree, Time Series Forecasting, Recommender Engines, Text Mining, ANN, SVM, K means Clustering, Ensemble Machine Learning Techniques, among others.


PROJECT-1

 
TITLE - Predict House Price using Linear Regression
DESCRIPTION - With attributes describing various aspect of residential homes, you are required to build a regression model to predict the property prices.


PROJECT-2

 
TITLE - Predict credit card defaulter using Logistic Regression
DESCRIPTION - With various customer attributes describing customer characteristics, build a classification model to predict which customer is likely to.


PROJECT-3

 
TITLE - Predict chronic kidney disease using KNN
DESCRIPTION - Predict if a patient is likely to get any chronic kidney disease depending on the health metrics.


PROJECT-4

 
TITLE - Predict quality of Wine using Decision Tree
DESCRIPTION - Wine comes in various styles. With the ingredient composition known, we can build a model to predict the Wine Quality using Decision Tree (Regression Trees).

This course is delivered by industry-recognized experts who would be having more than 10 years of real-time experience in Data Science with R.


Not only will they impart knowledge of the fundamentals and advanced concepts, they will provide end-to-end mentorship and hands-on training to help you work on real-world projects with regards to Data Science with R.

Once you register for the course you will be provided with system requirements and lab setup document which contains detailed information to prepare the environment for the course.

To attend Data Science with R training program, the basic hardware and software requirements are as mentioned below – 

Hardware Requirements 

  • Minimum of 8GB RAM – Windows, 6 GB - MAC 
  • Minimum  of 20 GB of Hard Disk space,  Recommended  SSD with 40 GB  
  • I3 Processor with 2.5 Hz, Recommended i5 or equivalent. 

Software Requirements 

  • R Studio 

Permissions Required 

  • Administrative privileges for Running R Studio’s utilities.

Data Science with R Online Training Experience

All our training programs are quite interactive and fun to learn with plenty of time spent on lot of hands-on practical training, use case discussions and quizzes. Our instructors also use an extensive set of collaboration tools and techniques which improves your online training experience.

This will be live interactive training led by an instructor in a virtual classroom.

You will receive a registration link to your e-mail id from our training delivery team. You will have to log in from your PC or other devices.

Yes, for all the online public workshops there would be participants logging in from different locations.

In case of any queries, you can reach out to our 24/7 dedicated support at any of the numbers provided in the link below: http://www.zeolearn.com/contact-us, or send an email to hello@zeolearn.com.

We also have Slack workspace for the corporates to discuss the issues. If the query is not resolved by email, we will facilitate a one-on-one discussion session with our trainers.

If you miss a class, you can access the class recordings anytime from our LMS. At the beginning of every session, there will be a 10-12 minute recapitulation of the previous class. You can watch the online recording and clarify your doubts at that time. You may need to login 15 minutes before the main lecture begins to avail this facility.
 

We also have a Free Lifetime enrollment for most of our courses. In case you miss out a class, you can also enroll for another complete workshop or only for a particular session.

Finance related

Typically, Zeolearn’s training are exhaustive and the mentors help you out in understanding the in-depth concepts.  

However, if you find it difficult to cope, you may discontinue within the first 4 hours of training and avail a 100% refund. Learn more about our refund policy here.

Zeolearn offers a 100% money back guarantee if the candidates withdraw from the course right after the first session. To learn more about the 100% refund policy, visit our refund page.

Yes, we have scholarships available for Students and Veterans. We do provide grants that can vary upto 50% of the course fees.

To avail scholarships, please get in touch with us at hello@zeolearn.com. The team shall send across the forms and instructions to you. Based upon the responses and answers that we receive, the panel of experts take a decision on the Grant. The entire process could take around 7 to 15 days.

Yes, we do have instalment options available for the course fees. To avail instalments, please get in touch with us at hello@zeolearn.com. The team shall explain how the instalments work and would provide the timelines for your case. 

Usually we allow payment in 2 to 3 instalments but have all to be paid before you complete the course.

Have More Questions?