Data Science Course with R

Learn Data Science using R covering Data Manipulation, Data Visualization, Advanced Statistics and More

  • 40 hours of Instructor led Training
  • Interactive Statistical Learning with advanced Excel
  • Comprehensive Hands on with R
  • Covers Advanced Statistics and Predictive Modeling
  • Learn Supervised and Unsupervised Machine Learning Algorithms
Group Discount

Description

KnowledgeHut’s in-depth workshop on Data Science with R will help you master R and use its inbuilt functions and libraries for creating applications and programs for data science. R is a much preferred program because of its robustness, flexibility and ease of coding. Its various techniques such as clustering, time-series analyses and classification techniques, nonlinear/linear modelling and classical statistical tests make it apt for use in the field of statistical computation and data science.

 This intensive program covers a wide spectrum of Data Science teaching concepts like exploratory data analysis, statistics fundamentals, hypothesis testing, regression & classification modeling techniques and machine learning algorithms.

You will be able to build applications and work as data scientists in top companies in various sectors including pharmaceuticals, cyber security, government offices and retail. You will create R programs that will help discover and interpret relationships in complex information and solve real world problems. You will also learn to create R visualizations that will help analyse and handle large data sets. Enrol now and get trained for the hottest career of the decade.

What You Will Learn

Prerequisites

While there are no prerequisites, elementary programming knowledge will benefit those who attend this course.

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Who should Attend?

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

KnowledgeHut Experience

Instructor-led Live Classroom

Interact with instructors in real-time— listen, learn, question and apply. Our instructors are industry experts and deliver hands-on learning.

Curriculum Designed by Experts

Our courseware is always current and updated with the latest tech advancements. Stay globally relevant and empower yourself with the training.

Learn through Doing

Learn theory backed by practical case studies, exercises and coding practice. Get skills and knowledge that can be effectively applied.

Mentored by Industry Leaders

Learn from the best in the field. Our mentors are all experienced professionals in the fields they teach.

Advance from the Basics

Learn concepts from scratch, and advance your learning through step-by-step guidance on tools and techniques.

Code Reviews by Professionals

Get reviews and feedback on your final projects from professional developers.

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 Covered:

  • 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 Covered:

  • 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 Covered:

  • 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 Covered:

  • 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 Covered:

  • 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 types. With the ingredient composition known, we can build a model to predict the 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 Covered:

  • 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.
  • How 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 Covered:

  • Industry relevant capstone project under experienced industry-expert mentor

Hands-on: 

Project to be selected by candidates.

Projects

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

This project involves building a classification model.

Read More

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).

Note: These were the projects undertaken by students from previous batches.  

reviews on our popular courses

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KnowledgeHut is a great platform for beginners as well as the experienced person who wants to get into a data science job. Trainers are well experienced and we get more detailed ideas and the concepts.

Merralee Heiland

Software Developer.
Attended PMP® Certification workshop in May 2018
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Everything was well organized. I would like to refer to some of their courses to my peers as well. The customer support was very interactive. As a small suggestion to the trainer, it will be better if we have discussions in the end like Q&A sessions.

Steffen Grigoletto

Senior Database Administrator
Attended PMP® Certification workshop in May 2018
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The course which I took from Knowledgehut was very useful and helped me to achieve my goal. The course was designed with advanced concepts and the tasks during the course given by the trainer helped me to step up in my career. I loved the way the technical and sales team handled everything. The course I took is worth the money.

Rosabelle Artuso

.NET Developer
Attended PMP® Certification workshop in May 2018
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It’s my time to thank one of my colleagues for referring Knowledgehut for the training. Really it was worth investing in the course. The customer support was very interactive. The trainer took a practical session which is supporting me in my daily work. I learned many things in that session, to be honest, the overall experience was incredible!

Astrid Corduas

Senior Web Administrator
Attended PMP® Certification workshop in May 2018
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The customer support was very interactive. The trainer took a practical session which is supporting me in my daily work. I learned many things in that session. Because of these training sessions, I would be able to sit for the exam with confidence.

Yancey Rosenkrantz

Senior Network System Administrator
Attended Agile and Scrum workshop in May 2018
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It is always great to talk about Knowledgehut. I liked the way they supported me until I get certified. I would like to extend my appreciation for the support given throughout the training. My trainer was very knowledgeable and liked the way of teaching. My special thanks to the trainer for his dedication, learned many things from him.

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Senior System Architect
Attended Certified ScrumMaster®(CSM) workshop in May 2018
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I really enjoyed the training session and extremely satisfied. All my doubts on the topics were cleared with live examples. KnowledgeHut has got the best trainers in the education industry. Overall the session was a great experience.

Tilly Grigoletto

Solutions Architect.
Attended Agile and Scrum workshop in May 2018
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The hands-on sessions helped us understand the concepts thoroughly. Thanks to Knowledgehut. I really liked the way the trainer explained the concepts. He is very patient.

Anabel Bavaro

Senior Engineer
Attended Certified ScrumMaster®(CSM) workshop in May 2018

FAQs

The Course

On completing R and knowing the fundamentals of Data science, you can aim for a rewarding career in data science. Since the evolution of big data, data science and data analysis have become the most sought after career paths because of the huge demand for data science professionals. Not only high profile technology companies such as Google and Facebook but companies across sectors are hiring data scientists who can generate business and solve complex data related problems. This is the perfect course for you to step into the world of data science and make a career in what has been rated as the best job in America by Glassdoor.com.

You will:
  • Get advanced knowledge of data science and how to use it in real life business
  • Understand the statistics and probability of Data science
  • Get an understanding of data collection, data mining and machine learning
  • Learn tools like R

By the end of this course, you would have gained knowledge on the use of data science techniques and build applications on data statistics. This will help you land jobs as Data Scientists.

Tools and Technologies used for this course are

  • R Programming
  • MS Excel

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

Yes, KnowledgeHut offers this training online.

On successful completion of the course you will receive a course completion certificate issued by KnowledgeHut.

Your instructors are R experts who have years of industry experience.

Finance Related

Any registration canceled within 48 hours of the initial registration will be refunded in FULL (please note that all cancellations will incur a 5% deduction in the refunded amount due to transactional costs applicable while refunding) Refunds will be processed within 30 days of receipt of a written request for refund. Kindly go through our Refund Policy for more details.

KnowledgeHut offers a 100% money back guarantee if the candidate withdraws from the course right after the first session. To learn more about the 100% refund policy, visit our Refund Policy

The Remote Experience

In an online classroom, students can log in at the scheduled time to a live learning environment which is led by an instructor. You can interact, communicate, view and discuss presentations, and engage with learning resources while working in groups, all in an online setting. Our instructors use an extensive set of collaboration tools and techniques which improves your online training experience.

Minimum Requirements: MAC OS or Windows with 8 GB RAM and i3 processor

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