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.
Get acquainted with various analysis and visualization tools such as Ggplot and plotly
Understand the behavior of data; build significant models to understand Statistics Fundamentals
Learn about the various R libraries like Dplyr, Data.table used to manipulate data
Use R libraries and work on data manipulation, data preparation and data explorations
Use of R graphics libraries like Ggvis, Plotly etc.
ANOVA, Linear Regression using OLS, Logistic Regression using MLE, KNN, Decision Trees
While there are no prerequisites, elementary programming knowledge will benefit those who attend this course.
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Interact with instructors in real-time— listen, learn, question and apply. Our instructors are industry experts and deliver hands-on learning.
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Learn theory backed by practical case studies, exercises and coding practice. Get skills and knowledge that can be effectively applied.
Learn from the best in the field. Our mentors are all experienced professionals in the fields they teach.
Learn concepts from scratch, and advance your learning through step-by-step guidance on tools and techniques.
Get reviews and feedback on your final projects from professional developers.
Get an overview of the world of data science. Get acquainted with various analysis and visualization tools used in data science.
Hands-on: No hands-on
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.
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.
Formulate Hypothesis and perform Hypothesis Testing on a real production plant scenario.
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.
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.
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.
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.
Project to be selected by candidates.
With attributes describing various aspect of residential homes, you are required to build a regression model to predict the property prices.
This project involves building a classification model.
Predict if a patient is likely to get any chronic kidney disease depending on the health metrics.
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).
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Everything from the course structure to the trainer and training venue was excellent. The curriculum was extensive and gave me a full understanding of the topic. This training has been a very good investment for me.
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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
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
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.
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.
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