For Corporates

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.

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What You Will Learn

1. Tools & Technologies

Get acquainted with various analysis and visualization tools such as Ggplot and plotly

2. Statistics for Data Science

Understand the behavior of data; build significant models to understand Statistics Fundamentals

3. Learn R for Data Science

Learn about the various R libraries like Dplyr, Data.table used to manipulate data

4. Exploratory Data Analysis

Use R libraries and work on data manipulation, data preparation and data explorations

5. Data Visualization using R

Use of R graphics libraries like Ggvis, Plotly etc.

6. Advanced Statistics & Predictive Modeling

ANOVA, Linear Regression using OLS, Logistic Regression using MLE, KNN, Decision Trees

6. Advanced Statistics & Predictive Modeling

ANOVA, Linear Regression using OLS, Logistic Regression using MLE, KNN, Decision Trees

1. Tools & Technologies

Get acquainted with various analysis and visualization tools such as Ggplot and plotly

2. Statistics for Data Science

Understand the behavior of data; build significant models to understand Statistics Fundamentals

3. Learn R for Data Science

Learn about the various R libraries like Dplyr, Data.table used to manipulate data

4. Exploratory Data Analysis

Use R libraries and work on data manipulation, data preparation and data explorations

5. Data Visualization using R

Use of R graphics libraries like Ggvis, Plotly etc.

6. Advanced Statistics & Predictive Modeling

ANOVA, Linear Regression using OLS, Logistic Regression using MLE, KNN, Decision Trees

1. Tools & Technologies

Get acquainted with various analysis and visualization tools such as Ggplot and plotly

Data Science with R Prerequisites

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

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.