Analytics With R

R analytics

Take this course to use R’s power and flexibility to solve real world data problems and perform effective data analysis leveraging its vast number of libraries and extensions.

Online Tutorial to learn Data Science in R

The Data Science certification course from Kovid Academy will comprehensively focus on discussing the different stages of Data Science – Data extraction, Munging, Cleansing, Modelling and Visualization. This course will give the participants an excellent understanding of the programming language ‘R’ and its calibre in the statistical computing environment. The language R, is categorically similar to Octave or Matlab (the tools that are specifically used for the scientific calculations), and in the recent past, R has acquired the capabilities of distributed processing and can work alongside with Hadoop and Spark. Today, R is considered as one of the de rigueur skills for the data science and predictive analytics.

Key concepts of R Programming.

In order to carve the participants as an expert in this field, our training curriculum will extensively allow them to attain a clear understanding of the data science, statistical concepts relevant to data science, programming using the R language, machine learning concepts and in-depth understanding of the implementation of libraries and models to solreal-worldl world data science challenges.

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Course Details

After completing this course, you will gain expertise in the following concepts:

  1. Determining what Data Science is all about and the role that R plays in it
  2. Identifying the phases of Data Analytics workflow
  3. Understanding the key statistical concepts relevant to Data Science
  4. Determining the R environment and libraries
  5. Getting data from open data sources and web
  6. Determining the key concepts of R programming
  7. Machine Learning algorithms
  8. Integrating R code with Hadoop and Spark

This course is extensively useful for designations like Technical Analyst, Data Analyst, Database Developer, Hadoop Developer, Big Data Architect, Programmer Analyst, Big Data Engineer, Business Analyst (Technical) etc. and aspirants who are looking for a career progression in the field of Data Science.

Instructor Led training 24 Hrs
Instructor Interaction Yes
Live Support Post Training 1 Year
Simulated Projects 2
Capstone/Hands On/Real Time Project 1
Kovid Academy Big Data Administrator Certificate Yes
35 CEU/PDU certificate Yes

Module 1: Introduction to Data Science

  • Introduction to Data Science
  • Introduction to R
  • Data Operations in R
  • Variable Assignment
  • Functions
  • Data transformation

Module 2: Basic Data Manipulation using R

  • Data structures in R
  • Reading, combining, sub setting and sorting data
  • Data generation functions

Module 3: Statistics for Data Science

  • Univariate analysis on a data
  • Find the distribution
  • Find mean, median, mode and standard deviation of the data
  • Multiple data with different distributions
  • Plotting
  • Hypothesis testing
  • Bivariate analysis
  • Correlation
  • Scatter plots
  • Making stratified samples
  • Categorical variables
  • Class variable

Module 4: Machine Learning Concepts

  • Supervised learning
  • Unsupervised learning
  • Common use cases

Module 5: Regression - Introduction

  • Linear regression: Lasso, Ridge
  • Variable selection

Module 6: Logistic Regression

  • Logistic regression: Lasso, Ridge
  • Naive Bayes

Module 7: Classification

  • Distance Concepts
  • Classification
  • k nearest
  • Clustering
  • k means
  • Multidimensional Scaling
  • PCA

Module 8: Random Forest

  • Decision trees
  • Cart C4.5
  • Random forest
  • Boosted trees
  • Gradient boosting

Module 9: Support Vector Machines

  • Support vector machines
  • Hyper-plane
  • Hyper-plane to segregate to classes
  • Gamma

Module 10: Recommenders

  • Recommendation engines
  • Collaborative filtering
  • Content based filtering

Module 11: R on Hadoop and Spark

  • RMR
  • RevR
  • Mahout
  • MLLib

Module 12: Kaggler Project

  • Take your skills to Kaggle
  • Participate in competitions
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Why Data Science and Why R?

According to Forbes, Data Science is one of the sexiest jobs of 21st century. The number of problems that can be solved using the data science are endless. As a matter of fact, there is a massive shortage of data scientists in the current market.

Over the decades, R is used as the highly preferred statistical computing language in the field of research. Of late, R with its distribution execution capabilities has made its straight entry from the classrooms to the corporate world, which has constructed it as one of the mandatory tools in the inventory of the data scientists.

What are the prerequisites of this course?

To make the most of this course, the participants are required to have: Basic familiarity with the computing and programming concepts and Good knowledge of mathematical and statistical concepts

Who is the right candidate for this course?

This course is extensively useful for the aspirants who are looking for a career progression in the field of Data Science and also who have the designations including (but not limited) to – Technical Analyst, Data Analyst, Database Developer, Hadoop Developer, Big Data Architect, Programmer Analyst, Big Data Engineer, Business Analyst (Technical) etc.

What are the training materials provided?

For all the training modules that are covered in this course, adequate materials and good references will be provided to the participants. In the case of online interactive trainings, every session will be recorded and uploaded in the LMS, giving the participants a feasibility to recap their completed training sessions.

What are the System requirements for participants?

The participants are required to have i3 or more processor. 4GB of RAM and about 50 GB of free hard disk space.

Is Certification offered and if so, how is it earned?

Once the training is complete, there will be a certification exam. The candidates will be evaluated on the assignments, final project and the final certification.

How many hours is a student expected work?

This depends on the experience of the participants and also how soon they can grasp the different modules. On an average we have noticed that the participants need to spend double the training hours. Let’s say the training is for 10 hours, then the participants need to spend and additional of 20 hours more. Also, the more the participants spend on a particular piece of software the more the comfortable they will become.