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Data Analytics & Machine learning with R Online Training

This course focuses mainly on imparting knowledge about learning from the data and possibilities of analytics and machine learning techniques.A complete hands-on approach to learning the art of Data analytics in both business analytics and data science context using R and Hadoop.This course will draw a roadmap for one to become a Data Scientist.

What is R Language?

R is a widely used open-source statistical software which has a huge community and a leading platform for data analytics and machine learning.

Concepts and Tools covered

Basics of R, Exploratory data Analytics in R, Visualisation in R, building web-based applications and visualization in Shiny web app, supervised and unsupervised Machine learning techniques in R.

Methodology

The Course content is handcrafted by Experts and it comprises of Presentation slides, Quizzes & Assignments for each Module, Class recording can be accessed in LMS. We would be using a lot of Industry specific use cases and datasets to make the learner job ready.

Target Audience

This course is recommended for freshers who are looking to start their career in Data Analytics, Professionals who want to learn analytics and machine learning to change their career to data science and anybody who wants to learn and start a career in building solutions based on data analytics and machine learning can choose this course.

We also conduct Data Analytics & Machine learning with R Classroom training in Bengaluru

  • Introduction to Data Science and some use cases 

    This module lets you know about how Data Science is a wider perspective of Business Analytics

    0/3
    • Understand what is Data Science
    • Understand how Data science is applied in different domains
    • Understand some use cases
  • Data manipulation and exploratory data analytics 

    0/2
    • Learn to juggle data using R and do EDA
    • Understand different data analysis techniques and learn to visualize the data
  • Data Import Techniques in R 

    This module tells you about the robustness of R in terms of importing any file formats, datasets from different sources

    0/3
    • Creating Data frames
    • Importing datasets from different databases and local drives
    • Uploading CSV, TSV, Excel, JSON files and delimited files to R
  • Deep Dive into Shiny Web App 

    In this module, you will learn to build web applications and dashboards using Shiny App

    0/3
    • Understand Shiny web application
    • Build a shiny based web app
    • Learn to build dashboards in shiny and host it in web
  • Introduction to Machine Learning 

    This module lets you know about the various Machine Learning algorithms. The two Machine Learning types are Supervised Learning and Unsupervised Learning and the difference between the two types

    0/2
    • Understand difference between supervised and unsupervised learning
    • Learn about different clustering, classification techniques
  • Linear and Logistic Regression 

    learn ‘Regression Techniques’ Linear and logistic regression is explained from the very basics with the examples and it is implemented in R using two case studies dedicated to each type of Regression discussed

    0/2
    • Understand difference between Linear and Logistic regression
    • Implement regression techniques on two different datasets
  • Clustering Techniques 

    Learn about unsupervised learning and it’s different clustering techniques

    0/3
    • Understand and implement K-means
    • Understand and implement K medoids
    • Understand and implement K nearest and hierarchical clustering
  • Decision Trees and Random Forest 

    It covers the concepts of Decision Trees and Random Forest. The Algorithm for the creation of trees and forests is discussed in a step wise approach and explained with examples. At the end of the class, these are the concepts implemented on a real-life data set. The case studies are present in the LMS

    0/2
    • Learn about decision trees
    • Implement decision tree and random forest on a dataset and then visualize the data
  • Text Mining 

    To understand Text mining algorithms and sentiment analysis using it

    0/1
    • Text mining algorithms
  • Deep Learning 

    Learn about artificial neural networks and deep learning

    0/2
    • Artificial Neural network
    • Deep neural
  • Proof of concept project 

    Working on Sample data set to demonstrate the techniques learned throughout the workshop

    0/1
    • Demonstrate ability to apply analytics and machine learning techniques

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