A warm welcome to the Data Science with R course by Uplatz.

Data Science includes various fields such as mathematics, business insight, tools, processes and machine learning techniques. A mix of all these fields help us in discovering the visions or designs from raw data which can be of major use in the formation of big business decisions. As a Data scientist it’s your role to inspect which questions want answering and where to find the related data. A data scientist should have business insight and analytical services. One also needs to have the skill to mine, clean, and present data. Businesses use data scientists to source, manage, and analyze large amounts of unstructured data.

R is a commanding language used extensively for data analysis and statistical calculating. It was developed in early 90s. R is an open-source software. R is unrestricted and flexible because it’s an open-source software. R’s open lines permit it to incorporate with other applications and systems. Open-source soft wares have a high standard of quality, since multiple people use and iterate on them. As a programming language, R delivers objects, operators and functions that allow employers to discover, model and envision data. Data science with R has got a lot of possibilities in the commercial world. Open R is the most widely used open-source language in analytics. From minor to big initiatives, every other company is preferring R over the other languages. There is a constant need for professionals with having knowledge in data science using R programming.

Uplatz provides this comprehensive course on Data Science with R covering data science concepts implementation and application using R programming language.

Data Science with R - Course Syllabus

1. Introduction to Data Science

• 1.1 The data science process

• 1.2 Stages of a data science project

• 1.3 Setting expectations

• 1.4 Summary

  
  

2. Loading Data into R

• 2.1 Working with data from files

• 2.2 Working with relational databases

• 2.3 Summary

  
  

3. Managing Data

• 3.1 Cleaning data

• 3.2 Sampling for modeling and validation

• 3.3 Summary

  
  

4. Choosing and Evaluating Models

• 4.1 Mapping problems to machine learning tasks

• 4.2 Evaluating models

• 4.3 Validating models

• 4.4 Summary

  
  

5. Memorization Methods

• 5.1 Using decision trees 127

• 5.2 Summary

  
  

6. Linear and Logistic Regression

• 6.1 Using linear regression

• 6.2 Using logistic regression

• 6.3 Summary

  
  

7. Unsupervised Methods

• 7.1 Cluster analysis

• 7.2 Association rules

• 7.3 Summary

  
  

8. Exploring Advanced Methods

• 8.1 Using bagging and random forests to reduce training variance

• 8.2 Using generalized additive models (GAMs) to learn nonmonotone relationships

• 8.3 Using kernel methods to increase data separation

• 8.4 Using SVMs to model complicated decision boundaries

  
  

9. Documentation and Deployment

• 9.1 The buzz dataset

• 9.2 Using knitr to produce milestone documentation