# Data Sciences with R

Learn Data Science using R from scratch. Build your career as a Data Scientist. Explore knitr, buzz dataset, adv methods

Lectures -**36**

Duration -**21.5 hours **

30-days **Money-Back Guarantee**

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## Course Description

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

## Who this course is for:

- Data Scientists
- Anyone aspiring for a career in Data Science and Machine Learning
- Machine Learning Engineers
- R Programmers
- Newbies and Beginners wishing to start their career in R Programming and Data Science
- Data Analysts & Advanced Data Analytics Professionals
- Software Engineers & Developers
- Senior Data Scientists
- Chief Technology Officers (CTOs)
- Statisticians and Data Science Researchers
- Data Engineers
- R Programmers Analytics
- Senior Data Analysts - R, Python Programming
- Data Science Engineers

### Goals

What will you learn in this course:

Learn to program in R at a good level

Learn how to use R Studio

Learn the core principles of programming

Learn how to create vectors in R

Learn how to create variables

Learn about integer, double, logical, character and other types in R

Learn how to create a while() loop and a for() loop in R

Learn how to build and use matrices in R

Learn the matrix() function, learn rbind() and cbind()

Learn how to install packages in R

Learn how to customize R studio to suit your preferences

Understand the Law of Large Numbers

Understand the Normal distribution

Practice working with statistical data in R

Practice working with financial data in R

Practice working with sports data in R

### Prerequisites

What are the prerequisites for this course?

No prior knowledge or experience needed. Only a passion to be successful!

## Curriculum

Check out the detailed breakdown of what’s inside the course

##
Introduction to Data Science with R

1 Lectures

- Introduction to Data Science with R 54:03 54:03

##
Data Collection and Management

1 Lectures

##
Model Deployment and Maintenance

1 Lectures

##
Setting Expectations

1 Lectures

##
Loading Data into R

1 Lectures

##
Exploring Data in Data Science and Machine Learning

1 Lectures

##
Exploring Data using R

1 Lectures

##
Benefits of Data Cleaning

1 Lectures

##
Cross Validation in R

1 Lectures

##
Data Transformation

1 Lectures

##
Modeling Methods

1 Lectures

##
Solving Classification Problems

1 Lectures

##
Working without known Targets

1 Lectures

##
Evaluating Models

1 Lectures

##
Confusion Matrix

1 Lectures

##
Introduction to Linear Regression

1 Lectures

##
Linear Regression in R

2 Lectures

##
Simple and Multiple Regression

1 Lectures

##
Linear and Logistic Regression

1 Lectures

##
Support Vector Machines (SVM) in R

2 Lectures

##
Unsupervised Methods

1 Lectures

##
Clustering in Data Science

1 Lectures

##
K-means Algorithm in R

1 Lectures

##
Hierarchical Clustering

3 Lectures

##
Market Basket Analysis

1 Lectures

##
MBA and Association Rule Mining

1 Lectures

##
Implementing MBA

1 Lectures

##
Association Rule Learning

1 Lectures

##
Decision Tree Algorithm

1 Lectures

##
Exploring Advanced Methods

1 Lectures

##
Using Kernel Methods

1 Lectures

##
Documentation and Deployment

1 Lectures

## Instructor Details

Uplatz

Uplatz is UK-based leading IT Training provider serving students across the globe. Our uniqueness comes from the fact that we provide online training courses at a fraction of the average cost of these courses in the market.

Over a short span of 3 years, Uplatz has grown massively to become a truly global IT training provider with a wide range of career-oriented courses on cutting-edge technologies and software programming.

## Course Certificate

User your certification to make a career change or to advance in your current career. Salaries are among the highest in the world.

## Our students work

with the Best

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