Projects In Data Science Using R

The practical guide to master real world Data science building projects

What you'll learn

Learn the fundamentals of R programming

Learn the core concepts of Data science

Learn data concepts building real world projects

Requirements

Basic knolwedge of R programming will be helpful for completion of the course

Description

Data Science is a multi-disciplinary field that uses scientific methods, processes, algorithms and systems for gaining insights by analyzing the structured or unstructured data. Basically, it helps in finding hidden patterns from the raw data by using technologies like R, Hadoop, Machine Learning and others.With its use from the healthcare  to retail, it has one of the greatest potentials to change numerous sectors to its entirety. Similar to the rise of data in recent years, the demands of data scientists have also exploded with average salaries being offered up to $110,000 depending upon the locality.Why you should learn Data Science?Desired in different fields like business, healthcare, finance and othersIn order to perform complicated data analysisTo find the hidden patterns by data manipulationFor making precise predictionsWhy you should take this course?The regular need for storing, modifying and analyzing data have made data science one of the most important field. From big to small companies, all are in a constant search for the data scientists or the individuals who understand and can work with a huge pool of data. Knowing all these facts, we have designed this comprehensive online tutorial which will help you in building different real-world projects. This tutorial with over 5 hours of videos will be sufficient enough to make you explain different aspects of data science in the most simplest, easiest and practical way.Projects covered in the course :Data Transformations on Iris DatasetProject on Wide and Long DataPerforming Joins on DatasetsProject on Facets, Geoms and TansformationsTake this course for building different real-world projects in Data Science which has great potential in the world ruled by data.

Overview

Section 1: Introduction to Data Science Using R

Lecture 1 Introduction

Lecture 2 Intro to R studio

Lecture 3 The Assignment Operator

Lecture 4 Basic Data Types in R

Lecture 5 Vectors

Lecture 6 Matrices and Data Frames

Lecture 7 Subsetting Syntax

Lecture 8 Project 1 : Introduction to R - Problem Statement

Lecture 9 Project 1 Solution

Section 2: Data Transformation

Lecture 10 Data Transformations on Rows

Lecture 11 Data Transformations on Columns

Lecture 12 Data Transformations on Iris Dataset - Project Problem Statement

Lecture 13 Data Transformations on Iris Dataset - Project Solution

Lecture 14 Wide and Long Data

Lecture 15 Grouped Transposes

Lecture 16 Project 2 : Wide and Long Data - Problem statement

Lecture 17 Project 2 Solution

Lecture 18 What are Joins

Lecture 19 Programming Joins Part 1

Lecture 20 Programming Joins Part 2

Lecture 21 Project 3 :Performing Joins - Problem Statement

Lecture 22 Project 3 Solution

Section 3: Data Visualization

Lecture 23 GGPLOT Basics

Lecture 24 Aesthetic Mappings in GGPLOT

Lecture 25 Facets in GGPLOT

Lecture 26 Geoms in GGPLOT

Lecture 27 Statistical Transformations in GGPLOT

Lecture 28 Project 4 : GGPLOT - Problem Statement

Lecture 29 Project 4 Solution

Lecture 30 Project 5: Facets, Geoms and Tansformations

Lecture 31 Project 5 Solution

Section 4: Exploratory Data Analysis

Lecture 32 How to Identify Missing Values

Lecture 33 How to Identify Outliers

Lecture 34 What to do with Missing Values and Outliers

Lecture 35 Functional Transformations

Section 5: Regression Models

Lecture 36 Intro to Regression Problem and Data Set

Lecture 37 Exploratory Data Analysis

Lecture 38 Correlations and Final Data Set

Lecture 39 What is Multiple Regression

Lecture 40 Building a Multiple Regression Model

Lecture 41 Measuring Regression Model Accuracy

Section 6: KNN Model

Lecture 42 What is KNN

Lecture 43 Building a KNN Model

Lecture 44 Assessing KNN Model Performance

Lecture 45 Assessing Training and Test Error for KNN

Lecture 46 What is a Decision Tree

Lecture 47 Creating a Decision Tree

Lecture 48 Assessing Performance of a Decision Tree

Lecture 49 Model Comparison

Lecture 50 Project: Build a model that is better than our multiple regression and KNN model

Section 7: Classification Dataset

Lecture 51 Intro to Classification Dataset and Problem

Lecture 52 EDA Part 1

Lecture 53 EDA Part 2

Lecture 54 What is Logistic Regression

Lecture 55 Building a Logistic Regression Model

Lecture 56 Building a Classification Tree

Lecture 57 Building a Random Forest

Lecture 58 Project: Build a model better than logistic regression, decision and RF model

Anyone who wants to learn R programming and fundamentals of Data Science will find this course very useful