Last updated 3/2020
MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz
Language: English | Size: 4.35 GB | Duration: 10h 21m
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
Homepage
https://www.udemy.com/course/projects-in-data-science-using-r/
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