Applied Data Science with R
Course Description
“Data Science is the sexiest job of the 21st century – It has exciting work and incredible pay”.
Learning Data Science though is not an easy task. The field traverses through Computer Science, Programming, Information Theory, Statistics and Artificial Intelligence. College/University courses in this field are expensive. Becoming a Data Scientist through selfstudy is challenging since it requires going through multiple books, websites, searches and exercises and you will still end up feeling “not complete” at the end of it. So how do you acquire fullstack Data Science skills that will get you a and give you the confidence to execute it?
Applied Data Science with R addresses the problem. This course provides extensive, endtoend coverage of all activities performed in a Data Science project. If teaches application of the latest techniques in data acquisition, transformation and predictive analytics to solve real world business problems. The goal of this course is to teach practice rather than theory. Rather than deep dive into formulae and derivations, it focuses on using existing libraries and tools to produce solutions. It also keeps things simple and easy to understand.
Through this course, we strive to make you fully equipped to become a developer who can execute full fledged Data Science projects. By taking this course, you will
Appreciate what Data Science really is
Understand the Data Science Life Cycle
Learn to use R for executing Data Science Projects
Master the application of Analytics and Machine Learning techniques
Gain insight into how Data Science works through endtoend use cases.
By becoming a student of V2 Maestros, you will also get maximum discounts on all of our other current and future courses (coupon codes inside the course material). You will also get prompt support of all your queries and questions. We continuously strive to improve our course material to reflect the latest trends and technologies
What are the requirements?
Programming Experience in at least one language like Java, C/C++/C#, Python, Perl
Experience in analyzing Data preferred
What am I going to get from this course?
Appreciate what Data Science really is
Understand the Data Science Life Cycle
Learn to use R for executing Data Science Projects
Master the application of Analytics and Machine Learning techniques
Gain insight into how Data Science works through endtoend use cases.
What is the target audience?
IT Professionals aspiring to be Data Scientists
Students who want to learn about Data Science domain
Statisticians and Project Managers who want to expand their horizon into Data Science
Curriculum
Section 1: Introduction  

Lecture 1 
About this Course

08:12  
Lecture 2 
About V2 Maestros

01:39  
Lecture 3 
Resource Bundle

Article  
Section 2: What is Data Science?  
Lecture 4 
Basic Elements of Data Science

11:51  
Lecture 5 
The Dataset

10:44  
Lecture 6 
Learning from relationships

12:55  
Lecture 7 
Modeling and Prediction

09:31  
Lecture 8 
Use Cases for Data Science

07:47  
Section 3: Data Science Life Cycle  
Lecture 9 
Stage 1 – Setup

11:46  
Lecture 10 
Stage 2 – Data Engineering

11:57  
Lecture 11 
Stage 3 & 4 – Analysis and Production

19:16  
Section 4: Statistics for Data Science  
Lecture 12 
Types of Data

07:29  
Lecture 13 
Summary Statistics

16:10  
Lecture 14 
Statistical Distributions

19:05  
Lecture 15 
Correlations

10:09  
Section 5: R Programming  
Lecture 16 
Downloading and Installing R and R Studio

Article  
Lecture 17 
R Studio – Walkaround

06:40  
Lecture 18 
R Language Basics

12:04  
Lecture 19 
Vectors and Lists

08:51  
Lecture 20 
Data Frames and Matrices

14:41  
Lecture 21 
Data Manipulation and I/O Operations

10:30  
Lecture 22 
Programming and Packages

12:41  
Lecture 23 
Statistics in R

03:01  
Lecture 24 
Graphics in R

06:51  
Lecture 25 
R Code Examples – Variables and Vectors

16:18  
Lecture 26 
R Code Examples – Data Frames and Matrices

15:05  
Lecture 27 
R Code Examples – Programming Elements

17:18  
Lecture 28 
R Code Examples – Statistics and Base Plotting System

17:29  
Lecture 29 
R Code Examples – ggplot

17:22  
Section 6: Data Engineering  
Lecture 30 
Data Acquisition

16:01  
Lecture 31 
Data Cleansing

10:50  
Lecture 32 
Data Transformations

11:09  
Lecture 33 
Text PreProcessing TFIDF

14:53  
Lecture 34 
R Examples for Data Engineering

11:14  
Section 7: Machine Learning and Predictive Analysis  
Lecture 35 
Types of Analytics

12:08  
Lecture 36 
Types of Learning

17:16  
Lecture 37 
Analyzing Results and Errors

13:46  
Lecture 38 
Linear Regression

19:00  
Lecture 39 
R Use Case : Linear Regression

18:01  
Lecture 40 
Decision Trees

10:42  
Lecture 41 
R Use Case : Decision Trees

19:36  
Lecture 42 
Naive Bayes Classification
Preview 
19:21  
Lecture 43 
R Use Case : Naive Bayes

19:12  
Lecture 44 
Random Forests

10:31  
Lecture 45 
R Use Case : Random Forests

18:47  
Lecture 46 
Kmeans Clustering

11:53  
Lecture 47 
R Use Case : KMeans clustering

16:24  
Lecture 48 
Association Rules Mining

11:31  
Lecture 49 
R Use Case : Association Rules Mining

13:11  
Section 8: Advanced Topics  
Lecture 50 
Artificial Neural Networks and Support Vector Machines

04:35  
Lecture 51 
Bagging and Boosting

11:27  
Lecture 52 
Dimensionality Reduction

07:28  
Lecture 53 
R Use Case : Advanced Methods

17:18  
Section 9: Conclusion  
Lecture 54 
Closing Remarks

03:35  
Lecture 55 
BONUS Lecture : Other courses you should check out

Article 
Instructor Biography
V2 Maestros, Big Data Science / Analytics Experts  10K+ students
V2 Maestros is dedicated to teaching big data / data science at affordable costs to the world. Our instructors have real world experience practicing big data and data science and delivering business results. Big Data Science is a hot and happening field in the IT industry. Unfortunately, the resources available for learning this skill are hard to find and expensive. We hope to ease this problem by providing quality education at affordable rates, there by building data science talent across the world.
Course Features
 Lectures 0
 Quizzes 0
 Duration 50 hours
 Skill level All level
 Language English
 Students 3469
 Assessments Self