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 self-study 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 full-stack 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, end-to-end 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 end-to-end 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
CURRICULUM
About this Course
About V2 Maestros
Advanced Topics
Analyzing Results and Errors
Artificial Neural Networks and Support Vector Machines
Association Rules Mining
Bagging and Boosting
Best Practices and Guidance
Closing Remarks
Conclusion
Correlations
Data Acquisition
Data Cleansing
Data Engineering
Data Frames and Matrices
Data Manipulation and I/O Operations
Data Science Life Cycle
Data Science Life Cycle - Analysis and Production
Data Science Life Cycle - Data Engineering
Data Science Life Cycle - Setup
Data Transformations
Decision Trees
Dimensionality Reduction
Graphics in R
Introduction
K-means Clustering
Linear Regression
Machine Learning and Predictive Analysis
Naive Bayes Classification
Programming and Packages
R Code Examples - 1
R Code Examples - 2
R Code Examples - 3
R Code Examples - 4
R Code Examples - 5
R Examples for Data Engineering
R Language Basics
R Programming
R Studio - Walkaround
R Use Case : Advanced Methods - Medical Practice
R Use Case : Association Rules Mining - Public Safety
R Use Case : Decision Trees - Life Sciences
R Use Case : K-means Clustering - Automobiles
R Use Case : Linear Regression - Automobiles
R Use Case : Naive Bayes - Information Technology
R Use Case : Random Forests -Financial Services
Random Forests
Statistical Distributions
Statistics for Data Science
Statistics in R
Summary Statistics
Text Pre-Processing TF-IDF
Types of Analytics
Types of Data
Types of Learning
Use Cases for Data Science
Vectors and Lists
What is Data Science - Part 1
What is Data Science - Part 2
What is Data Science - Part 3
What is Data Science - Part 4
What is Data Science?
LINK FOR THE FREE COURSE
