R Programming A-Z™: R For Data Science With Real Exercises!

Learn Programming In R And R Studio. Data Analytics, Data Science, Statistical Analysis, Packages, Functions, GGPlot2
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  • Lectures 76
  • Length 10.5 hours
  • Skill Level All Levels
  • Languages English
  • Includes Lifetime access
    30 day money back guarantee!
    Available on iOS and Android
    Certificate of Completion
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About This Course

Published 2/2016 English

Course Description

Learn R Programming by doing!

There are lots of R courses and lectures out there. However, R has a very steep learning curve and students often get overwhelmed. This course is different!

This course is truly step-by-step. In every new tutorial we build on what had already learned and move one extra step forward.

After every video you learn a new valuable concept that you can apply right away. And the best part is that you learn through live examples.

This training is packed with real-life analytical challenges which you will learn to solve. Some of these we will solve together, some you will have as homework exercises.

In summary, this course has been designed for all skill levels and even if you have no programming or statistical background you will be successful in this course!

I can't wait to see you in class,

Sincerely,

Kirill Eremenko

What are the requirements?

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

What am I going to get from 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

What is the target audience?

  • This course is for you if you want to learn how to program in R
  • This course is for you if you are tired of R courses that are too complicated
  • This course is for you if you want to learn R by doing
  • This course is for you if you like exciting challenges
  • You WILL have homework in this course so you have to be prepared to work on it

What you get with this course?

Not for you? No problem.
30 day money back guarantee.

Forever yours.
Lifetime access.

Learn on the go.
Desktop, iOS and Android.

Get rewarded.
Certificate of completion.

Curriculum

Section 1: Hit The Ground Running
Welcome to the R Programming Course!
Preview
05:46
Installing R and R Studio (MAC & Windows)
07:02
Exercise - Get Excited!
Preview
07:58
BONUS: Using R in The Real World
Article
Section 2: Core Programming Principles
Welcome to this section. This is what you will learn!
01:11
Types of variables
10:10
Using Variables
10:23
Logical Variables and Operators
Preview
07:42
The "While" Loop
07:33
Using the console
04:04
The "For" Loop
05:29
The "If" statement
12:15
Section Recap
05:22
HOMEWORK: Law of Large Numbers
15:13
Core Programming Principles
5 questions
Section 3: Fundamentals Of R
Welcome to this section. This is what you will learn!
01:04
What is a Vector?
04:17
Let's create some vectors
13:12
Using the [] brackets
10:31
Vectorized operations
Preview
05:49
The power of vectorized operations
16:22
Functions in R
17:58
Packages in R
09:19
Section Recap
06:19
HOMEWORK: Financial Statement Analysis
07:42
Fundamentals of R
5 questions
Section 4: Matrices
Welcome to this section. This is what you will learn!
01:10
Project Brief: Basketball Trends
09:22
Matrices
06:30
Building Your First Matrix
13:41
Naming Dimensions
03:34
Colnames() and Rownames()
10:02
Matrix Operations
06:12
Visualizing With Matplot()
11:42
Subsetting
09:20
Visualizing Subsets
04:13
Creating Your First Function
08:47
Basketball Insights
Preview
12:38
Section Recap
06:04
HOMEWORK: Basketball Free Throws
07:13
Matrices
5 questions
Section 5: Data Frames
Welcome to this section. This is what you will learn!
01:47
Project Brief: Demographic Analysis
04:16
Importing data into R
05:52
Exploring your dataset
10:18
Using the $ sign
06:23
Basic operations with a Data Frame
09:47
Filtering a Data Frame
09:04
Introduction to qplot
09:09
Visualizing With Qplot: Part I
Preview
06:22
Building Dataframes
10:02
Merging Data Frames
07:38
Visualizing With Qplot: Part II
06:50
Section Recap
07:19
HOMEWORK: World Trends
06:16
Data Frames
5 questions
Section 6: Advanced Visualization With GGPlot2
Welcome to this section. This is what you will learn!
01:23
Project Brief: Movie Ratings
04:02
Grammar Of Graphics - GGPlot2
Preview
11:26
What is a Factor?
07:13
Aesthetics
06:54
Plotting With Layers
05:18
Overriding Aesthetics
07:49
Mapping vs Setting
08:09
Histograms and Density Charts
07:08
Starting Layer Tips
08:41
Statistical Transformations
10:38
Using Facets
09:30
Coordinates
10:28
Perfecting By Adding Themes
11:04
Section Recap
09:50
HOMEWORK: Movie Domestic % Gross
07:05
Advanced Visualization With GGPlot2
5 questions
Section 7: Homework Solutions
Homework Solution Section 2: Law Of Large Numbers
12:01
Homework Solution Section 3: Financial Statement Analysis
18:35
Homework Solution Section 4: Basketball Free Throws
16:11
Homework Solution Section 5: World Trends
16:30
Homework Solution Section 6: Movie Domestic % Gross (Part 1)
11:41
Homework Solution Section 6: Movie Domestic % Gross (Part 2)
11:09
Section 8: Bonus Tutorials
BoxPlots
13:32
Section 9: Bonus
**CLAIM YOUR EXCLUSIVE BONUS**
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Instructor Biography

Kirill Eremenko, Data Scientist & Forex Systems Expert

My name is Kirill Eremenko and I am super-psyched that you are reading this!

I teach courses in two distinct Business areas on Udemy: Data Science and Forex Trading. I want you to be confident that I can deliver the best training there is, so below is some of my background in both these fields.

Data Science

Professionally, I am a Data Science management consultant with over five years of experience in finance, retail, transport and other industries. I was trained by the best analytics mentors at Deloitte Australia and today I leverage Big Data to drive business strategy, revamp customer experience and revolutionize existing operational processes.

From my courses you will straight away notice how I combine my real-life experience and academic background in Physics and Mathematics to deliver professional step-by-step coaching in the space of Data Science. I am also passionate about public speaking, and regularly present on Big Data at leading Australian universities and industry events.

Forex Trading

Since 2007 I have been actively involved in the Forex market as a trader as well as running programming courses in MQL4. Forex trading is something I really enjoy, because the Forex market can give you financial, and more importantly - personal freedom.

In my other life I am a Data Scientist - I study numbers to analyze patterns in business processes and human behaviour... Sound familiar? Yep! Coincidentally, I am a big fan of Algorithmic Trading :) EAs, Forex Robots, Indicators, Scripts, MQL4, even java programming for Forex - Love It All!

Summary

To sum up, I am absolutely and utterly passionate about both Data Science and Forex Trading and I am looking forward to sharing my passion and knowledge with you!

Instructor Biography

SuperDataScience Team, Helping Data Scientists Succeed

Hi there,

We are the SuperDataScience team. You will find us in the Data Science courses taught by Kirill Eremenko - we are here to help you out with any questions and make sure your journey through the courses is always smooth sailing!

The best way to get in touch is to post a discussion in the Q&A of the course you are taking. In most cases we will respond within 24 hours.

We're passionate about helping you enjoy the courses!

See you in class,

Sincerely,

The Real People at SuperDataScience

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