R For Data Science With Real Exercises!
Data science is the field of study that combines domain expertise, programming skills, and knowledge of mathematics and statistics to extract meaningful insights from data. Data science professionals apply machine learning algorithms to numbers, text, images, video, audio, and more to produce artificial intelligence (AI) systems to perform tasks that ordinarily require human intelligence. In turn, these systems generate insights that analysts and business users can translate into tangible business value.
“Learning from data is virtually universally useful. Master it and you will be welcomed anywhere”.
– John Elder, Elder Research
Day by day more and more companies are coming to realize the importance of data science, AI, and machine learning. Regardless of industry or size, organizations that wish to remain competitive in the age of big data need to efficiently develop and implement data science capabilities or risk being left behind.
Why use R for Data Science?
1. R is open-source and free
- R is free to download
2. R is popular – and increasing in popularity
- IEEE publishes a list of the most popular programming languages each year. R was ranked 5th in 2016, up from 6th in 2015. This not only shows the increasing interest in R as a programming language but also of the fields like Data Science and Machine Learning where R is commonly used.
3. R runs on all platforms
- You can find distributions of R for all popular platforms – Windows, Linux and Mac.R code that you write on one platform can easily be ported to another without any issues.
4. Learning R will increase your chances of getting a job
- According to the Data Science Salary Survey conducted by O’Reilly Media in 2014, data scientists are paid a median of $98,000 worldwide. The figure is higher in the US – around $144,000.Of course, knowing how to write R programs won’t get you a job straight away, a data scientist has to juggle a lot of tools to do their work. Even if you are applying for a software developer position, R programming experience can make you stand out from the crowd.
5. R is being used by the biggest tech giants
- Adoption by tech giants is always a sign of a programming language’s potential. Today’s companies don’t make their decisions on a whim. Every major decision has to be backed by a concrete analysis of data.
About this Course
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! In this course, you will find step-by-step solutions.
After every video, you will 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 you 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!
Who this course is for:
- 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
The course on R for data science is not only exciting, but also at the same time, it dives deep into Machine Learning. It is structured the following way.
1. Core Programming Principles
- You will learn Types of variables.
- Logical Variables and Operators.
- Using a while loop, for loop, IF statement including core programming principles.
2. Fundamentals of R
- Will learn about vectors and usage of brackets
- The power of vectorized operations
- Functions and packages in R
- You will build your First Matrix
- Naming Dimensions and Colnames() and Rownames()
- Matrix Operations and Visualizing With Matplot()
- Subsetting and Visualizing Subsets
4. R for Data Science: Data Frames
- Importing data into R and Exploring your data set
- Basic operations with a Data Frame and Filtering a Data Frame
- Introduction to qplot
- Building Data frames and Merging Data Frames
5. R for Data Science: Advanced Visualization with GGplot2
- You will learn about Factor, Aesthetics, Plotting With Layers, Overriding Aesthetics.
- Mapping vs Setting and Histograms and Density Charts.
- Starting Layer Tips, Statistical Transformations, Using Facets, Coordinates, Perfecting By Adding Themes and Advanced Visualization With GGPlot2
6. Homework Solutions
In this section, you will go through different Homework Solution Sections such as:
- Law Of Large Numbers
- Financial Statement Analysis
- Basketball Free Throws
- World Trends
- Movie Domestic % Gross (Part 1)
- Movie Domestic % Gross (Part 2)
Along with this, you will have one bonus tutorial including videos.
What you will learn from this course on R for data science
- You will learn to program in R at a good level, how to use R Studio, the core principles of programming, how to create vectors in R, how to create variables.
- About integer, double, logical, character, and other types in R.
- How to create a while() loop and a for() loop in R and how to build and use matrices in R.
- The matrix() function, rbind() and cbind().
- how to install packages in R.
- How to customize R studio to suit your preferences.
- Understand the Law of Large Numbers and Normal distribution.
- Practice working with statistical data in R and financial data in R and sports data in R.
Note: Your review matters
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- Commencement from basic maths.
- The course is very interactive and expects you to code as you go.
- Course contains brief and easily understandable content.
- Excellent course for beginners to understand everything in R Programming.
- This course needs to have in-depth exercises.
- Course should include datasets and exercises practical enough to the real world.
- Needs explaination on mathematics behind each algorithm.
Specification: R Programming A-Z: R For Data Science With Real Exercises!