
Start by installing R and RStudio, explore the RStudio interface with main windows and tabs, set and check your working directory, and personalize the appearance for focused data science work.
Explore vectors in R, focusing on integers and doubles; learn the L suffix for integer vectors, why doubles are default, and how to inspect objects with type and ls.
Explore how functions in R take multiple arguments and how to inspect them with args, including optional digits in rounding. Learn why naming arguments prevents misalignment.
Build your first R function to draw three cards from a five-character deck using sample with replace, encapsulating deck creation, sampling, and printing the hand.
Use the dim function to turn a vector into a three by four matrix, observe column-major filling, and note how changing dimensions alters the object's class while preserving its type.
Explore categorical variables, levels, and the distinction between ordinal and nominal data with examples like education and ethnicity. Learn how R uses the factor function to create factor variables.
Discover how for loops in R automate tasks by iterating over values with a clear initiation, decision, and body, following the idea 'for every value of x, do y'.
Learn how while loops in R run while a condition is true, updating values to stop when the condition becomes false, for non-fixed iterations and dynamic conditions.
Explore repeat loops in R, learning that the condition comes after execution and a break stops iterations, then compare with for and while loops, and saving results externally.
Explore subsetting data frames in R with tidyverse tools and the Star Wars tibble. Use square-bracket, dollar, and double-bracket indexing, and the combine function to select multiple columns.
Extend a data frame in R by adding columns with dollar sign, double brackets, or cbind. Bind one-row data frames to add rows, and ensure names match when combining.
Detect missing values in R using is.na and any, then replace NAs with unknown or with a column mean or median. Learn to clean data frames and subset results.
Learn how to transform data with the dplyr package in R, applying filtering, selecting, mutating, and arranging by mass, then group by species and summarize with averages.
R Programming for Statistics and Data Science 2023
R Programming is a skill you need if you want to work as a data analyst or a data scientist in your industry of choice. And why wouldn't you? Data scientist is the hottest ranked profession in the US.
But to do that, you need the tools and the skill set to handle data. R is one of the top languages to get you where you want to be. Combine that with statistical know-how, and you will be well on your way to your dream title.
This course is packing all of this, and more, in one easy-to-handle bundle, and it’s the perfect start to your journey.
So, welcome to R for Statistics and Data Science!
R for Statistics and Data Science is the course that will take you from a complete beginner in programming with R to a professional who can complete data manipulation on demand. It gives you the complete skill set to tackle a new data science project with confidence and be able to critically assess your work and others’.
Laying strong foundations
This course wastes no time and jumps right into hands-on coding in R. But don’t worry if you have never coded before, we start off light and teach you all the basics as we go along! We wanted this to be an equally satisfying experience for both complete beginners and those of you who would just like a refresher on R.
What makes this course different from other courses?
Well-paced learning.
Receive top class training with content which we’ve built - and rigorously edited - to deliver powerful and efficient results.
Even though preferred learning paces differ from student to student, we believe that being challenged just the right amount underpins the learning that sticks.
Introductory guide to statistics.
We will take you through descriptive statistics and the fundamentals of inferential statistics.
We will do it in a step-by-step manner, incrementally building up your theoretical knowledge and practical skills.
You’ll master confidence intervals and hypothesis testing, as well as regression and cluster analysis.
The essentials of programming – R-based.
Put yourself in the shoes of a programmer, rise above the average data scientist and boost the productivity of your operations.
Data manipulation and analysis techniques in detail.
Learn to work with vectors, matrices, data frames, and lists.
Become adept in ‘the Tidyverse package’ - R’s most comprehensive collection of tools for data manipulation – enabling you to index and subset data, as well as spread(), gather(), order(), subset(), filter(), arrange(), and mutate() it.
Create meaning-heavy data visualizations and plots.
Practice makes perfect.
Reinforce your learning through numerous practical exercises, made with love, for you, by us.
What about homework, projects, & exercises?
There is a ton of homework that will challenge you in all sorts of ways. You will have the chance to tackle the projects by yourself or reach out to a video tutorial if you get stuck.
You: Is there something to show for the skills I will acquire?
Us: Indeed, there is – a verifiable certificate.
You will receive a verifiable certificate of completion with your name on it. You can download the certificate and attach it to your CV and even post it on your LinkedIn profile to show potential employers you have experience in carrying out data manipulations & analysis in R.
If that sounds good to you, then welcome to the classroom :)