Regardless of your role in the data science world (or any other industry), it's rare that data will come to you in an easily-understandable format.
Statistics is a mathematical discipline aimed at helping us glean accurate insights from data, so statistical methods are likely to be helpful for analyzing almost any data set you come across.
In our first statistics in R course, we focused on organizing frequency. Now, we're launching a new course that's aimed at helping you understand your data using average and variability measurements.
This new course, Statistics Intermediate in R, is the latest addition to our Data Analyst in R path. If you have a Dataquest subscription already, click the button below to dive directly into the new course and start learning!
(If you don't have a subscription yet, you can sign up for a free account and work through our free interactive R programming courses.)
What Will I Learn in This Course?
This course is designed to build from what you learned in our Statistics Fundamentals in R course, and add average and variability measurements to your statistical arsenal.
The course begins with an examination of the mean, and how the mean can be used to summarize the distribution of a variable.
From there, it moves into related measurements like weighted mean, median, and mode. You'll learn how, and more importantly when, to use these alternative measurements instead of or in addition to the mean to better understand your data.
Then, the course digs into variability measures such as range, mean absolute deviation, variance, and standard deviation. You'll learn to use these tools to measure the variability of any distribution.
Finally, you'll learn about z-scores and how they can be used to standardize a distribution, and you'll practice locating and comparing values using z-scores.
Once you've worked through all of the learning missions, you'll be challenged to put your new stats skills to the test in a guided project in which you'll analyze real estate markets to find the ideal one for a company to advertise in.
Throughout it all, you'll be working with our interactive, in-your-browser coding interface. That means you'll be going hands-on and applying these concepts using R code as you learn them.
For this course, our platform will help you make use of popular R packages like purrr, readr, stringr, dplyr, DescTools, and ggplot2. This mirrors real-world R data science workflows.
By the end of the course, you'll be comfortable summarizing data accurately using concepts like mean and standard deviation. You'll be able to visualize data and identify how variables are spread or clustered. And you'll be confident that you can choose the right average measures to use given the type of data you're working with.
Why Should I Study This?
Understanding the structure of your data is key to almost any analysis project, and the concepts you'll learn in this course will help you do that more completely and accurately.
Even if the ultimate goal of your analysis is something more sophisticated, average and variance measures are generally an important step in the analysis process. Being able to visualize your data with these measures can quickly reveal patterns even in massive, complex data sets.
And while you're probably already aware of concepts like mean, median, and standard deviation, it's crucial to know when and how to apply them correctly to ensure that your results are accurate.
Of course, even if you're already a master of these concepts, the guided project that comes at the end of this course provides a great way to review them all in an applied, real-world setting. And the project you build can go into your project portfolio to help you secure your next job, or help you prove your skills so that you can get that next raise.
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