Course: Data Analysis and Visualization in R

Data Analysis and Visualization in R

  • Life Time Access
  • Certificate on Completion
  • Access on Android and iOS App
About this Course

What is it?

Data Science for Professionals is simply the best way to gain a in-depth and practical skill set in data science. Through a combination of theory and hands-on practice, course participants will gain a solid grasp of how to manage, manipulate, and visualize data in R - the world's most popular data science language.

Who should take this course?

This course is for professionals who are tired of using spreadsheets for analysis and have a serious interest in learning how to use code to improve the quality and efficiency of their work. At the end of this course, participants will have a developed a solid foundation of the fundamentals of the R language. Participants will have also gained a perspective on the modern data science landscape and how they can use R not only to better analyze data, but also to better manage projects, create interactive presentations, and collaborate with other teams. Whether it's spreadsheets, text documents, or slides, anyone who analyzes, reports, or presents data will benefit from a knowledge of data science programming.

Who should NOT take this course?

While this course covers examples of machine learning in later lectures, this is not a machine learning or a statistics-focused course. The course does go through examples of how to use code to deploy and assess different types of models, including machine learning algorithms, but it does so from a coding perspective and not a statistics perspective. The reason is that the math behind most machine learning algorithms merits a course entirely on its own. There are many courses out there that make dubious claims of easy mastery of machine learning and deep learning algorithms - this is not one of those courses.

A Different kind of data science course

This course is different from most other courses in several ways:

  • We use very large, real-world examples to guide our learning process. This allows us to tie-together the various aspects of data science in a more intuitive, easy-to-retain manner.
  • We encounter and deal-with various challenges and bugs that arise from imperfect data. Most courses use ideal datasets in their examples, but these are not common in the real-world, and solving data-related issues is usually the most difficult and time-consuming part of data science.
  • We are focused on your long-term success. Our downloadable course code is filled with notes and guidance aimed at making the transition from learning-to-applying as smooth as possible.

Who this course is for:

  • Anyone who collects, analyses, reports, or presents data. So pretty much everyone
  • Anyone who is tired of spreadsheets. Again, pretty much everyone
  • Anyone who wants to add a lot of value to their skill set and is willing to invest a few hours per week
Basic knowledge
  • No prior coding knowledge required
What you will learn
  • Students will be able to analyze, manipulate, explore, illustrate, and report data in ways that will set them far apart from those who use spreadsheets and other traditional Office products
Number of Lectures: 32
Total Duration: 06:31:24
  • Course Goals and the Data Science Process  

    Welcome to Data Science for Professionals! This course revolves around a single, very large problem: A company is worried about employee attrition (losing employees). The company has collected data on its employees and would like to know which factors are most important in determining whether someone stays or leaves the company. They would also like a predictive model that will allow them to predict which high-value employees are likely to leave in the future. We will learn R programming to solve this problem. By the end of this course, you should be comfortable using R in a way that's practical to your work or study. 

  • Why Use R?  
  • A Quick Overview of the R Language  
  • Downloading and Installing R  
  • RStudio and Project Setup  
R Essentials - Data Objects
  • Section Overview  
  • Vectors - Part 1  
  • Getting Help with R  
  • Vectors - Part 2  
  • Vectors - Part 3  
  • Vectors - Part 4  
  • Matrices  
  • Data Frames  
  • Lists  
  • Data Object Recap  
R Essentials - Functions and Loops
  • Loops and IF Statements  
  • Custom Functions  
R Essentials - Putting it all Together!
  • The Challenge  
  • The Solution  
Data Gymnastics
  • Tidy Data  
  • Tidying our Data with tidyr  
  • Data Manipulation with dplyr - Part 1  
  • Data Manipulation with dplyr - Part 2  
  • Data Manipulation with dplyr - Part 3  
Data Visualization
  • Making Graphics Easy with ggplot2 - Part 1  
  • Making Graphics Easy with ggplot2 - Part 2  
Modelling and Machine Learning
  • What is Machine Learning?  
  • Training and Testing  
  • Inference Trees and Random Forests  
  • Conclusion to the HR Attrition Problem  
Advanced Reporting with R
  • RMarkdown and Git Version Control  
  • Shiny Web Apps  
Reviews (0)