Introduction To Data Science

Win-Vector LLC’s Nina Zumel and John Mount are proud to announce their new data science video course Introduction to Data Science (50% off) is now available on Udemy (click here for 50% off).


Data Science 2

We designed the course as an introduction to an advanced topic. The course description is:

Use the R Programming Language to execute data science projects and become a data scientist. Implement business solutions, using machine learning and predictive analytics.

The R language provides a way to tackle day-to-day data science tasks, and this course will teach you how to apply the R programming language and useful statistical techniques to everyday business situations.

With this course, you’ll be able to use the visualizations, statistical models, and data manipulation tools that modern data scientists rely upon daily to recognize trends and suggest courses of action.

Understand Data Science to Be a More Effective Data Analyst

  • Use R and RStudio
  • Master Modeling and Machine Learning
  • Load, Visualize, and Interpret Data

Use R to Analyze Data and Come Up with Valuable Business Solutions

This course is designed for those who are analytically minded and are familiar with basic statistics and programming or scripting. Some familiarity with R is strongly recommended; otherwise, you can learn R as you go.

You’ll learn applied predictive modeling methods, as well as how to explore and visualize data, how to use and understand common machine learning algorithms in R, and how to relate machine learning methods to business problems.

All of these skills will combine to give you the ability to explore data, ask the right questions, execute predictive models, and communicate your informed recommendations and solutions to company leaders.

Contents and Overview

This course begins with a walk-through of a template data science project before diving into the R statistical programming language.

You will be guided through modeling and machine learning. You’ll use machine learning methods to create algorithms for a business, and you’ll validate and evaluate models.

You’ll learn how to load data into R and learn how to interpret and visualize the data while dealing with variables and missing values. You’ll be taught how to come to sound conclusions about your data, despite some real-world challenges.

By the end of this course, you’ll be a better data analyst because you’ll have an understanding of applied predictive modeling methods, and you’ll know how to use existing machine learning methods in R. This will allow you to work with team members in a data science project, find problems, and come up solutions.

You’ll complete this course with the confidence to correctly analyze data from a variety of sources, while sharing conclusions that will make a business more competitive and successful.

The course will teach students how to use existing machine learning methods in R, but will not teach them how to implement these algorithms from scratch. Students should be familiar with basic statistics and basic scripting/programming.

The course has a different emphasis than our book Practical Data Science with R and does not require the book.

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Most of the course materials are freely available from GitHub in the form of pre-prepared knitr workbooks.

We also have free example lecture from the course here.



4 thoughts on “Introduction To Data Science”

    1. Unfortunately we don’t have an instructors packet for the book (slides, exercises). We do have a paid-view Video course that covers a lot of related topics (“Introduction to Data Science” available through Udemy), but I assume that isn’t quite what you are looking for.

    1. Not at this time. We are pretty happy with how it stands up. The main thing we would change is adding use of magrittr/dplyr, but that would be one more thing to teach (at some point you do need to know standard base-R) and frankly wasn’t a great teaching option prior to “R for Data Science.” Perhaps we will translate all the examples at some point so there are side-by side applications of base-R and tidyverse, but that would likely end up being a free extra we distribute.

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