Coupon & course info
Course Name: Introduction To Data Science
Subtitle: Use the R Programming Language to execute data science projects and become a data scientist.
Instructor: Taught by Nina Zumel, Data Scientist, Win-Vector LLC
Subcategory: Data & Analytics
Provided by: Udemy
Price: $149 (before discount)
Free coupon code: See above (no charge for coupon)
Review info & popularity
As of March 9, 2016…
Students: 2431 students enrolled
Ratings: 36 reviews
Rank: ranked #160 in Udemy Business Courses
Brief course description
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.
(Read more about this course on the official course page.)
Nina Zumel bio
Nina Zumel, PhD, has over 10 years of experience in research, machine learning, and data science. She is a co-author of the popular book Practical Data Science with R, co-author of the EMC data scientist certification program, and blogs often on statistics, data science, and data visualization.
(Learn more about this instructor on the official course page.)
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Final details for this Udemy course
Skill level: Intermediate Level
Lectures: 28 lessons
Duration: 6 Hours of video
What you get: Start and execute the steps of a data science project, from project definition to model evaluation.
Target audience: The course is for analytically minded students who are looking for an introduction to applied predictive modeling methods, and who want to learn about what goes into successful data science projects. 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. Some familiarity with R is helpful; otherwise, students should be willing to learn R as they go. We will direct you to ready-to-go implementations and additional references throughout the course.
Requirements: You should be familiar with basic scripting or programming, and basic statistics.
Access: Lifetime access
Peace of mind: 30 day money back guarantee
Availability: available online, as well as on iOS and Android
Download options: check course to see if you can download lessons