Skip to main content

Disclaimer

 

Disclaimer for DatSci Hub

If you require any more information or have any questions about our site's disclaimer, please feel free to contact us by email at datsci.hub@gmail.com.

Disclaimers for DATA SCIENCE SPECIALIZATION: LEARN ONLINE WITH FREE R COURSES & LECTURES

All the information on this website - https://datscihub.blogspot.com/ - is published in good faith and for general education purpose only. DATA SCIENCE SPECIALIZATION: LEARN ONLINE WITH FREE R COURSES & LECTURES does not make any warranties about the completeness, reliability and accuracy of this information. Any action you take upon the information you find on this website (DATA SCIENCE SPECIALIZATION: LEARN ONLINE WITH FREE R COURSES & LECTURES), is strictly at your own risk. DATA SCIENCE SPECIALIZATION: LEARN ONLINE WITH FREE R COURSES & LECTURES will not be liable for any losses and/or damages in connection with the use of our website.

From our website, you can visit other websites by following hyperlinks to such external sites. While we strive to provide only quality links to useful and ethical websites, we have no control over the content and nature of these sites. These links to other websites do not imply a recommendation for all the content found on these sites. Site owners and content may change without notice and may occur before we have the opportunity to remove a link which may have gone 'bad'.

Please be also aware that when you leave our website, other sites may have different privacy policies and terms which are beyond our control. Please be sure to check the Privacy Policies of these sites as well as their "Terms of Service" before engaging in any business or uploading any information.

Consent

By using our website, you hereby consent to our disclaimer and agree to its terms.

Update

Should we update, amend or make any changes to this document, those changes will be prominently posted here.

Comments

Post a Comment

Type your comment here.
However, Comments for this blog are held for moderation before they are published to blog.
Thanks!

Popular posts from this blog

Debugging Your R Code: Indications and Best Practices

The Beginner’s Guide to Debugging Tools: As with any programming language, it's important to debug your code in R to ensure it is functioning correctly. Here are some indications that there may be something wrong with your R code, along with examples of common mistakes that can cause these issues: Error messages:   If R encounters an error in your code, it will often provide an error message indicating the source of the problem. For example, if you forget to close a parenthesis, you may get an error message like "Error: unexpected ')' in 'my_function'". Here, R is indicating that there is a syntax error in your function. Unexpected output:  If the output of your code is unexpected or doesn't match your expectations, there may be an issue with your code. For example, if you are trying to calculate the mean of a vector of numbers, but the output is much higher or lower than expected, there may be an issue with the code you used to calculate the mean. L...
Post a Comment
Read more

Getting Started with R Programming

The Beginner’s Guide to R Programming. I'm very excited to start R Programming and I hope you are too. This is the second course in the Data Science Specialization and it focuses on the nuts and bolts of using R as a programming language. The recommended background for this course is the course The Data Scientist's Toolbox . It is possible to take this class concurrently with that class but you may have to read ahead in the prerequisite class to get the relevant background for this class. For a complete set of course dependencies in the Data Science Specialization please see the course dependency chart , that has been posted on our blogpost. The primary way to interact with me and the other students in this course is through the discussion forums which in our case are comments section under the lectures, social media and blogpost . Here, you can start new threads by asking questions or you can respond to other people's questions. If you have a question about any aspect...
Post a Comment
Read more

Mastering R Programming: Best Coding Practices for Readable and Maintainable Code

The Beginner’s Guide to Coding Standards: When it comes to programming, writing code that is easy to read and maintain is just as important as writing code that works. This is especially true in R programming, where it's common to work with large datasets and complex statistical analyses. In this blog post, we'll go over some coding standards that you should follow when writing R code to ensure that your code is easy to read and maintain . Indenting One of the most important coding standards to follow is to use consistent indenting. Indenting makes your code more readable by visually indicating the structure of your code. In R programming, it's common to use two spaces for each level of indentation. For example: if (x > y) {   z <- x + y } else {   z <- x - y } Column Margins Another important coding standard is to use consistent column margins. This means that you should avoid writing code that extends beyond a certain number of characters (often 80 or 100). Th...
Post a Comment
Read more