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

Popular posts from this blog

Mastering R Data Types: Matrices, Factors, Missing Values, Data Frames, and Names Attribute

The Beginner’s Guide to R Data Types: R is a programming language that is widely used for data analysis and statistical computing. It has a powerful set of data structures, including vectors, lists, and data frames, that allow users to work with data in a flexible and efficient way. Matrices A matrix is a two-dimensional array in R that can contain elements of any data type. You can create a matrix using the matrix() function. For example: # Create a matrix with 3 rows and 2 columns  my_matrix <- matrix(c(1, 2, 3, 4, 5, 6), nrow = 3, ncol = 2) Factors A factor is a type of variable in R that represents categorical data. Factors are stored as integers, where each integer corresponds to a level of the factor. You can create a factor using the factor() function. For example: # Create a factor with three levels: "low", "medium", "high"  my_factor <- factor(c("low", "high", "medium", "high", "low")) Missin...

Installing R on Windows and MAC Operating System

The Beginner’s Guide to Installing R on Windows and MAC OS Hello and Welcome to next part of our first course in The Data Science Specialization. After getting familiar with what is Data science, Data, Data Science process and knowing what actually is Data Scientist, we move towards the next part of getting familiar with the tools that will be needed during our Data science specialization. First, let’s remind ourselves exactly what R is and why we might want to use it. R  is both a programming language and an environment, focused mainly on statistical analysis and graphics. It will be one of the main tools you use in this and following courses. R is downloaded from the  Comprehensive R Archive Network , or CRAN, and while this might be your first brush with it, we will be returning to CRAN time and time again, when we install packages - so keep an eye out! Why should you use R? Outside of this course, you may be asking yourself -  why should I use R? The rea...

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...