Skip to main content

Welcome to the Data Science Specialization using R!

The Beginner’s Guide to the Data Science Specialization using R!

In my first video, I introduced the learners to the Data Science Specialization using R. I have covered topics such as data manipulation, data visualization, statistical inference, and machine learning. I have also talked about the importance of using R in data science and the benefits of the Data Science Specialization. You are now ready to dive deeper into the world of data science with R and learn from my expertise. If you haven't watched my first video please find it below:



Course Dependency Table:

To help my viewers better understand the structure and dependencies of the Data Science Specialization using R, I have provided a course dependency table. This table will show which courses build upon the knowledge learned in previous courses and which courses are prerequisites for others.

For the courses, we consider two forms of dependency:

  • Hard dependency: Students will be required to know material from the prerequisite course. Taking the dependent course simultaneously will be challenging and only possible for highly motivated students willing to work ahead of the course schedule for the prerequisite. Taking hard dependent courses out of order is not possible unless the student already knows the material covered in the prerequisite course.
  • Soft dependency: Knowledge of material from the prerequisite course is recommended and useful. Concurrently taking the prerequisite course and the dependent course is possible. It is not recommended to take them out of order, but would be possible for highly motivated students willing to self-teach components of the prerequisite course as needed. 

Course Number

Course Name

Soft Dependency

Hard Dependency

1.

Data Scientist Toolbox

 

 

2.

R Programming

1.       Data Scientist Toolbox

 

3.

Getting and Cleaning Data

 

1.       Data Scientist Toolbox

2.       R Programming

4.

Exploratory Data Analysis

 

1.       Data Scientist Toolbox

2.       R Programming

5.

Reproducible Research

 

1.       Data Scientist Toolbox

2.       R Programming

6.

Statistical Inference

 

1.       Data Scientist Toolbox

2.       R Programming

7.

Regression Models

 

1.       Data Scientist Toolbox

2.       R Programming

3.       Statistical Inference

8.

Machine Learning

1.       Exploratory Data Analysis

1.       Data Scientist Toolbox

2.       R Programming

3.       Regression Models

9.

Data Products

1.       Exploratory Data Analysis

1.       Data Scientist Toolbox

2.       R Programming

3.       Reproducible Research

10.

Capstone Projects

All Courses

All Courses

This table will help you understand the order in which you should take the courses and how each course builds upon the knowledge gained from the previous one.


Subscribe to my social media accounts:

If you have enjoyed my first video and want to see more, you can subscribe to my social media accounts. This way, you will receive updates whenever I release a new video or post.


Here are the links to my social media accounts:




Conclusion:

In this blog post, we introduced our first video, discussed the course dependency table, and provided links to your social media accounts. We hope that you will find this information useful and continue to learn from our expertise. Best of luck with your data science journey!


Labels:

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