Skip to main content

What is Data? And What is Data Science Process?

The Beginner’s Guide to Data & Data Science Process

  • About Data:

In our First Video today we talked about Data and how the Cambridge English Dictionary and Wikipedia defines Data, then we looked on few forms of Data that are:


  •  Population census data (Here is the US census website and some tools to help you examine it, but if you aren’t from the US, I urge you to check out your home country’s census bureau (if available) and look at some of the data there!)
  • Electronic medical records (EMR), other large databases
  • Geographic information system (GIS) data (mapping)
  • Image analysis and image extrapolation (A fun example you can play with is the DeepDream software that was originally designed to detect faces in an image, but has since moved on to more artistic pursuits.)
  • Language and translations
  • Website traffic
  • Personal/Ad data (e.g.: Facebook, Netflix predictions, etc.)

These data forms need a lot of preprocessing and cleaning after which these are used to answer a lot of questions and to make informed decisions.



Here is our video, if you haven’t watched it yet, watch now!

  • Data Science Process:

In our second video today, we discussed The Data Science Process So what is Data Science Process?

Here is our video on Data science Process.


The data science process is a structured approach to solving data-related problems. It involves several stages, from problem definition to model deployment. Here is a brief overview of the data science process in R:



1. Define the problem: The first step is to clearly define the problem you are trying to solve. This involves understanding the business problem, identifying the stakeholders, and specifying the data requirements.

2. Collect the data: Once you have defined the problem, the next step is to collect the data. This may involve obtaining data from various sources such as databases, APIs, or web scraping.

3. Explore the data: After collecting the data, the next step is to explore and analyze the data. This involves cleaning the data, summarizing the data using descriptive statistics, and visualizing the data using graphs and charts.

4. Prepare the data: Once the data is explored, the next step is to prepare the data for analysis. This involves transforming the data into a format that is suitable for analysis, such as reshaping the data, creating new variables, and filtering out irrelevant data.

5. Build the model: With the data prepared, the next step is to build the model. This involves selecting a modeling technique that is appropriate for the problem at hand and using R to develop the model.

6. Evaluate the model: Once the model is built, the next step is to evaluate its performance. This involves testing the model on a holdout dataset and assessing its accuracy and robustness.

7. Deploy the model: Finally, if the model is deemed satisfactory, the next step is to deploy the model. This may involve integrating the model into a production system, creating a user interface, or providing documentation for end-users.


R is a popular programming language for data science because it provides a wide range of tools for data manipulation, visualization, and statistical modeling. R also has a large community of users and a wealth of resources, such as packages and tutorials, that can be used throughout the data science process.


We also discussed an example that follows Data Science Process in R, "Predicting House Prices using Regression Models: A Case Study in R"

Please Subscribe our YouTube channel, if you haven't subscribed yet, so you don't miss on latest updates.

Thanks for reading our Blog!
Labels:

Comments

  1. Anonymous3 March 2023 at 23:45

    Really enjoyed reading, how well you explained complete Data Science Process, great Resource of learning Data science!

    ReplyDelete

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