I have a 2 projects due at 6 pm EST can you guys help. (Subject - Analyzing & Visualizing Data ) Attaching the file and the dataset which needs to be used.

Project 2: Answer those questions in the scope of your dataset

Question 1: CRITICAL EVALUATIONS

Now that you have learnt about the definition of data visualisation and the key principles that distinguish good from bad visualisation design (trustworthy, accessible, elegant), undertake some reflective evaluations on any visualisation you come across. It might be your own work, it might be work you receive from others or it could be just work from around the web. There are hundreds being shared on a daily basis online via social media, blogs and news media, you won’t struggle to find any! (If you do, check out the reading materials alongside each chapter and you’ll find many examples). Give yourself time to fully immerse yourself and experience each one (whether small and instant or long and involving) and consider the following prompts:

1. What one word describes how you instinctively feel about the work within the first 5-10 seconds? Is it positive or negative? – what are the good/bad thing about your dataset

2. Very subjective but do you like the visualisation (might be the subject or visual form)? What score on a scale of 0 to 10 would you give it (10 is best)? Consider what factors influenced your ratings?

Rate it based on how much its easy or hard to learn

And how much you think it can be utilized in your profession

1. Do you feel the project successfully – and sufficiently – facilitates understanding (does it help you learn something about the subject matter or, at least, confirm/reinforce what you already knew)? What score on a scale of 0 to 10 would you give it (10 is best)? Consider what factors influenced your ratings?

1. Consider the project’s effectiveness or otherwise in demonstrating the principles of trustworthy, accessible and elegant design: where does it succeed and where does it fail?

What are the things that you think u did well in the project and what are the things that you think you need more time to learn

1. Whilst you may not know much about the project’s hidden context, what would you do differently? How would you help to get these pair of ratings higher towards the maximum of 10?

Chapter 4

This exercise involves you working with a dataset of your choosing. Visit the Kaggle website, browse through the options and find a dataset of interest, then follow the simple instructions to download it. With acquisition completed, work through the remaining key steps of examining, transforming and exploring your data to develop a robust familiarisation with its potential offering:

Examination: Thoroughly examine the physical properties (type, size, condition) of your dataset, noting down useful observations or descriptions where relevant.

https://www.statsandr.com/blog/descriptive-statistics-in-r/

Descriptive statistics : give some details about your dataset

Talk about the size of your dataset

Talk about the types of columns

Talk about Univariate (the distribution of values )

Talk about histogram (frequency of values in each column )

Transformation: What could you do/would you need to do to clean or modify the existing data to create new values to work with? What other data could you imagine would be valuable to consolidate the existing data?

• Normalization

• Dealing with missing values/blanks

• Creating new columns

• Eliminating some columns

• Binarization

(extra credit )/optional : Exploration: Using a tool of your choice (such as Excel, Tableau, R) to visually explore the dataset in order to deepen your appreciation of the physical properties and their discoverable qualities (insights) to help you cement your understanding of their respective value. If you don’t have scope or time to use a tool, use your imagination to consider what angles of analysis you might explore if you had the opportunity? What piques your interest about this subject?