Assignment Requires usage of R tool for the project.

Analyzing and Visualizing data

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.

I believe my work has been positive for someone who has never worked with coding at a

higher level.

Good thing about my data set

• It has multiple numerical values that could and were adjusted to create other types

of data like continues, binary, etc.

• My data set consist of categorical data.

• My data set consists of multiple columns of discrete data.

• My data set consists of string data.

• My data set contains one column of continues data

Bad things about my data.

• My data does not provide date and time data.

• My data only contains one column of continues data, making it challenging to

create chart that requires two continues variables.

2. Very subjective but do you like the visualization (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

I believe visualization is one of the most important tools to represent and present data to

the audience. The ease at which, if generated and presented properly, a data visualization

can help the viewers quickly understand the data, whether it is recognizing trends or what

contributed most to the success or failure of a brand, sales, etc.

I would give it an 9 since it is not as easy to learn as I would like. My learning curve

might be hindered by the fact that I do not have coding experience with R in the past.

3. 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?

The project 1 was more challenging than the all the quizzes we had so far. I believe the

quizzes helped me prepare and feel comfortable with what was coming in project 1. I feel

project 1 forced me to use my critical thinking and problem-solving skills with limited

amount of time. The project helped me learn more about statistical coding which is

important when it comes to moving towards a career as a Data Scientist. I would give it

an 8 because prior teaching did not involve statistical coding as much as it did in the

project 1.

4. 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

I was successful in executing the code with no errors, but I would like to take more time

to understand all the results that were provided by the individual codes. I am new to

coding and I would like to learn more as I go.

5. 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?

I would have taken more time to understand the data sets that were provided and all the

possibilities of the data set. I would have used more time to learn more about statistical

coding before I attend the residency so that I would have been able to better describe and

understand the results of the codes. This would have helped me make change to the code

to better suit my data set.

Question 2

DEVELOPING INTIMACY WITH YOUR DATA

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 familiarization 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

My data has 103 observations and 9 variables

Talk about the types of columns

I have originally had 8 columns. 5 numerical columns, one categorial column, two string data

columns.

Talk about Univariate (the distribution of values)

Please see below screen shots for all the distributions

Talk about histogram (frequency of values in each column)

My histogram is for Age 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

Since my last column row contained totals of new columns, it created missing data that

was solved by removing the last row.

• Creating new columns

I generated few columns to scale down my values, to create a column that only had vales

from 0 to 1 and I have even created column using if statement.

• Eliminating some columns

I did not eliminate any columns, but I did remove one row that contained the totals of

couple of columns.

• Binarization

I used my Transfer fee column to generate a binary column that was needed for

completion of project 1.

Descriptive Statistics

Below a preview of this dataset and its structure:

Minimum and maximum

Below shows min and max value of column Years.

Below shoes min, max and range

Median

Below shows quarters.

Below shows Interquartile range

Below shoes standard deviation and variance

Below shows summary

Coefficient of variation

Mode

Contingency table

Mosaic Plot

Bar chart

Histogram

Box plot

Dot plot

Scatterplot

Line plot

QQ-plot

For a single variable

By Groups

Below is Density Plot from Quiz 2

Frequency tables

Cross-tabulations

Descriptive statistics

Data frame summaries

aggregate() function

(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?

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