This site was conceived as a way to ease my job search as a
Data Analyst, both for me to show my skills and the tasks I
am more interested to perform and for the recruiters to evaluate my
competences and how I reason to assess my fit inside their
organization.
I therefore took a data frame from the web and I analyzed it as I would do in a working environment, organizing the site into three different subsequent parts that build on each other, plus a recently added dashboards section that presents some of the findings of the previous ones.
People with vastly different skills can be actors in a recruitment
process, and I want this site to be accessible to everybody, hence the
choices of limiting technical vocabulary and of hiding the code by
default (bar for a specific section of a document).
Notwithstanding, certain pieces can be not very interesting for non
technical people, I am thinking first and foremost of the
Data Wrangling pages, and a basic knowledge of data
visualization (probability distributions, histograms and box plots) is
required.
About the site’s visual presentation, I kept it simple and clean, also because I didn’t nor don’t want to spend too much time on it. I find it pleasant enough nonetheless but YMMV of course. I also didn’t test the site on various platforms, just on a Chrome browser running on a Windows machine.
All the graphs are in b/w, as, after toying for a little while with colorblind-friendly palettes, I deemed it the most elegant and easiest solution to adopt.
The dashboard section follows different principles, starting with the adoption of a colorblind-friendly palette, as there the visual representation is more important for its scope.
I kept the exposition style that I had in my most recent position,
where I was doing remote presentations several times a week.
The audience mainly consisted of domain experts not trained in the R
programming language or into machine learning techniques, but all of
them had engineering backgrounds and/or coding experience. During that
time, I never took anything for granted though, i.e. I briefly explained
box plots the first time I used them for instance.
For this site I tried to keep the singular pages short not to
overload with information, with the general idea that every one of them
can be processed also at the end of a rather heavy working day. The
limit I imposed myself was .Rmd files of 300 lines, but that was not
always respected, especially for the Data Wrangling pages
(the outliers ones in particular) as I preferred to provide
comprehensive analyses.
The way I name variables is not an accepted standard, because they are meant to look pleasant in the outputted tables.
Likewise, I do not load all the packages at the beginning of the
script as common practice dictates but just before I use a function from
them. It is a personal pet peeve, as when I was first learning to code I
didn’t like to not know what function belonged to what package. This is
anyway not very consistent, for example I sometimes use the
package::function() syntax as well, and it is something that can be
easily corrected, if required.
The packages I used are very few and very common anyway.
In the same way I like to comment after the code, as I see comments
like subtitles that can help its comprehension. Using them before takes
away the attention from the code itself in my opinion.
I did not comment much in any case, as I find most of the code here
rather basic and easy to understand.
Case in point, I could have initialized a function for many similar blocks of code applied to different columns, but I decided against it to keep the code as simple as possible.
I also decided to present static tables, and not dynamic ones like the following,
deeming unnecessary the highest requirements in terms of space and computing power for the purely demonstrative objectives of this site.
For the same reason, the tables are not downloadable.
The data frame was first retrieved, on the 1st of September 2022,
from https://archive.ics.uci.edu/ml/datasets/Online+Retail+II.
It was chosen between several candidates, but not too much thought was
given to the selection process.
I chose a data frame from the retail industry, as it is the one I know better thanks to my studies, working experiences and personal inclinations.
I am interested in professional opportunities also in other fields though, especially the ones that concern people behaviors, as I find fascinating to investigate them, not excluding at all pursuits in, broadly speaking, humanitarian organizations.
Economic satisfaction is not my main goal, a healthy life/work balance is what I strive to achieve the most.
I am currently in mainland Europe, but I am willing to relocate
anywhere in the world.
My only strong objection would be the local climate, as the rigid one
was the reason I quit my previous job in Paris.
Introduction & Data Wrangling contains mandatory
preliminary operations. They are not the most glamorous tasks but I find
morbidly satisfying to discover errors and inconsistencies, feeling as
well I possess the attention to detail and meticulousness necessary to
prepare data frames for further work. Always interested in learning more
advanced techniques.
Given its nature, this section is the most conclusive one.
Extracting Insights it is what I enjoy the most to do,
to interrogate the data frame like it is a culprit to squeeze of
valuable information, and then presenting the results to decision makers
in a clear and concise manner. It is in my opinion the main activity for
a Data Analyst and I fit right into it. Eager to improve my
effectiveness and my level of sophistication to be of more value to the
firm.
The third section, Advanced Inquiries, doesn’t differ a
lot in spirit from the second one, just the tools are different as they
consist of more advanced statistical and algorithmic methods. I have a
lot of interest in these as well, given the insights they can provide is
vastly superior to basic rows and columns manipulations.
In this site I offered a small sample of quick expositions of the ones
more easily applied to the data frame in exam, but I also have working
experience with linear regression and of several classification
models.
I’m furthermore interested in recommendation engines, A/B testing, RFM
analysis, operations research and in general in using Data Analysis to
investigate logistics & transportation problems.
The newly added Dashboards section was developed because
many job postings require knowledge of visualization tools like Tableau,
Power BI or Looker, and I only have experience of the former, but solely
as an end user and not as a developer. I could have picked one of the
aforementioned softwares and start to learn its ropes, but I wouldn’t
know which one to bet on, so I decided to use the R package
flexdashboard instead to show my aptitude in creating dashboards. That
is something I never did, as I always presented reports instead, so
there’s a lot I can do to improve the quality of the output, from
getting more acquainted with the R package, to apply visualization
theory’s principles like the Gestalt’s ones, to tinker with the fonts
and colors.
I had fun in any case and that is something I can see myself doing with
ease and participation in a working environment, willing to learn other
tools if required as well.
I consider myself a good learner, my path in the Data Science field might attest to that, and also the fact that after a short time I was able to work in French despite having never studied it before hand (being Italian probably helped though).
Beside those two languages, I am proficient in English, I feel this site suffices as proof, and I speak Portuguese and Danish as well. I am very rusty in the latter, plus I don’t see working opportunities that will make me refresh my knowledge of it. On the contrary I would be very pleased if I can use Portuguese in a professional capacity.
I usually worked alone in my previous experiences, so you can say that I am autonomous, organized and dependable, but at the same time I lack familiarity with the practices of a modern Data Science team.
My main objective is to become a better Data Analyst.
About transitioning to different roles, I can see myself becoming a Data
Scientist, if I manage to acquire the command of more in deep
statistical and mathematical techniques, while that is not true for a
Data Engineer position, as the related IT skills are less enticing for
me to study.
Linkedin is the best way to contact me, also for comments, questions or critiques about the site itself.
Thanks for your time.