I'm a backpacker sharing my journey into quantitative financial risks in the hopes that you don't have to suffer on your journey too.

Henrique Costa

In this article I want to update and improve an analysis I did some time ago, and published on LinkedIn, which can be accessed through this link: Credit Granting can be used as a proxy for the Financial Cycle, which tends to present a “boom” before the beginning of a recession.
<strong>
 Intro
</strong>
I will start with the definition of a cycle: a cycle is a low-frequency variation event.

Financial Cycle: Credit - Part I

<strong>
 Rambling (proxy for an introduction)
</strong>
We are experiencing the chaos of the information age, and its technological advents with voracious evolution and expansion. And in the midst of all this we have the advent of Big Data that has reached the delights of many with Data Science, Machine Learning and Artificial Intelligence. As a result, the idea of mathematical and statistical “models” became the rage, gaining visibility.

Trivia Series: Regression

Na publicação de hoje, quero trazer uma brincadeira que fiz comigo mesmo, um pequeno incentivo para voltar a publicar conteúdos sobre R (eu já estava enferrujado) entre outros assunto sobre economia. Passeando pelo instagram, encontrei um perfil que pública muita informação sobre economia em formato de visualização, o Economista Visual.

Reproduzindo gráfico do Economista Visual - Valor da cesta básica (PT-BR)

When it comes to job titles, data scientist is one of the biggest buzzwords in recent years. It is also one of the fastest growing fields of professional activity. Can an economist really be a data scientist? What skills are needed?

Can Economists Become Data Scientists?

In today’s post, I want to start a discussion about the use of mathematics and statistics in economics, and with this shocking sentence:  When a person studies economics for the first time, it is likely that they will not encounter “crazy” equations that go beyond basic mathematics.

The mathematization of Economics

<strong>
 Modeling Principles
</strong>
Almost all, if not all, applied science is based on the idea of a model. There are problems in the real world, and we would like to create a theory to solve such problems. One of the pillars or the main foundation of quantitative modeling is its effectiveness. But how can a model be effective? By demonstrating that it is simple, reproducible, and applicable in other areas of knowledge.

Trivia Series: Modelling foundations

Aqui voce pode encontrar a apresentação: Link da Apresentação  Aqui você se cadastra para participar do sorteio dos livros: Link para o Formulário  You can also reproduce this by choosing the city you want. Check out the complete code on my Github. Hey! 👋, did you find my work useful? Consider buying me a coffee ☕, by clicking here 👇🏻   Reuse   CC BY 4.0 Citation   For attribution, please cite this work as: Costa, Henrique. 2024.

Sorte e Apostas

I started this website some time ago with the aim of writing down the expansion of my knowledge and experience in the area of financial risk management. And also other things like programming with R, econometric modeling and a little about
<em>
 data science
</em>
and
<em>
 machine learning
</em>
. So I’m going to use this publication as a starting point, and start discussing texts and examples applied to financial risks.

A few words about risk management

Having decided to improve my skills in the short space of time left between my professional life and personal life, I started researching the available data science options.

For me, the choice was R or Python

I will present here some personal reflections on a concept that is considered very simple and perhaps common in economics, but which is labeled as the height of ignorance for making an assumption that cannot be observed in reality, but which in my view is a mistake. mistake.

The Ceteris Paribus Condition

Risk Taker!

[![](https://hcostax.com/blog/r_or_python/feature.jpeg){.img-fluid}](feature.jpeg){.lightbox
gallery="quarto-lightbox-gallery-1"}

Having decided to improve my skills in the short space of time left
between my professional life and personal life, I started researching
the available data science options. It quickly became clear that I
should choose a programming language to learn and apply the techniques
and the choice fell on R or Python, for me both options are good because
they are free and open source and have an excellent reputation, in
addition to each one having its own own community of active users, and
very active!

I don't remember where I saw this tip, but for me it was the central
factor in my decision and something worth sharing:

If you are a professional who uses analysis tools in a business context,
and with experience in Microsoft Excel, my tip is: select R to start
your journey in data science. R is a single-threaded object-oriented
functional programming language, and once you understand the main
commands, it is intuitive to use. As such, it's pretty predictable,
which has been great for me as a professional. R has an excellent
graphics package that complements the idea of analyzing data or *data
wrangling*, which Python programmers often turn to *ggplot2* to generate
beautiful visualizations, or use the ggplot2 theme within Python (lol),
even though in recent years the Python's visualization libraries have
improved a lot, yet this happens frequently.

On the other hand, if you already have some experience in programming,
or are from the computing field, Python is a much more general purpose
and more readable language for those coming from this field, and
everything you can do in Python is the same as most of the things R is
good at.

Both groups have strong communities that share their knowledge on
various blogs and events and I really think they are both great choices.
Today the market is more heated when it comes to Python, and
professionals who know both languages do very well.

Now that I've been using R for a few years (since 2013), I've had the
chance to use it a few times in the workplace, and I use it whenever I
can, showing the resources this tool offers, creating econometric models
and estimation procedures. in modeling for financial risks to provide
some business *insights*. I have already carried out many analysis
projects for private clients and I feel honored that my efforts to learn
are generating results.

A fellow Python expert with an interest in machine learning once saw the
few lines of code needed to organize the dataset, train a model, and
predict results, and, frankly, he was shocked. We had an awkward
R-to-Python conversation trying to understand the differences between
data matrices and data frames afterwards, although for me it clarified
where R was strong: the ease of preparing and building a model quickly
for any type of analysis.

But depending on who you ask, when I first came into contact with R in
2013, R probably had a slight, if not substantial, advantage over Python
in user adoption for machine learning and what is now known as data
science. Since then, the use of Python has grown substantially and it
would be difficult to argue against the fact that Python is the new
favorite, although the race may be tighter than one might expect given
the enthusiasm of Python fans supporting the new and Brilliant tool with
biggest hype.

In recent years, Python has benefited greatly from the rapid maturation
of free add-ons such as the *Scikit-learn* machine learning framework,
the *Pandas* data structure library, the *Matplotlib* graphing library,
and the *Jupyter* notebook interface, among several other open
applications. Source libraries that make it easier than ever to do data
science in Python. Of course, these libraries only brought Python to par
with what R and RStudio could do long ago! However, Python is
comparatively fast and memory efficient -- at least relative to R --
which may have contributed to the fact that Python is now arguably the
language most frequently taught in formal programs in data science. and
quickly gained adoption across business domains.

Rather than indicating the imminent death of R, because Python's rise is
steep. In fact, the use of R is also growing rapidly, and R and RStudio
are becoming more popular than ever. Although students sometimes ask
whether it's worth starting with R rather than jumping straight to
Python, there are still many good reasons to choose to learn machine
learning with R over the alternative.

Please note that these justifications are quite subjective -- not just
mine, but any justification on the internet will be like this -- and
there is no right answer for everyone, so I hesitate to put this in
writing! However, as someone who still uses R almost daily as part of my
work for a large corporation, here are a few things I've noticed:

- As I mentioned above, R may be more intuitive and easier to learn for
  people with a background in social sciences or business (such as
  economics, marketing, and so on), while Python may make more sense for
  computer scientists and other types of engineers .

- R tends to be used more like a "calculator" where you type a command
  and something happens; In general, coding in Python tends to require
  more consideration of loops and other program flow commands (this
  distinction is disappearing over time with additional functionality in
  Python libraries).

- R uses relatively few types of data structures (those included are
  adapted for data analysis) and the frequently used spreadsheet-type
  data format is a built-in data type; Comparatively, Python has many
  specialized data structures and uses libraries like *NumPy* or
  *Pandas* , and for array data format, each has its own syntax.

- R and its packages can be easier to install and update than Python, in
  part because Python is managed by some operating systems by default,
  and maintaining separate dependencies and environments is a challenge
  (modern Python installation tools and managers of packages addressed
  this simultaneously, in some ways making the problem worse!).

- R is typically slower and more memory-intensive than Python for data
  manipulation and iteration over large data structures, but if the data
  fits in memory, this difference is somewhat insignificant. For real?
  If you don't work with BigData, you probably don't even feel this
  difference; R has improved in this area, R is making data preparation
  faster and easier, and for data that doesn't fit in memory there are
  workarounds (R with BigData), but this is admittedly one of Python's
  biggest advantages.

- R has the support and vision of the Posit team (formerly known as
  RStudio) driving innovation and making R easier and more enjoyable to
  use in a unified software environment (RStudio Desktop); In contrast,
  Python's innovations are occurring on multiple fronts, offering more
  "right" ways to accomplish the same thing (for better or worse).

Hopefully, the reasons above will give you the confidence to begin your
journey. There is no shame in starting with R (as some people think),
whether you stay with R long-term, use it side-by-side with other
languages like Python, or major in something entirely different, the
fundamental principles you learn will be transferred to any language (a
clear example is: if you haven't yet learned SQL and already understand
the basics of *Tidyverse*, you will have an easier time studying SQL, or
vice versa) or tools you choose. Although code written in R is much
"prettier" to me, I highly recommend that you use the right tool for the
job, whatever it may be. You may find, as I did, that R and RStudio are
your tools of choice for many real-world data science and machine
learning projects - even if you occasionally take advantage of Python's
unique strengths!

In the end, my recommendation is: just pick one and get started.
Although I will still need to learn a lot more about Python to get the
unique benefits each language offers, amplified through collaboration.
The good thing about this is that, using RStudio, an IDE for R, it is
possible to use Python and SQL as well.

::::::: {.callout .callout-style-default .callout-note .callout-titled}
::::: {.callout-header .d-flex .align-content-center}
::: {.callout-icon-container}
:::

::: {.callout-title-container .flex-fill}
Hey! 👋, did you find my work useful? Consider buying me a coffee ☕, by
clicking here 👇🏻
:::
:::::

::: {.callout-body-container .callout-body}
[![](https://img.buymeacoffee.com/button-api/?text=Buy%20me%20a%20coffee&emoji=&slug%20=hcostax&button_colour=5F7FFF&font_colour=ffffff&font_family=Cookie&outline_colour=000000&coffee_colour=FFDD00)](https://www.buymeacoffee.com/hcostax)
:::
:::::::

:::::::::: {#quarto-appendix .default}
::::: {#quarto-reuse .section .quarto-appendix-contents}
## Reuse {#reuse .anchored .quarto-appendix-heading}

:::: {.quarto-appendix-contents}
<div>

[CC BY 4.0](https://creativecommons.org/licenses/by/4.0/){rel="license"}

</div>
::::
:::::

:::::: {#quarto-citation .section .quarto-appendix-contents}
## Citation {#citation .anchored .quarto-appendix-heading}

<div>

::: {.quarto-appendix-secondary-label}
For attribution, please cite this work as:
:::

::: {#ref-costa2023 .csl-entry .quarto-appendix-citeas}
Costa, Henrique. 2023. "For Me, the Choice Was R or Python." June 20,
2023. <https://doi.org/10.59350/83vew-1de35>.
:::

</div>
::::::
::::::::::
