Reskilling towards Data Science

I want to strengthen my professional value proposition through Data Science, and most of the articles I have read about how to start in Data Science are from people who already had several years as Data Scientist, and now, from their success shared how they got there, many with studies in Computer Science, PhDs, mathematicians, etc. that did not fit my reality.

I am an apprentice in Data Science, although I have extensive professional experience and studies in Project Management, Innovation, and Organizational Management, just over 1 year ago I stopped “knowing” about Data Science to start learning to “use” it, and so I finished the Professional Certification in Data Science from IBM — Coursera, several acquaintances have asked me about it, so in this context, I will share some experiences I have lived.

EVERY TREE IS DIFFERENT

There are not 2 equal trees, some of the same species, even growing together, are different, so it happens with us and our professional life, in my case, I studied Electronic Engineering and never practiced it, since before graduating I have worked in 2 things, Organizational Management and Project Management, so I did a Master in Administrative Engineering and “officialized” everything I had worked on, after this I have deepened in Project Management (as PMP-PMI, PM4R, MGA, etc.) and also in entrepreneurship, Innovation and Project Management. Administrative and “officialized” everything I had worked, after this I have deepened in Project Management (as PMP-PMI, PM4R, MGA, etc.) and also in entrepreneurship, Innovation, and agile methodologies (Lean Launchpad, Design Thinking, Agile, etc.), I have also done training in Coaching and NLP, in short, a passionate of knowledge and its application to improve human life at work.

We may ask ourselves, what does this have to do with me? Well, everything and nothing, everything because it was from my experience and knowledge (as well as everyone else’s) that I came to Data Science, and nothing because what I have experienced is perhaps totally different from what someone else has experienced, therefore, the question arises, where do we come from professionally and where do we want to go through Data Science.

I first came across Data Science about 2 to 3 years ago because I have always been passionate about technology and innovation, in my sessions as a consultant, mentor, and coach I have always mentioned the 4th. Industrial Revolution and the VUCA environment in which we live.

I started reading about Artificial Intelligence, Machine Learning, Data Science, Big Data, IoT, etc. I had just studied Coaching and NLP (Neuro-Linguistic Programming). I was fascinated by the idea of emulating human thinking and decision-making, so I began to dig deeper and deeper and here what perhaps has happened to all of us at some time happens, “I drowned” In the middle of the sea of information, the tree of curiosity, I saturated it.

At that moment I made a “stop” and the Ministry of Information Technologies and Communications of Colombia, issued a call to study Artificial Intelligence, I applied, stayed, and here I had my first approach with a realistic approach.

DEFINE YOUR FRUIT

When I won the call, I chose Platzi, they are an enterprise of Colombian origin, it is in Spanish and they gave access to the entire platform, so that’s why I chose it, however, I confess, I got stuck, without programming bases, although I followed the rhythm, it was difficult to advance, however, I finished and I learned many things from this.
The first is that before starting I have to choose what fruit I want, Data Science is very broad, it has many roles and purposes, therefore, it is not the same to study it because it is fashionable, because it touches or because “ajá” (as they say in Barranquilla, where I am from) but because there is a higher purpose.

In my case, as a Consultant, Mentor, and Trainer in Project Management, Organizational Management, and Innovation, I wanted to study Data Science to develop Innovation Projects using this tool for my clients, thus strengthening my Professional Value Proposition.

What is your Professional Value Proposition? what problems do you solve? what makes you unique? how are you going to strengthen this with Data Science? under what role?

CHOOSE A TRUNK

Once the fruit is defined, the tree is defined, as the saying goes “you can not ask for pears from the elm” so with that fruit in mind I chose a sturdy trunk, a column that supported my start in Data Science and by this I mean a school, either synchronous or asynchronous, classroom or virtual, this depends on your learning style, fundamentals, approach, etc.

In my case, I started with Platzi, with Artificial Intelligence, and reinforced what I learned by reading and watching free online videos and lectures. After that, I jumped to IBM-Coursera with the Professional Certification focus on programming, however, other courses can focus more on the part of statistics, linear algebra, mathematics (for my studies I already have those bases, although there is always something to learn).

This trunk to be robust must have 3 minimum components: Programming, Statistics, and Linear Algebra.

It’s important to know that you do not need to know everything in Programming, Statistics, and Linear Algebra. Only what is applicable in that field, if you want to do more research level, you will realize what you need to learn as you go deeper.

Another key part, that in my case I require to reinforce is Data Literacy (ability to read, understand, create and communicate data as information, Wikipedia), which involves other skills that I am just starting to learn.

So, which trunk or column will you choose? What is your learning style, your experience, knowledge? How much time and resources do you have available? is that trunk coherent with the purpose, the fruit you want?

GENERATE YOUR ROOTS

Once you have chosen your trunk, you may feel that it is too weak for the fruit you want, that you may not advance as you expected, however, everything depends on you, and here it is important to start to see if your roots are enough.

In my case, in principle when I started Artificial Intelligence in Spanish, sometimes I read, listened, and knew that it was Spanish, however, I did not understand! I got to feel frustrated, a little slow, until I understood that the problem was with the roots, imagine wanting to be a mango tree with roots that do not grow.

I have focused on programming, especially Python, and although it is a “simple” language, in the end, it is that a language, and like the roots of the tree, it grows over time, with constant practice. This skill has something particular, it is not enough to know, in other words, it is not enough to know concepts but to apply them, even, many times I have applied and I do not know how it works, I only know that they solve the problem I want (happens with libraries, for example).

Learning to program is for me to learn a new language, a new way of thinking, therefore, if the roots in that part are weak, you have to pause, breathe and start reading from the best free source there is, Google! But you have to do it with focus, the one who wants to solve something, not the one who procrastinates wanting to find explanations for everything and ends up drowning in the end.

Something that makes the roots strengthen… is a “mantra” that I repeat to myself (and which is perhaps an apostasy for perfectionists), “it is better done than perfect”, each accepted mistake is a learning opportunity, what roots do you require? beef up? Mathematics, statistics, programming…?

SPREAD YOUR BRANCHES

The learning of Data Science never ends for the one who really develops his purpose through it, for me, the goal is not to know about Data Science, it is to solve problems through it, Data Science is a means to an end, it is a tool to make decisions.

What kind of decisions do you want to improve? Data Science is a tool to solve problems, what kind of problems do you want to solve? In the end Data Science is the branch that sustains and feeds the fruit, your Professional Value Proposition.

Therefore, my invitation is that you identify the problems that exist in your professional environment and connect them with Data Science, more than an expert in Data Science you are an expert in solving problems in your professional area through it, some examples:

• If you are in Maintenance, you can work to create Predictive Maintenance solutions using Machine Learning.
• If you work in production processes, you can identify variables, cluster them using Unsupervised Learning and identify, for example, their impact on production.
• In Quality Control you can develop Artificial Intelligence solutions to optimize non-conformance indicators.
• In the construction sector, you can focus on improving the use of raw materials, space, shifts, etc.
• In marketing, you can use it to build customer loyalty.
• In sports, to predict the performance of players results according to strategies, etc.
• If you are an academic, create research groups that apply Data Science in your area of interest.

And so, there are as many branches as there are problems, that’s why it is important that the branch is focused and bears the fruits that fulfill the purpose, to improve your decision making.

LINKS OF INTEREST

I like to leave the links at the end, in my case, during the reading I feel the temptation to enter and I get scattered, so here I share with you some links of what has worked or done or I have reviewed, there are thousands more, however, to start I think there are enough.

Define your fruit

This is my “Wikipedia” on Data Science: Towardsdatascience

Choose a trunk

You have to pay:

• Platzi. I did a special one on Artificial Intelligence from MintTIC, currently, I am studying one that is much more complete.
• IBM- Coursera English. This is the one I completed.
• Codeacademy. It has been recommended to me by third parties..

Free:

• Cognitiveclass. It is from IBM, it gives badges, they are almost the same as Coursera, the only thing is that it does not give the general certification and they are not all, it is excellent if you want to first explore without spending.
• Elements of AI. Provides the basics to get you started.
• Machine Learning de Stanford. By Andrew Ng. The most popular course, however, uses Octave or Matlab, I paused it and I plan to continue it later since I want to focus on Python.

This is the end of this first part, I hope I have contributed something to this professional reskilling, that like me you have decided to take, in future installments I will continue sharing my journey, thanks for reading.

Let’s connect!

Jesús Romero Palacio / Medium: