How I felt in love with Data

Everything was started when I have gotten the data from the "outside world" and my body and brain analyse the raw data I got and then ... we "interpret" things.

Before online courses and reading relevant books, I model all the time, without even know what I do at that time. Now, a little bit more confidence on my first steps, I could say that modelling is one of the most important piece of the process od understanding the "reality", the world around us, creating a higher level prototype that will describe the things we are seeing, hearing and feeling, but it's a representative thing, not the "actual" or "real" thing.

Moreover, independently how much important modelling is, they must have a purpose, and it might looks obvious but we have to understand the model which we have created before using them.

What is Data Science?

Data science not just knowing some programming languages, math, statistics and have “domain knowledge”.

The time has come. We’ve created a new field, or something like that. There’s a lot of things to say and study in this field. It doesn’t matter the name, maybe data science is just a temporary name for a bigger field, but the scientific study of data, getting insights from it and then be able to predict somethingis the present and future of the world.

I’ll focus on business related definitions and proposals for data science, maybe these can apply for the field as a whole, but the ideas in this article are about data science for business.

I’m going to propose two things:

1. Data science is a science

2. There are awful ways to learn data science

I know this maybe controversial for some people but stick with me. What I want to say here is that data science is of course linked to the business, but it is a science in the end, or in the process of becoming one.

I defined data science before as:

[…] The resolution to Business / Organizations problems through mathematics, programming and the scientific method that involves the creation of hypotheses, experiments and tests through the analysis of data and the generation of predictive models. It is responsible fortransforming these problems into well-posed questions that can also respond to the initial hypothesis in a creative way. It must also include the effective communication of the results obtained and how the solution adds value to the Business / Organization.

I’m stating here a description and definition of data science as a science. I think it could be very useful that data science can be described as a science because if that’s the case, every project this field should be at least:

• Reproducible: Necessary for making easy to test other’s work and analysis.

• Fallible: Data science and science doesn’t look for the truth, they look for knowledge, so every project can be substituted or improved in the future, no solution is the ultimate solution.

• Collaborative: Data scientists don’t exists alone, they need a team, this team will make things possible for developing intelligent solutions. Collaboration is a big part of science, and data science should not be an exception.

• Creative: Most of what data scientists do is new research, new approaches or takes on different solutions, so their environment should be very creative and easy to work. Creativity is crucial in science, is the only way we can find solutions to hard and complex problems.

• Compliant to regulations: Right now there are a lot of regulations in science, not that much in data science, but there will be more in the future. Is important that the projects we are building are aware of these different types of regulations so we can create a clean and acceptable solution for the problems.

Seeing and seeing without practicing

If you are taking a class on anything related to data science, like math, statistics, programming or something like that, and you are there just listening to the class with this face:

Believe me, I was on online class for months. Maths, Python, R, principals of computer science and many others. At the same time. I wanted to learn as much as I could.

However, I lost the MOST important.

Drums please.

Practice.

Lack of Practice.

Lack of sort problems out.

Lack of efficient.

Practice.

Well you are wasting you time. Data science needs practice. Everything you learn, even though if the professor doesn’t tell you, practice and try it. This is fundamental to really comprehend things and when you are working in the field you will be doing a lot of different practical stuff.

A good knowledge on statistics, math and python won’t make you a successful data scientist. You need more, you need to master your craft. Be able to use these tools to solve business problems. So if you are learning something new, and you want to understand it for real, find a scenario where you can apply it or play with it.