What is Data Science?
Data Science is something very global. It mixes programming with statistics to generate models and apply them to the business world.
Therefore, knowing Data Science is not just knowing statistics or knowing how to program, it is much more than that. A Data Scientist must be able to ask the right business questions to detect improvement options within an organization and execute them with data.
It is true that in large companies these objectives are usually already given. However, in medium-sized companies that don’t have large teams of Data Scientists, many times the same person must carry out the entire process, from the extraction and processing of the data, creating the model, and putting it into production.
Why a Data Science blog?
Precisely because of the above, I myself have had to learn (and learn) many things that I did not learn during the Master’s Degree in Big Data & Business Intelligence.
When you have to learn and you are looking for how to do certain things is when you realize that, although a lot is written about Data Science… there are few blogs where they talk about real problems that data scientists experience on a daily basis and how to solve them.
That is why I decided to create this blog: to share what I learn in my work so that other Data Scientists don’t have to walk the same path.
That is why in this blog about Data Science you will find everything you need to be a good Data Scientist, from programming algorithms from scratch and fully understand how they work, to learn how to put those algorithms into production using Cloud services, either through API or automating them.
Also, if you have any suggestions you can always write to me to see if I can help you. In fact, one of my most popular posts, “How to Put an R Model Into Production”, was suggested to me by a reader!
Other Data Science Blogs
If you want to learn about Data Science, I’m sure that you will find my blog interesting. However, my blog is not the only one, as I have said, there are many good resources out there.
Therefore, in this section, I want to share with you other Data Science blogs and resources where you will surely find a lot of interesting material that, in fact, I also use to learn. There they go:
- The Tensorflow Blog: in this blog, you will find a lot of information about neural networks and deep learning, from already created models to how to put these models into production.
- PyImageSearch: if you want to dive into the world of computer vision, you must follow Adrian Rosebrock’s blog. In it, you will find a lot of highly specialized content on how to create and implement computer vision models using Python.
- Statquest by Josh Starmer: Josh is an incredible communicator that teaches is able to show you how some of the most complex machine learning algorithms work in a very funny and easy to follow way. So, ff you’re into machine learning, Statquest is a must.
- Towards Data Science: this is the main blog for Data Science. In it, you will find everything about Data Science: from model creation to production. In addition, I would recommend you subscribe to their newsletter, the content they offer is very good and personalized.
How to start as a Data Scientist
If you are new to the world of data and do not know how or where to start, I would recommend that you, first of all, choose a programming language.
As you can see, my main language is R, but I also know Python. If your question is which of the two to choose… I would tell you that if you are a programmer choose Python and, if not, R.
At the end of the day, the important thing about Data Science is not the language you use, but your ability to understand what you want and what you have to do to achieve it.
In fact, once you learn a language, you will see how learning another is not difficult. You simply have to understand its bases and find out what function does what you want.
Also, as you can see in my blog I write both Python and R. Come on, whatever language you choose, my data science blog will do the same for you!
In short, I hope you like the posts and, as always, if you have any suggestions, don’t hesitate to write to me!