Data Science 101
By: Arthur Mello
Tags: Data, Data Science, Supervised Learning, Science, Analytics

Photo by Franki Chamaki on Unsplash

Data Science 101

What is data science?

Well, if you have just woken up from a 10-year coma and have no idea what is data science, don’t worry, there’s still time. Many years ago, statisticians had some pretty good ideas for analysing data and getting insights from it, but they lacked the computational power to do it, so their hands were tied. Until one day, when computers managed to catch up with those guys, and made all their dreams come true. All of a sudden, we not only had more data available than ever in history, but we also had powerful machines to perform heavy calculations on this data, allowing statisticians to try out all these new algorithms. Data science is the hip daughter born from this marriage between statistics and computer science. In other words, it is the science of extracting useful patterns from data sets by use of computer power.

What is it used for?

One of the reasons data science is so popular nowadays is the number of possible applications that are emerging.

Marketing and sales

A typical use case for data science in marketing is product recommendation. When you check out a product on Amazon and they tell you there’s another product you might like, there is an algorithm behind that recommendation that thinks you will like those products based on what other customers who also saw that product actually bought.

Finance

The most common way that banks use data science methods is for credit risk analysis: back in the day, when someone asked for a loan, usually the banker took a good look at their financial record to decide whether to do it or not. Nowadays, there are sophisticated statistical models that are constantly updated and give a good estimated probability of default, making the whole process a lot faster and more reliable.

Healthcare

Healthcare is one of the most promising industries when it comes to data science. There is a lot of data being generated by connected wearables such as smartwatches, including calories spent, miles walked and heartbeats. One of the possible applications is tracking variables that can help explain some diseases, and even remind you to go see a doctor if you present a behavior that might indicate a health issue.

What questions does it answer?

We can split data science tasks into two main groups: supervised vs. unsupervised learning

Illustration of a linear regression for one explanatory variable. (Image by author)

Supervised learning

Supervised learning comprises all tasks for which we have a target variable, that is, some feature in our data that we already know we want to predict. For example, if we want to explain house prices based on their characteristics (such as number of rooms and floors), or if we want to predict the likelihood that a customer will stop using our services.

Unsupervised learning

These are the tasks for when we are not sure of the question we are asking. A typical case is clustering tasks, when we just want to find patterns in the data, not necessarily related to one specific variable (customer segmentation, for instance).

Who does it?

Besides the knowledge required in statistics and computer science, data science also calls for business awareness: no matter how good your algorithms are, they will be useless if they are not applicable in that domain. People who work with data usually fall into three categories, depending on which one of those three areas of expertise they are more focused on:

Data analyst

Sometimes also called business analyst, this guy knows how to talk to people who don’t work directly with data. He’s usually in charge of translating business needs into data requirements (and data insights into business recommendations). He has an overall understanding of the main data science algorithms, and usually has really good skills in data visualization.

Data engineer

This is the person who makes sure the data is collected from all its sources, integrated almost seamlessly into the company’s tech environment and that all the algorithms developed turn well and fast. They almost always come from a tech background, and sometimes have to create dedicated tools to display the data processes, especially if they are to be shared with other stakeholders in the company.

Data scientist

As you can guess from the name, this guy has a deeper understanding of the way most algorithms operate, and which are the best ones for each situation. They probably know more about statistics than the data analyst and the data engineer, but less about the ins and outs of the business or of the process industrialisation. Some companies prefer to hire PhD’s for this position, but it is not always the case.

Where is it going?

In the next few years, we will see much progress in many different domains. By using data, cities will be able to better manage their traffic, their energy consumption and even their police units allocation. By the use of wearables, we’ll be able to exercise, eat and sleep better. And there might be many other possibilities of which we haven’t even thought of.

However, we will also find out that not everything can be improved with data, and we will soon find out where this limit lies. There will always be an important random component in every human activity or natural phenomenon that will never be tracked by any machine learning algorithm, no matter how sophisticated it is.

This data-driven culture might also cause some important behavioural changes. People are starting to realize how much of their personal lives is being tracked by big companies and the government, and most do not seem to enjoy it. This might lead people to voluntarily downgrade their tech devices, use tools to prevent data collection, and even reduce their overall technology usage. Governments are already aware of these concerns, and regulation is getting stricter all over the world when it comes to people’s privacy. Let’s see in the years to come how this will shape society (the Black Mirror series offer interesting insights into these possibilities).

How to do it?

If you want to learn more about it, I recommend the MIT Press Essential Knowledge series book “Data Science”, by John D. Kelleher and Brendan Tierney. It is a very good introduction to the subject, without getting too technical, to help you see if data science is really for you.

Next in line is “Data Science for Business” by Foster Provost and Tom Fawcett. This one is more focused on business applications and it goes deeper into the details of the algorithms. It will give you a really good grasp of all the possibilities enabled by data-driven decision making.

Then, once you got the basics covered, it’s time to study for real: you will almost certainly need to learn to code (if you don’t know it already). The main languages you should focus on are SQL and R or Python. The first one is used to querying databases to extract the data you need, in the right shape. The other two are used for applying the algorithms and creating plots. R was created with a focus on statistics, whereas Python is a more general programming language. To start with, just choose one of the two to concentrate your efforts and, if needed, learn the other one later on.

A good way to start practicing your skills is Kaggle.com, where you can play with toy datasets and take part into real competitions. It will help you put your knowledge to test and also build a portfolio of your own. However, keep in mind that eventually, you will need to work with real-life cases, it’s a different beast.

Conclusion

Now that you know some of the data science lingo, you are able to go out there and do your own research. The amount of available resources is pretty much endless, and there’s new information coming out every day, so make sure you are always up to date on the new methods and possibilities.