Switching careers: from Designer to Data Scientist

 
Switching careers-.jpg
 

I have always had this curiosity about numbers and data, but there was also an eagerness to make them visually appealing and easier to understand. For seven years I have created Infographics and Data Visualizations for tech and marketing companies. It has been my passion to make numbers, trends and stats look beautiful and easy to understand.

But in 2018 this new job became popular, and there was a considerable noise around it: "DATA SCIENTIST."

It took about six months to make up my mind and decide between becoming a UX designer or a Data Scientist.

Data science won!

I chose data science because of my background in data visualization and storytelling.

The first step taken in this direction was to do research and see what data science is all about.

The second step was to look for data scientist and figure out what programs and skills they have so I can decide on what I want to learn.

I also took a look at job posts and made a list of skills and programs companies require.

What I did next was to look for classes that teach the basics in SQL, Python, NumPy/SciPy, Java, Statistics and interactive Data Visualization tools. I already know Exel, Data Visualization and I am familiar with coding.

Although I am planning to find a job that is more connected to data visualization, it is crucial to understand what it means to work with data and databases.

These are the places that I found the most useful in my journey:

1 Codecademy.com- This website offers different paths and great projects to help you practice. I am currently taking the Data Science path, which means they start with SQL . The rest of the course looks like this:

  • The Importance of Data and SQL Basics

  • SQL: Basics

  • SQL: Intermediate

  • Python Functions and Logic

  • Python Lists and Loops

  • Python Cumulative Project

  • Introduction to Statistics with NumPy

  • Hypothesis Testing with SciPy

  • Data Analysis with Pandas

  • Data Visualization

  • Visualization Cumulative Projects

  • Machine Learning: Supervised Learning 🤖

  • Machine Learning: Unsupervised Learning 🤖

2. Edx.org

3. Lynda.com- can also give you certifications for your Linkedin profile.

As I am only a few months in this transition, I am still working as a graphic designer specialized in data visualization and infographics, but I try to study at least 2 hours a day and weekends.

I expected to be very hard, but so far I can keep up!

Why do I feel that my design experience can benefit data science?

Graphic Design in Data Visualization tools, such as Tableau, is vital because you still have to apply the correct settings and configurations to apply design principles.

Data is just "numbers" without a story, and this is where my Infographic experience will come in handy. Also, when presented to a less educated audience in the field, you need someone to show the complex information quickly and clearly so the main idea can be grasped in just a few seconds.

Design helps inform your audience by using the human visual system’s ability to see patterns and trends. In some cases, graphical elements such as text and typography are used on purpose to send a clear message.

Business intelligence expert Stephen Few sums up his disdain for the ornamental aspect of infographics: “When visualizations are used primarily for artistic purposes, they are not what we call data visualizations or infographics, which are terms that have been in use for a long time with particular meanings.”

David McCandless, on the other hand, describes his work differently: “I love taking all kinds of information – data, numbers, ideas, knowledge – and making them into images. When you visualize information in this way, you can start to see the patterns and connections that matter.”

Visualization can serve a great help to data as it can amplify the cognition of measure both the efficacy and reveal the integrity of that data.

But what the most important value an infographic designer can bring to data is storytelling. The ability to have a narrative makes data more human and relatable. People love stories; stories persuade, stories help us imagine.

Alberto Cairo, infographics professor and author of The Functional Art, put it like this: “So you’ve amassed terabytes of data. Now, tell a story . ”As another example, journalist Reg Chua described a particular compelling visualization: “It’s not simply a dump of data, but one designed – intended – to persuade.” The concept of persuasion regularly comes up as a recurring theme in discussions about the purpose of visualizations, especially infographics.

Data science has many branches, and data visualization is one. I am aiming for becoming a data science with visualization skills. I want to tell stories and make data pretty.

Graphic design is a problem-solving career. Graphic designers know how to solve client's problems and they know design principals that will make a difference in how data is presented and consumed.

Designers know that people forget 90% of the information we see in a day.

Knowing design principals like high contrast, signalling colours, interactivity, using the magic number of objects and how to steer focus can create the empathy that leads to storytelling.

I believe design is a great tool in data science and it can lead to more efficient processes, more leads, more insights and faster results.

What are your thoughts on data science?