Georgios Triantopoulos’ interest in physics started at a young age. He has always wanted to understand the principles according to which everything works. To him, mathematics and physics came to summarise the reasons of Being into elegant theories and formulae.
And now he is a Data Scientist.
One could think in the end he probably didn’t like physics that much if he ended up pursuing a totally different career. But that would be a wrong assumption.
The Physics of Data
Physics is one of the oldest academic disciplines, Simplifying to the core, its main goal is to understand how the universe behaves. Data science, instead, uses scientific methods, processes, algorithms and systems to extract knowledge and insights from structured and unstructured data.
For Georgios, the two are deeply connected: “In physics and in data science you need to prepare and normalize your data for experiments.”
And it is useful to have the skills of a physicist when processing data! Indeed, “with a physics background, you can categorize data into different frequencies,” Georgios explains. “So you can identify the frequency that resembles the characteristics of noise. By removing this part of the data, your results are more robust.”
In other words, it is like searching for shells in the sand. Physics is like a sieve that makes the searching process more accurate.
Becoming a Data Scientist
However, even though coding and physics do have their intersection, the latter only touches the basics of programming skills. The road to becoming a Data Scientist still needs to be paved. And to do that, self-training is crucial.
“For the advanced programming skills, you have to learn it by yourself,” Georgios smiles and adds, “I like to go on different websites with tutorial videos and data to download. So I can do mini-projects by myself and hone my skills.”
He has done many mini-projects in the past. Recently, he has been playing with the data collected from cleaning robots. These robots automatically detect the home environment and build a memory database. They “remember” where the carpet lies, where the chairs are, etc.
Georgios’ goal is to use the three-dimensional spatial model of the cleaning robot to improve its performance. For instance, after remembering the positions of the furniture, it will also be able to calculate the optimal routine for a faster cleaning.
A place to keep learning
When asked why he chose to work with Dashmote, Georgios names data again.
“I like the visual recognition technology that Dashmote owns. And I like the idea of turning data into insights. After all, data means power today. Moreover, I like the multi-cultural environment of this company. I have the chance to interact with people of different cultures and backgrounds, and I am learning a lot”
Georgios is in constant touch with Bruno, Data Scientist in our Shanghai office. They exchange ideas for handling data and test it, share tutorials and opinions about new techniques and improvements in their field. It is a constant learning process.
The future of data science for business
According to Georgios, the potential of artificial intelligence is enormous, especially considering it finds its main applications in industries in fast evolution. Every company has big data in its future, and it is only a matter of time for data analytics to take the spotlight.
For example, right now, visual recognition technology can already reveal consumer preferences and allow businesses and enterprises to understand their audience better. AI-based marketing solutions are winning the marketers’ hearts.
“This is just the start,” Georgios said, “the best has yet to come.”