Iliya Valchanov, Co-founder of 365 Data Science
Iliya is the co-founder of 365 Data Science. HeĀ is the author of our online courses in Mathematics, Statistics, Machine Learning, and Deep Learning. Iliya has demonstrated a remarkable affinity for numbers since childhood, winning more than 90 national and international awards and competitions over the years. He first started teaching at university while he was helping other students with their statistics and econometrics studies. Now, Iliya has authored and instructed 4 online courses (and counting!) and is here to answer our meet-the-team questions.
Hi Iliya. Nice to have you here. Letās start with a quick get-to-know-you challenge. What is your 15-second bio?
Hey. I am a Finance graduate with a strong quantitative background who ended up as an entrepreneur at an educational start-up focused on data science.
Whatās the least useful talent you have?
Haha, I donāt know how you came up with these questions.
Letās break this down.
Since the word ātalentā is inherently positive, talents are not regarded as useless per se. To be able to answer your question, in this context Iāll think of a talent as: āsomething Iām naturally skilled at, no matter if Iām good at itā.
Alright.
My least useful talent would be running. When I was younger, I was faster than most people my age, and Iāve even won a regional relay race. However, I never pursued running further than that. Now I can call my running āsafe for my kneesā. Iād rather not use a velocity measuring word. However, I doubled down on another talent I have ā mathematics.
I believe most people have many talents, but usually, they never pursue or never discover them. To be successful at something you need hard work, not talent. Otherwise, youād be as good at that skill as Iām at running now.
To be successful at something you need hard work, not talent.
Wise words spoken. Now letās continue with a practical question. What is the most useful nifty tool that you discovered or were introduced to, which you now canāt live without?
Hey, thatās a question I like to ask people, not vice versa!
Iāll tell you two. One that’s data science related and one thatās just rad.
First, if you are handed a new computer and you need to install apps in bulk, just use ninite.com. Theyāve got everything from web browsers, through compression software, Spotify, Dropbox, Skype, antivirus programs, even developer tools such as Python.
It works like this: you tick the boxes next to the apps you need and then download a custom installer. You run the installer and everything youāve chosen will be installed at once, turning an hour-long agony into a 5-min job.
The second one is data science related as promised. If youāre using a weak computer (Iāll assume your CPU or GPU donāt allow you to create the deep learning algorithms that youād like), you can use Google Colab. Itās just incredible ā you open it in your browser, select the Python version you want to code in and then utilize Google servers for your deep learning efforts. The end. No need to install Anaconda, Python or whatever. Each time I donāt have my primary computer, Iād go for coding in Google Colab.
These are some really great tools, Iliya! Thank you for the insight! You say youāre a foodie. Do you have a favourite weird food combination?
That is correct, but whatās weird for Europeans may not be weird for Asians and vice versa. Food is never weird, unless itās disgusting, haha.
Joke aside, while I love Italian and Asian cuisine, people at the office know that if I had to pick one dish, it would be: beef with rice and prunes.
Yes, thatās the one! You’re also a skilled data science instructor. Off the top of your head, what are the top 3 common challenges most beginner data scientists must face and overcome in their day to day professional life?
First, in data science, thereās always something new. Yesterday I stumbled upon a paper from April 2019 called: āExploring randomly wired neural networks for image recognitionā. I felt compelled to get acquainted with the work on my phone at 1 AM. Funnily, my girlfriend approached me and asked: āWhat are you reading?ā. I had to say with a bit of nerdy shame in my voice: āWell, about randomly wired neural networksā. She just walked away in silence.
Either way, Iāve worked with ResNets and DenseNets, where you wire NNs in unconventional ways, but still following some pattern when you wire them. However, the paper showed that if you wire the net randomly, youād get competitive results to the best algorithms out there. The takeaway for me was that if random wiring works so well, focused wiring based on some conceptual ideas deserves a lot more attention. The takeaway for you is that data science and ML are quite new. The industry is always changing and so should you.
The industry is always changing and so should you.
Second, you must be comfortable with at least one programming language. There are great drag-and-drop tools, but donāt be fooled. Thereās nothing better for data science (as of today) than an open source high-level programming language like Python or R. In fact, they aren’t too hard to learn. What’s important is to have a programming mindset. I acquired mine from C++. So, if you want to get into data science, make sure you devote enough time to coding.
Finally, you have to practice. You just learned a new model or technique? Take a dataset you already know very well and have worked on and see how you can apply your new knowledge to it.
One of the best examples I remember was a trainee who just graduated from college and was just getting acquainted with PowerBI (not a new āmodelā, but a new tool). To get familiar with the software during his training, he loaded the data from his college dissertation ā a dataset he had been working on for months and knew inside and out. He knew how the graphs should look and what types of computational fields he used to create. The only novelty in the process was the tool ā PowerBI. I think thatās the proper approach to learning new skills ā you have to isolate the novelty, turning the problem into āall else equalā or āceteris paribusā as we like to say in data science.
Iliya, thank you so much for this helpful interview! Looking forward to your next data science courses!
Do you provide data science internship?