Quality Data, Quality Decisions: Why Web Scraping is Essential for Advanced Analytics
Gediminas Rickevičius·9 min
Data Science & Analytics
Being Good at Google is a Skill
Luka Beverin
August 11, 2020·4 min read
Being good at ‘Google” is a skill. Yes, you heard me. Knowing what and how to Google or search for something is an undervalued asset, especially in the fields of Data Science and developing. The problem is that nobody wants to admit that they’re good at “Googling” because having to Google something in the first place is construed to knowing nothing about that which we are searching for. This couldn’t be further from the truth. Google is there to help the ones that know something, not the ones that don’t know anything.
There is a plethora of information and resources available on the internet with more and more being added every second. Anyone can contribute to the expansion of the internet, and this feature comes with its own pros and cons. The pro: to be connected, learn and share ideas. The con: you can’t trust everything on the internet. Knowing how to sort through the rubble is as important as finding the rubble.
Nowadays people learn to play the guitar and other various instruments on YouTube. Disregarding the costs of internet, these guitar lessons are essentially free and such is the case for many other available resources (think in terms of books, music and academic works). In the sense that we want to learn how to play the guitar, the “Googling” aspect is rather straightforward because we know the end goal: learn how to play the guitar. What happens if we don’t know what the goal is? Taking it even further, what if we know what the goal is, yet we don’t know how to find the correct information to help complete the goal?
This is where being good at “Google” comes into play, or in the context of Data Science, being good at navigating Stack Overflow (a popular online community of developers). If you believe that coding is exactly as you see it in the movies, I am sorry to say that you will be largely disappointed. Most of the time, programmers spend hours or even days figuring out what to code in order to get the result that they want. They usually have the idea in mind, but translating that idea into a computer algorithm is a different story. They have all the ingredients without the recipe.
This is where being good at “Google” comes into play, or in the context of Data Science, being good at navigating Stack Overflow (a popular online community of developers). If you believe that coding is exactly as you see it in the movies, I am sorry to say that you will be largely disappointed. Most of the time, programmers spend hours or even days figuring out what to code in order to get the result that they want. They usually have the idea in mind, but translating that idea into a computer algorithm is a different story. They have all the ingredients without the recipe.
When to Google?
At one point or another, a developer will be required to complete a task in a programming language that he or she has never experienced before. For example, asking a Data Scientist to create a webpage (Python to Java). Every programming language comes with its own syntax and the developer will have to master the syntax if they want to be successful in the language. To Google the workings of a new syntax, you must already know what it is that you want to do. For example, the syntax for installing packages in R Studio and Python is completely different, but the difficulty gap closes if you can transfer your programming understanding between languages. In such a simple scenario, you’d Google something along the lines of: How to import packages in R/Python? Sometimes you will have to Google something because you genuinely don’t know what else to do. This process may come at different times in a project. It could come at the beginning when you just need an idea to start. It could come somewhere in the middle, where you need help to tune a certain parameter, or it could come in the end when it’s time to interpret the results of the model. In general, Googling is turned to when we have a problem that we can’t solve, in the hope that somebody else has had the same problem, but has solved it for you.
Isn’t it just copy and paste?
No data scientist or programmer worth their salt would survive by continuously copy-pasting code from Stack Overflow. Reiterating, Google is there to help developers that know something and not the one’s that don’t. Most likely you’ll only get answers to a piece of the puzzle and not the entire solution. Once you “copy and paste”, it’s still up to you to make sure that the added code is understandable, reproducible, robust and more. For example, a lot of aspiring Data Scientists are applying XGBoost to all data under the Sun (seriously guys, sometimes linear regression is good enough). It might be beneficial in some cases, but the problem is that they have no understanding of the parameters and how to fine-tune them in order to get the best results with the data that they have. As you can see, we should Google the stuff we Googled. I am seriously considering putting “Good at Googling stuff” on my CV. Until then, the term “resourceful” will do just fine in its place. Finally, giving credit to people’s work is more important than the purpose for which you are copying. If you are unable to give credit where it is due, please reconsider using their information, especially when they are providing such an upright service for the developer’s community.📈DataDrivenAlphaBetaDDI Smart Block
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As a current Masters in Statistics student, Luka is eager to simplify complex topics and provide big-data solutions to real-world problems. He also has an educational background in actuarial and financial engineering. In his spare time, Luka enjoys traveling, writing on machine learning topics and taking part in data science competitions.