What does a newbie do in the age of technology when they single out data science as their career path?
They keep stacking data science certificates and share them on LinkedIn.
What’s the probability of failing to earn those certificates?
Close to zero.
Personally, I’ve always wondered why people keep bragging about their data science achievements. I’ve never done that, and I don’t have the slightest intention of doing it anytime soon.
Data science certificates are like a drug. Some people would skip video lessons, run the code solution without knowing what it is about for the sake of earning the final good-looking, sharable certificate. That’s time wastage.
The point, however, from data science courses is to help you test the water and know what to expect from the field and whether or not it’s the right career path for you, aside from the monetary appeals.
If you want to become a data scientist for money, you’d better raise the bar a little and try to become a doctor. They earn twice as much as data scientists.
Now, to get the most out of the data science certificates, you should pick courses wisely.
For example, a few years back, I took a course from Coursera delivered by IBM called “Introduction to artificial intelligence (AI).” Honestly, it was a complete waste of time. The course was 8 hours long. The content was pretty much centered around answering one question: what are the advantages of AI. Something you could spend 10 min or less on.
But, in the end, I learned an important lesson from the experience. That is to run away as far as I can when I see a course without a problem to solve or without code. This is crucial.
Kaggle is the stronghold of data scientists.
Firstly, Kaggle boasts of an extensive up-to-date batch of datasets that spares you the trouble of searching for raw data or scraping it.
Scraping data is not as easy as it might sound. Some websites, such as Facebook and LinkedIn, have strict policies against data scraping and data crawling. One should be ready to say goodbye to their account if they were busted.
Secondly, you should know that data training, an essential part of the data science pipeline, requires tremendous computational power.
Kaggle offers exactly that by providing access to state-of-the-art machines through their servers. This means that you don’t need a high-end computer to run your models on, saving you a great deal of time and resources.
Thirdly, Kaggle has a vibrant and supportive community geared towards data science. Whenever you have a data science question, head to the discussion section, check out the already existing threads, or you can start your own.
Best of all is that Kaggle instills a competitive spirit in its members by providing monetary rewards to the best data scientists out there.
So why wait?
Join Kaggle and start sowing and reaping knowledge.