When a data scientist builds any kind of data science project, they are often building it to uncover secrets and informa

Google Photos was my favorite photo service. Until now I’ve been hopelessly optimistic about its potential. Even though I’m leaving it behind, it’s got a lot going for it. It’s seamlessly integrated with your Google account, has the best user interface and iOS/Android apps.

Most of the time, this is done in an academic or a business-oriented manner. One of the skills that every data scientist must develop is the ability to tell a compelling story with their data.

But most importantly, I don’t want to lock myself into an infinite cycle of increasing storage costs. For those reasons alone, it doesn’t make any sense for me to invest time and resources into Google Photos.

But as you probably already know, Google decided to remove its best feature; free unlimited high-quality uploads. Soon, you’ll need to buy a Google One subscription if you want more than 15Gb of storage.

Before you go very deep with such advanced topics, you need to make sure that you’ve built a solid foundation of machine learning basics. Mastering the basics will not only strengthen your skill base but will give you the knowledge necessary to pick up any advance and new concepts faster and with ease.

There are many publically available datasets that you can use to practice data visualization, building dashboards, and telling a story with your data. Some of my favorite ones include: FiveThirtyEight, Google’s Dataset Search, Data is Plural, and of course we can’t talk about datasets without mentioning Kaggle.

For people like me who shoot and upload unreasonable amounts of photos, even 2TB isn’t enough. I’d need to pay for at least the 10TB plan, which would cost $50/month. Not only is this unaffordable for most of us, I see two other major problems with Google Photos:

Landing a good job in data science can be quite challenging due to the huge pool of applicants and people interested in the field. To stand out among others, your portfolio needs to prove that you have a solid foundation of the basic concepts of data science.

They still don’t allow any folder or sub-folder organization. They just want you to search for stuff. Yes, their search is very good and I use it frequently. Still, I assume most people want better album organization.

In order to stand out, you need to be a good story-teller. Your data needs to effectively visualize. Luckily, there are many recourses where you can learn and practice your data visualization skills. You can read articles about visualizations, or go through effective visualizations courses.

One of the things that can make or break your chances of landing a data science job is your machine learning fluency. Sometimes when newcomers join the field, they tend to skip over the basics and jump straight into the more advanced “buzzwords” of the field.

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a step further. Your code isn’t saved in some separate file you may occasionally load up in your REPL environment. No, the whole development process happens in this interactive environment and the whole thing gets dumped to disk. Essentially you have no separate text files that you manage.

Make sure to have projects that overs all machine learning basics, such as regression (linear, logistics, etc.), classification algorithms, and clustering. Some of my favorite recourse on machine learning basics are the machine learning basics chapter of The Deep Learning Book, and the CodeAcademy machine learning course.

SmugMug is probably the best photo service out there. It has the best organizing and sharing tools, a great user interface, and pro features like the ability to sell photos, attach a domain, or even edit the CSS