AI News, How to choose a good data science project to practice data science

How to choose a good data science project to practice data science

However, projects can be extremely useful for practicing data science and refining your skillset, if you know how to select the right project.

It’s based on the idea that the best way to learn a new skill is to select a large project and just build, even if you don’t know most of the requisite skills.

While I will admit that it is possible to learn a new skill by jumping into a new project, you have to understand that it’s extremely inefficient.

That’s another way of saying that if a beginner selects a project that’s too big, they’re likely to learn very little (although, large projects can be very useful for advanced practitioners).

The reason for this is that if you choose a project that’s too big, and you don’t know most of the skills, you get bogged down just trying to learn everything before you can move on to getting things done.

to a very complicated project, but you don’t know the requisite skills, you’re going to spend 99% of your time just looking things up.

Essentially, if you try to work on a project that’s too large or too complicated, you’ll spend all of your time trying to learn dozens of small things that you should have learned before starting the project.

More importantly, it would take years of preparation by learning thousands of little skills before you’d be at a level to perform like this.

For beginning guitarists, it’s much, much more effective and efficient to start with the absolute foundational guitar skills, master the foundational skills, and progressively move on to skills of increasing difficulty.

It’s much more effective to put together a systematic plan with a skilled teacher that puts you on the path to your goal in structured way.

and try to lift an amount of weight that’s far beyond your strength level, you’re likely to fail.

In data science, you won’t have a risk of injuring yourself physically, but you might incur a different sort of damage: you might injure your ego.

There’s actually a much better way to become a strong data scientist and it’s a lot like trying to get strong in the gym.

In the gym, the best way to get strong is to start with light weights, and learn the basic motions safely with those low weights.

Similarly, in data science, instead of jumping into a project with a high difficulty level, you should start with something small and do-able with your current skill level, then increase the size and complexity of your projects as you learn more over time.

This is just like a guitar player: a guitar player might practice a guitar scale every single day for a few weeks (or years).

It allows you to take ggplot() and filter() – which you should have practiced separately – and integrate them in a way that produces something new and more complex.

So if the project requires bar charts, histograms, data sorting, adding new variables, etc, you should already know those skills.

However, even if you’ve learned and practiced the required tools, when you dive into your project, you’ll begin to find little gaps.

Let me give you an example: when you’re starting out with ggplot2, I recommend that you learn 5 critical data visualizations: the bar, the line, the scatter, the histogram, and the small multiple.

But after creating the basic visualizations to analyze the data, you decide that you want to make them look a little more polished by modifying the plot themes.

If, at that point, you haven’t learned ggplot2::theme() and all of the element functions (like element_line() element_rect(), etc) then you’ll have a hard time formatting your plots and making them look more professional.

To get the benefits from project work, the critical factor is selecting a project that’s at the right skill level: not too hard, but not too easy.

Having said this, when you consider a new project, you should just ask a few simple questions: Here’s an example: about a year ago, I did an analysis of a car dataset that I obtained online.

After identifying the tools and techniques you’ll need for a project, here’s a good rule of thumb: you should already know about 90 percent of the tools and techniques.

For example, if you’re working on a project that requires about 20 primary tools or techniques, you should be able to execute roughly 18 of those techniques.

So to restate, here’s a good rule of thumb: when you start a project, you should already know about 90% of the techniques (and the remaining 10% will force you to stretch your skill).

For example, if you’ve been a data scientist for a year or two, and you’ve made a few hundred bar charts and line charts, then choosing a project that uses only the basic tools might be little too easy for you.

Having said that, as I mentioned above, projects are an important part of a systematic learning plan because they help you integrate what you’ve already learned, they help you identify skill gaps, and they can push you beyond your comfort zone.

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