The Beginner’s Guide to Kaggle

It's no surprise that some beginners hesitate to get started on Kaggle. They have reasonable concerns such as:

How do I even start?
Will I be up against teams of experienced Ph.d researchers?
Is it worth competing if I don't have a realistic chance of winning?
Is this what data science is all about? (If I don't do well on Kaggle, do I have future in data science?)
How can I improve my rank in the future?
Well, if you've ever had any of those questions, you're in the right place.

In this guide, we'll break down everything you need to know about getting started, improving your skills, and enjoying your time on Kaggle.

Kaggle competitions
By nature, competitions (with prize pools) must meet several criteria.

Problems must be difficult. Competitions shouldn't be solvable in a single afternoon. To get the best return on investment, host companies will submit their biggest, hairiest problems.
Solutions must be new. To win the latest competitions, you'll usually need to perform extended research, customize algorithms, train advanced models, etc.
Performance must be relative. Competitions must crown a winner, so your solution will be scored against others'.
"Typical" data science
In contrast, day-to-day data science doesn't need to meet those same criteria.

Problems can be easy. In fact, data scientists should try to identify low-hanging fruit: impactful projects that can be solved quickly.
Solutions can be mature. Most common tasks (e.g. exploratory analysis, data cleaning, A/B testing, classic algorithms) already have proven frameworks. There's need to reinvent the wheel.
Performance can be absolute. A solution can be very valuable even if it simply beats a previous benchmark.
Kaggle competitions encourage you to squeeze out every last drop of performance, while typical data science encourages efficiency and maximizing business impact.

So is Kaggle worth it?
Despite the differences between Kaggle and typical data science, Kaggle can still be a great learning tool for beginners.

Each competition is self-contained. You don't need to scope your own project and collect data, which frees you up to focus on other skills.
Practice is practice. The best way to learn data science is to learn by doing. As long as you don't stress out about winning every competition, you can still practice interesting problems.
The discussions and winner interviews are enlightening. Each competition has its own discussion board and debriefs with the winners. You can peek into the thought-processes of more experienced data scientists.

How to Get Started on Kaggle
Next, we'll give you a step-by-step action plan for gently ramping up and competing on Kaggle.

Step 1: Pick a programming language.
First, we recommend picking one programming language and sticking with it. Both Python and R are popular on Kaggle and in the broader data science community.

If you're starting with a blank slate, we recommend Python because it's a general-purpose programming language that you can use from end-to-end.

R vs Python for Data Science
How to Learn Python for Data Science
Step 2: Learn the basics of exploring data.
The ability to load, navigate, and plot your data (i.e. exploratory analysis) is the first step in data science because it informs the various decisions you'll make throughout model training.

If you go the route of Python, then we recommend the Seaborn library, which was designed specifically for this purpose. It has high-level functions for plotting many of the most common and useful charts.

Python Seaborn Tutorial
Step 3: Train your first machine learning model.
Before jumping into Kaggle, we recommend training a model on an easier, more manageable dataset. This will allow you to become familiar with machine learning libraries and the lay of the land.

The key is to start developing good habits, such as splitting your dataset into separate training and testing sets, cross-validating to avoid overfitting, and using proper performance metrics.

For Python, the best general-purpose machine learning library is Scikit-Learn.

Python Scikit-Learn Tutorial
Step 4: Tackle the 'Getting Started' competitions.
Now we're ready to try Kaggle competitions, which fall into several categories. The most common ones are:

Featured - These are usually sponsored by companies, organizations, or even governments. They have the largest prize pools.
Research - These are research-oriented and have little to no prize money. They also have non-traditional submission processes.
Recruitment - These are sponsored by companies who want to hire data scientists. These are still relatively uncommon.
Getting Started - These are structured like featured competitions, but they have no prize pools. They feature easier datasets, plenty of tutorials, and rolling submission windows so you can enter them at any time.
The 'Getting Started' competitions are great for beginners because they give you a low-stakes environment to learn, and they are also supported by many community-created tutorials.

Kaggle Getting Started Competitions
Step 5: Compete to maximize learnings, not earnings.
With that foundation laid, it's time to progress to 'Featured' competitions. In general, these will require much more time and effort to rank well.

For that reason, I recommend picking your battles wisely. Enter competitions that will expose you to techniques and technologies that align with your long-term goals.