Are you new to the concept named Kaggle? If you don’t know about it, then it’s okay, we’re going to answer it together. But first, you need to know a little background information about this data science network. So let’s get started.

First of all, what’s Kaggle? 

Kaggle was initiated in 2010; it is a well-renowned platform for arranging data science competitions with the notion that data scientists need a place to come together and team up on projects. This has transformed into a group with more than 1,000,000 registered users and has created a secure place for attaining data science learning, sharing, and competition.  

Some of the enlisted competitions have prize money of over $1,000,000 and numerous qualified competitors. Kaggle is an Airbnb for Data Scientists – this is where they spend their nights and weekends. Here, data scientists arrive from different parts of the world to solve data science, machine knowledge and analytics problems. 

To boost the human competitive spirit, Kaggle came up with this fantastic platform for organizations to host competitions that stimulated new methodology and techniques in data science and provided data that they were oblivious about. It created an openness to attain further information. Kaggle makes the environment competitive by awarding prizes and rankings for winners and participants. The prizes are monetary and also consist of attractive rewards such as jobs or free products from the company hosting the competition. 

According to the initiator, this rising growth can be attributed to supreme-quality content, data, and code allotted by Kagglers.

Most Kaggle users are highly dedicated to what they are doing; hence there are 4,000 forum posts per month and more than 3,500 competition submissions daily. This platform is the place to be for data scientists and machine learning engineers worldwide.

It’s not surprising that beginners do feel intimidated initially to get started on Kaggle. Their basis concerns are: 

How do I even begin?

Will I be up against teams of experienced PhD 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 a future in data science?)

How can I improve my rank in the future?

Well, if you have this kind of question in your mind, then you are in the right place. 

Here, we will share every necessary detail you need to know about to kick-start your journey, be more competent and enjoy your time on Kaggle.

How does Kaggle work?

While researching Kaggle in various websites and informative articles, I noticed three basic steps in Kaggle Competitions.  

1. Preparation: If you are determined enough to participate in such a prominent competition, you need to do your homework. The fundamental things which you should know before jumping into the warzone. When providing data, the host will offer you additional information such as a description, evaluation method, timeline, and rewards for winning.

2. Experimentation: You’ve had your morning coffee at this time, you’ve read all the necessary information in the overview 500 times, and you’re prepared to win 1st place. Now it’s time to conduct some experiments that will help you learn and follow-up the necessary procedure, which will help you ace the competition. You can submit your work in three ways:

Kaggle Kernels

Manual Uploads

Kaggle API

If you don’t want anyone to really know what you are up to, you should upload your experiments manually or using the Kaggle API. Kaggle Kernels are a way for participants to share what they’ve attained so far and acquire feedback from their mates. Kernels will provide you with ideas as to how to conquer the data, and I would suggest you go through some of the popular ones.

3. Results: In every competition, there are public and private leaderboards. Be cautious; each leaderboard is very different from the other. The public leaderboard relies upon a small percentage of the test data decided by the host. It gives you a good idea of what is going on, but it does not imply who will win and lose. The private leaderboard is the one where you should put your most focus on. This does not show the overall calculation till the end of the competition, the leaderboard is designed with a massive amount of data, and in the end, it decides who wins and losses. 

Tips for the Beginners who wants to participate in the Kaggle competition

First you need a few basic clarifications to move forward with the entire process.

Let us explain the differences between Kaggle and “Typical” Data Science.

Kaggle Competition

By nature, competitions that offer sufficient rewards such as prize money got few critical criteria which they expect the contestants would meet.

Problem must be difficult-  Competitions intend to assign complex tasks so that contestants can attain proper knowledge and be more competent. They give complex tasks to the participants, which requires both intellect and skills to ace those steps. It is a scheme to achieve the best return on investment.

Solutions must be new- To win the up to date competitions, you must have extensive knowledge, customize algorithms and trained advance models etc., to jump into the battle.  

Performance must be relative-  Competition must select a winner who would succeed excelling every phase of the given challenges, so that’s how you know you have intense competition.

“Typical” Data science

On the contrary, day to day science does not need to meet those specific criteria. 

Problems can be easy-  Here, data scientists are offered tasks that are extremely simple and easy to do. Assigned projects are elementary to complete. 

Solutions can be mature- They are assigned with the most common tasks such as data cleaning, A/B testing etc. Their mere job is to reinvent the wheel. 

Performance can be absolute- A solution is considered competent only if it matches the benchmark or exceeds it.

So is Kaggle worth your time?

Despite the critical differences between Kaggle and typical data science, beginners should give it a shot as this would immensely develop their knowledge signifying the true beauty of data science in the world.

1. Interesting and challenging projects where participants can learn and practice

Kaggle competitions involve solving challenging and analytical problems.  It especially is a great platform for beginners who are just trying to break into the data science field. Contestants will learn by doing the efficient method to be more proficient in the work field.

2. Insightful discussions with industry leaders and learned experts

Apart from the projects, Kaggle also arranges live discussions between several people on the platform. Such meetings are exciting, motivating and informative. Through these discussions, you can either seek advice from others or offer advice to people dealing with issues you comprehend.

3. Kaggle offers its interview a chance to learn about the most extensive data science community in the world

This platform is trusted by some of the world’s largest data science companies such as Walmart, Facebook and Winton Capital. On Kaggle, data scientists obtain experience and a chance to work on difficulties faced by big companies at present. While it does not assure, there is always the chance that the company will be impressed enough to hire you.

Steps that must be followed to get started on Kaggle

Next, we will give you a step-by-step action plan to jump start your journey on Kaggle. 

Step 1: Select a Programming language  

First, we would recommend you to pick one programming language and stick with it. Both Python and R are popular on Kaggle and in the data science community. 

If you start with a blank mind, we suggest you go for Python since it is a general-purpose programming language that you can use from end-to-end.

 Step 2: Learn the basics of exploring data

The skill to load, navigate and design your data is the first step in data science because it benefits to make various decisions, you’ll make throughout model training. 

If you follow our suggestion and take the route of Python, we recommend the Seaborn library which was developed particularly for this purpose. It has top-level operations for scheming most common and useful charts.

You can even watch this Python Seaborn Tutorial for better understanding. 

Step 3:  Train your first machine learning model

Before jumping into Kaggle, we suggest training a model on an at ease, more controllable dataset. This will allow you to become acquainted with machine learning libraries and the lay of the land.

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

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

 Step 4: Tackle the ‘Getting Started’ competitions

Now we’re ready to try Kaggle competitions, which fall into several classifications. The most common ones are:

•Featured – These are usually backed by companies, organizations, or even governments. They have the lucrative prize tools as well. 

•Research – These are research-oriented and have little to no prize money. They also have non-traditional submission methods.

•Recruitment – These are sponsored by companies who want to recruit data scientists. These are still somewhat uncommon.

•Getting Started – These are structured like featured competitions, but they have no reward pools. They include more accessible datasets, good 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 many community-created tutorials also support them. 

Step 5: Compete to maximize learnings, not earnings

The entire foundation is based upon; it’s time to develop into ‘Featured’ competitions. In general, these will require much more time and effort to be positioned well.

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

While prize money is lucrative, the more valuable (and reliable) reward will be the skills you’ll develop for your career.