Imagine being a beginner and attempting to compete against teams that boast decades of knowledge and experience tackling challenging problems such as analyzing complicated satellite data and predicting the sale price of a house etc. Kaggle can be overwhelming and intimidate for a beginner. This is mostly due to lack of experience or the simple fact that they are competing with hundreds of seasonal data scientists who are more experienced and know what it takes to win the competition.
Before we get started here are some basics to keep in mind: every single Kaggle competition is self-contained – this means it is not necessary for you to scope other projects for data. This is incredibly freeing as you can focus all your energy on other necessary tasks. Consistent practice is the only way to improve and enhance your data science skills – the best way to become better at data sciences is by doing it as often as you can. Consistent practice means your aim is learning and not to focus all your attention on winning. As long as you do not stress about winning, you will find the practice mentally stimulating and will enjoy it along the way. And the forum at the end of the competition is enlightening – a beneficial part of the learning process when engaging in a Kaggle competition is getting the chance to have insightful and informative discussions with the most brilliant minds in data science. When a project is ongoing there are discussion boards and the chosen winner is also interviewed; this gives every contributor a sneak peek into the thought process of knowledgeable and experienced competitors.
Step by step guide on how to get started on Kaggle
This step by step action plan will help you know how to navigate this platform even if you are a beginner with no prior knowledge:
Step 1: Choose a Programming Language:
It is advisable to choose a single programming language and stick with it. Two of the most popular programming languages on the Kaggle data community are R and Python. For the beginners who are completely starting with a blank slate, it is advisable to go with python. This is because Python is essentially a general-purpose programming language that is easy to use from end-to-end. Although both programming languages come in handy for Kaggle competitions, each is specifically suited for certain problems. While R is the right choice for data analysis, Python is suitable when you are dealing with statistics code or data integrated with web apps
Step 2: Data Exploration
Exploratory analysis is an indispensable first step in data science because it helps you ascertain the decisions that will be made in the model training process. You will understand different features, statistician distribution of values and learn about null and missing values in the process. Seaborn library is a popular and highly recommended for data exploration for Python users. It provides high-level functions to plot and visualizes the data.
Step 3: Start Simple
Before you get started on Kaggle, take your time to train and practice on a manageable and simpler dataset. Practice on an easier dataset is recommended because it helps you understand the lay of the land and also helps you familiarise with machine learning libraries. It is through practice on easier datasets that you can develop good habits such as cross-validating to prevent over fitting and to split data sets into different testing and training sets. Any programming language you choose will have training dataset that helps you get a feel of the real project
Step 4: Learning Competitions
Start with the “getting started” category of the competitions. Kaggle competitions fall into many categories: Featured competitions are posted by the governments, organizations, and companies and offer large monetary prizes. Research competitions usually offer a small or no price but are valuable for your resume and career progress. It also offers non-traditional submission procedures. Recruitment competitions are hosted by companies that are looking to hire brilliant data scientists like you. And the getting started competitions for beginners to provide numerous guiding tutorials and simpler datasets. This category of projects provides a pressure-free and low-stake environment to practice for beginners. There are numerous community-curated tutorials available for this category as well.
Step 5: Focus on Learning
Now you have a good foundation to build from, so you have the confidence to start working on the featured competitions. The key to success, if you are beginner, is patience and learning from your mistakes. It will take a lot of effort and time to get a good ranking. To avoid getting frustrated and discourage choose your battles wisely. While prize money is great but it is not the main focus, the most valuable benefit is learning skills that prepare you for the real-world. A research project is a great choice for a long-term project where you can exercise your data science skills and stimulate your creativity.
Tips to Have Fun
With the steps above there is no doubt you will have an amazing time at Kaggle, here are few tips to make it more concrete, fund and task-oriented:
- Incremental Goals will help you progress faster and learn more.
Video game addicts understand incremental goals all too well because this is how they get hooked in the first place. Incremental goals are effective because they give you sense of accomplishment and this fuels your motivation to keep tackling more projects gradually. The sad fact is that a large percentage of Kaggle contributors may never win any competition. It is therefore advisable to set achievable and realistic milestones that are within reach. You will learn and master the subject gradually, incremental goals will keep you grounded to access your progress and make it fun
- Review Kernels with the most votes
The kernel is a cool feature on this platform. It is essentially a short script which shares a solution, explores a concept and even showcases a great technique. You can get novel ideas by going through popular kennels
- Take part in the forums
Forums are a valuable place to ask as many questions as you like. Do not be afraid to inquire and learn as much as you can; you have nothing to lose and a lot to learn. You have the opportunity to learn from brilliant and more experienced data scientists from all over the world.
- Solo and team projects
Working alone on a project helps you learn more because you are forced to tackle every step of the journey alone right from exploratory analysis, model training and handling dirty data. As a beginner you should take a couple of competitions alone to get the basics right. Once you are proficient in what you do and understand the fundamental data science project life cycle you can participate in teams to benefit from diversity. You will have a great learning working with diverse set of people with unique and competing skills, education levels, professional experience and industry backgrounds.
How to make Kaggle Profile
Here is a step by step guide on how to register an account and create a profile on Kaggle. Please remember that Kaggle only allows one profile per user.
- Go to the Kaggle website and click on sign up section
- Setup your Kaggle profile by providing a brief bio, picture, location, title and current workplace. You can also add your LinkedIn, Github and Facebook accounts so people can contact you when needed
Joining Kaggle is a straightforward process that requires very little time and effort. As a Kaggler, you have access to unlimited data sets, numerous Kaggle competitions, and the Kaggle forum. You can also follow strong Kaggle users to learn from.
Why wait, welcome to the world of Kaggle!
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