Kaggle is the largest platform for data scientists and machine learning experts, offering aspirants with the most hands-on experience in the complicated field of data science. Due to this, experts have high praise for the Kaggle community for its role in data scientist upskilling. The study of any subject requires classification. If you want to crack open all the data types for the juicy information inside, check out the different types of big data articles here.
The winners of each competition post what they did throughout the competition, and they share the code they wrote too. If a data scientist or machine learning expert has some real skills and a competitive edge, Kaggle can help them dig deep into post-competition writeups.
What is Kaggle?
Kaggle is a platform where data scientists spend their nights and weekends. It is a crowd-sourced platform for attracting, nurturing, training, and challenging data scientists from all over the world to solve data science, machine learning, and predictive analytics problems.
Do you know that almost all data scientists are only theorists who are rarely given the opportunity to practise before even being hired in the real world? Kaggle solves this issue by providing a platform for data science enthusiasts to engage and start competing in solving real-world problems. The expertise you gain on Kaggle will be immensely valuable in order to prepare you to know what goes into identify new big data solutions.
In addition, you can read more about measures of dispersion here.
Skills Required by a Data Scientist to Master Kaggle
Critical Thinking & Business Acumen:
You can get to the top if you know how to look at a business problem from all angles before formulating a hypothesis. One of the most important skills for you to have is critical thinking. Take a step back and assess the situation from the standpoints of business, technology, operations, and customers.
Storytelling & Communication:
Given that most organisations today make data-driven decisions, communication and the ability to weave a story using visualisation tools are critical. As a result, the ability to translate your data insights into business-friendly language becomes a must-have.
Passion for statistics and mathematics:
As a Data Scientist, you'll need to know basic statistical terms like distribution and hypothesis testing. During the exploratory data analysis and data preparation phase, having a good understanding of key statistical procedures will be beneficial.
Because there has been more data created in the last few years than at any other time in human history, you may find yourself using one or more machine learning algorithms, regardless of the size of your company. This essentially means that you should have access to algorithms like regression, random forest, k-neighbor, SVM, gradient boosting, and so on.
Data Architecture Knowledge & Programming Skills:
The standard job responsibilities of a typical Data Scientist have been compartmentalised in many organisations (Data Analyst, Data Engineer). This may work within the organisation for labour division, but a Data Scientist must have data extraction and wrangling skills. Data analysis and preparation account for more than 80% of the work in a typical data science project.
How to Use Kaggle for Data Science
Kaggle is the world's largest online community of data scientists and machine learning experts. This platform has over 1 million registered users, thousands of public datasets, and code snippets (also known as notebooks), and, most importantly, it is effectively utilized by many of the world's best data scientists. I'm not sure now how many fields provide something comparable. It offers aspiring data scientists a once-in-a-lifetime chance to learn from the world's finest for free.
I'm going to show you how to get started with Kaggle and how to use it to improve your data science skills. Although the method described in this article is not the only way to get started with Kaggle, you can check these data science course online.
Equip Yourself with the Basic Skills
Kaggle is indeed a fantastic resource to learn and master essential data science skills, but it can quickly become daunting if users don't know the fundamentals. So, first, conduct a gap assessment on your skill set, understand the current level of skill, and determine how much it would take so that you can reach a competency where you are comfortable with the following:
Fundamental programming in any programming language. Python and R are the most popular data science programming languages. Many of the notebooks available on Kaggle will also be in Python or R. Basic programming knowledge would be extremely beneficial in reviewing and comprehending the available notebooks.
You should be familiar with the libraries and packages provided by the programming language users decide to work on data analysis, numerical operations, statistics operations, and data visualisations. The data analysis notebook will make extensive use of libraries, so having a solid foundation is essential.
Have one basic understanding of the various types of algorithms and the various use-cases which can be fixed using them.
Once you have these fundamental skills, it will be easier for you to learn more advanced topics and enjoy some of the methodologies used mostly by expert data scientists.
Check out KnowledgeHut Data Science with Python course, this course comes with no learning prerequisites and helps you get hands-on learning data science with python skills.
Explore the Datasets
Start with dataset explorations if you're new to data science. Begin with simple datasets so that importing, analysing, and visualising the data takes less time. Also, pick datasets from a field that interests you seeing as having a liking for or greater sense of the dataset's domain aids in further data analysis.
Verify the dataset descriptions for details on how the data were collected, the time period to which the data belong, and other information that will assist you in framing your questions for exploratory data analysis.
Begin by exploring the dataset and tracking your findings. Check out the "Tasks" tab for more analysis ideas; this is a major update in which people can add fascinating things that could be done with the information and others can submit their solutions to it.
Experiment with different types of data, gradually moving out of your safety zone and becoming familiar with sets of data from areas you haven't worked with before. You also can submit your research and see how it is received by the community.
Learn From the EDA Code Snippets
It's necessary to study data exploration from the most knowledgeable individuals. Go to the Notebooks tab for the datasets you've been operating on and look for analytical code snippets with a lot of number of likes as well as those who come from highly qualified users. Investigate the current analysis and make a comparison from what you've done. Recognise the gap in the knowledge or the analyses that you have overlooked; this systemic review will make sure that your learning has progressed significantly.
Test out the other datasets and notebooks with the analysis scripts to see what sorts of analyses some of the more experienced data scientists have done.
Since you've learned from many of the experts, it's time to start putting what you've learned and apply. Choose a new dataset and begin analysing the data; I am confident that your evaluation will become much better now and try to incorporate other standards such as script documentation and formatting to ensure that they are easily readable.
It's critical to invest some time in these steps because the quality of your data analysis will have a direct impact on the model/solution you're creating, so make sure you take the time to explore and learn from the experts on data analysis.
Explore and Re-Execute the Data Science Notebooks
Now that you've crafted your data analysis skills, it's time to switch your attention to developing predictive models and other data science solutions. Examine the notebooks that address use-cases and try to decipher the logic line by line by re-running them. Try to explore a variety of solutions, such as notebooks on building regression and classification predictive models, as well as notebooks on building solutions such as a recommender system. Going through a variety of these solutions and understanding them will be very beneficial.
Next, concentrate on competitions. Start with a knowledge competition; it will assist you in better understanding the methodology used to solve competition problems, and these knowledge competitions will expose you to feature engineering and model building.
Some knowledge competitions to start with are listed below:
- Petals to the Metal - Flower Classification on
- Natural Language Processing with Disaster Tweets
The first is good for learning about classification algorithms, while the second is good for getting started with NLP.
After you've mastered the knowledge competition, move on to the closed competition and try your hand at attempting to solve them to see where you stand in terms of ranking and precision. In so many cases, the winning solution is shared with the participants via the discussion forum. Try to understand them and see if there are any lessons you can take away that you can apply to other competitions.
Pointers to Get Started with Kaggle
Thousands of datasets are available on Kaggle, and it's easy to get stuck in the details and options available. The examples below can be used to help you get started with Kaggle.
The housing price dataset is a good starting point because it is a dataset that we can all relate to, making it simple to analyse and learn. Here's a link to the Kaggle housing dataset.
House Prices - Advanced Regression Techniques
The above housing dataset may be used to know how to develop a regression algorithm that forecasts home prices. The notebooks in this dataset will contain a variety of algorithms available to building algorithms, which can be explored and tested to better understand how to build a predictive model.
After learning about a dataset that is appropriate for a regression problem, the next step is to learn about a classification problem, and a few good Kaggle datasets that can be used for this are listed below:
Credit Card Fraud Detection
Heart Failure Prediction
We can easily relate to both the credit card fraud and the heart failure datasets. The above credit card fraud dataset is reinvented data, so the details are encrypted into numerical columns. This may not be intuitive at first, but once you get used to the datasets, you can start exploring the credit card fraud dataset.
Try to comprehend the dataset first, then use the available exploratory data analysis notebooks to better understand the data. Finally, try to learn about the model-building part; there should be at least a few notebooks with model deployments using a variety of algorithms.
After covering the supervised learning regression and classification problem, the next section will look at a dataset related to an unsupervised learning problem. The Groceries dataset is a good example that is also easy to understand. Market Basket Analysis and recommendation algorithms can both benefit from this dataset.
Be Part of Kaggle Competitions and Follow the Discussions
Now that you're ready to participate in a live competition, pick something that inspires you because these competitions are like marathon running those last weeks and require constant effort and hard work to stay on top of the leaderboard, and picking something you enjoy will help you stay motivated.
Don't try to enter too many competitions at the same time. If you have time, limit yourself to only one or a few. However, doing a lot of things at once will not benefit you.
Always keep an eye on the discussion forums while participating in the competition because data issues and other issues faced by fellow competitors will be explored here, and solutions will be discussed and shared. As a result, it's critical to stay in touch with the discussion groups.
Benefits of Using Kaggle
- There are a lot of people with similar interests, so you might be able to find a good teammate for your next competition.
- There is usually a monetary prize attached to these competitions, and there are also recruitment competitions where you could potentially find your next employer.
- They also have a job portal, making it simple to apply for jobs.
- Kaggle offers a variety of courses that are generally short and useful for brushing up on your skills and knowledge.
- Because Kaggle is well-known in the data science community, your accomplishments here will be well-received and recognised in the industry.
Tips for Kaggle Data Science
Finally, we'll go over seven recommendations for getting the most out of your Kaggle experience.
1. Set incremental goals:
You've probably experienced the power of incremental goals if you've ever played an addictive video game. That you'll get connected on good games. Each goal is ambitious enough to provide a sense of accomplishment while remaining accurate enough to be achievable.
It's perfectly fine for the majority of Kaggle participants to never win a single competition. If you make that your first goal, you might get demotivated and lose interest after a few attempts.
2. Review most voted kernels:
Participants can submit "kernels," which are short scripts that explore a concept, demonstrate a technique, or even share a solution, to Kaggle.
Reviewing popular kernels can help you come up with new ideas when you're starting a competition or when you've reached a stalemate.
3. Ask questions on the forums:
Feel no guilt for asking "stupid" questions.
What's the worst that could happen, after all? Maybe you'll be ignored... and that'll be the end of it. From the other hand, you stand to benefit greatly from the advice and guidance of more experienced data scientists.
4. Work Solo to develop skills:
Working alone is recommended in the beginning. This will push you to work through each step of the applied machine learning process, which include exploratory analysis, data cleaning, feature engineering, and model training.
You might miss out on opportunities to develop those cornerstone skills if you start teaming up too soon.
5. Join forces to test your limits.
As a result, collaborating in future competitions can be a great way to push your limits and learn from others. Many of the previous winners were made up of individuals who banded together to pool their knowledge.
Furthermore, once you've mastered the technical skills of machine learning, you'll be able to collaborate with others who may have more domain knowledge than you, broadening your horizons even further.
6. Keep in mind that Kaggle can be used as a stepping stone.
Remember, you're not committing to being a Kaggler for the long haul. It's not a big deal if you discover you don't like the format.
Many individuals use Kaggle as a crucial step before embarking on their own projects or pursuing a career as a full-time data scientist.
Another reason to concentrate on learning as much as possible. In the long run, it's better to focus on competitions that will provide you with relevant experience rather than chasing after the largest prize pools.
7. Don't be concerned about your low ranking.
Some beginners never begin because they are concerned about their profile showing low ranks. Competition anxiety is, of course, a real thing that isn't unique to Kaggle.
Low rankings, on the other hand, aren't a big deal. Because they were all beginners once, no one else will judge you.
Even so, if you're still concerned about your profile's low rankings, you can create a separate practise account to learn the ropes. When you're ready, you can start building your trophy case with your "main account."
Getting the Work Experience while Learning
Although Kaggle is a perfect platform for generating buzz to the best performing models and techniques like cross-validation as well as other packages that can be used to enhance the performance of the model. In fact, the modelling phase accounts for only 10–20 percent of a data science project, and there is a huge amount of hard work that goes into establishing the business challenges, acknowledging the data requirement, and recognising data sources, transform the data, and so on.