Kaggle is a website where people who are interested in data science and machine learning can compete with each other, learn, and share their work. It’s kind of like a big playground for data nerds! Here are some of the main things you can do on Kaggle:
Join competitions: Companies and organizations post challenges on Kaggle, and you can use your data skills to try to solve them. The winners often get prizes or recognition, so it’s a great way to test your skills and see how you stack up against other data scientists.
Learn new skills: Kaggle has a lot of free courses and tutorials that can teach you about data science, machine learning, and other related topics. It’s a great way to learn new things and stay up-to-date on the latest trends.
Find and use datasets: Kaggle has a huge collection of public datasets that you can use for your own projects. This is a great way to get your hands on real-world data and practice your data analysis skills.
Connect with other data scientists: Kaggle has a large community of data scientists from all over the world. You can connect with other members, ask questions, and share your work. This is a great way to learn from others and build your network.
Growing community of Kaggle
Kaggle is a platform for data scientists to share their work, compete in challenges, and learn from each other. In recent years, there has been a growing trend of data scientists joining Kaggle. This is due to a number of factors, including the following:
The increasing availability of data
The amount of data available to businesses and individuals is growing exponentially. This data can be used to improve decision-making, develop new products and services, and gain a competitive advantage. Data scientists are needed to help businesses make sense of this data and use it to their advantage.
Businesses are increasingly looking for data-driven solutions to their problems. This is because data can provide insights that would otherwise be unavailable. Data scientists are needed to help businesses develop and implement data-driven solutions.
The growing popularity of Kaggle. Kaggle has become a popular platform for data scientists to share their work, compete in challenges, and learn from each other. This has made Kaggle a valuable resource for data scientists and has helped to attract more data scientists to the platform.
Benefits of using Kaggle for data scientists
There are a number of benefits to data scientists joining Kaggle. These benefits include the following:
1. Opportunity to share their work
Kaggle provides a platform for data scientists to share their work with other data scientists and with the wider community. This can help data scientists get feedback on their work, build a reputation, and find new opportunities.
2. Opportunity to compete in challenges
Kaggle hosts a number of challenges that data scientists can participate in. These challenges can help data scientists improve their skills, learn new techniques, and win prizes.
3. Opportunity to learn from others
Kaggle is a great place to learn from other data scientists. There are a number of resources available on Kaggle, such as forums, discussions, and blogs. These resources can help data scientists learn new techniques, stay up-to-date on the latest trends, and network with other data scientists.
If you are a data scientist, I encourage you to join Kaggle. Kaggle is a valuable resource for data scientists, and it can help you improve your skills, to learn new techniques, and build your career.
Why data scientists must use Kaggle
In addition to the benefits listed above, there are a few other reasons why data scientists might join Kaggle. These reasons include:
1. To gain exposure to new data sets
Kaggle hosts a wide variety of data sets, many of which are not available elsewhere. This can be a great way for data scientists to gain exposure to new data sets and learn new ways of working with data.
2. To collaborate with other data scientists
Kaggle is a great place to collaborate with other data scientists. This can be a great way to learn from others, to share ideas, and to work on challenging problems.
3. To stay up-to-date on the latest trends
Kaggle is a great place to stay up-to-date on the latest trends in data science. This can be helpful for data scientists who want to stay ahead of the curve and who want to be able to offer their clients the latest and greatest services.
If you are a data scientist, I encourage you to consider joining Kaggle. Kaggle is a great place to learn, to collaborate, and to grow your career.
For a 21st-century professional, having proven analytical skills is increasingly important. Companies all over the world have started to push data scientists to participate in leading data science competitions. Businesses now emphasize that all their employees gain analytical skillsets, regardless of their department.
One of the best ways to prove that you have a strong grip on analytics and data science skills is to take part in reputable competitions that test these to show your employer that you have the required skill set.
There are many events these days for data science professionals, so it can get overwhelming trying to figure out which ones are worth your time. If you are not sure where to begin, or which ones to take part in, here are a few notable ones to help you get started.
Participating in data science competitions – Data Science Dojo
1. Kaggle
Kaggle is the most popular platform for practicing data science skills. It hosts multiple popular datasets, and regularly has competitions where anyone can participate to build the best machine learning models with data set and compete against others working on the same dataset.
The IBM call for code competition asks for contributions across several different areas in order to solve real world challenges. There are currently 4 areas in 2022 where you can get involved and build solutions:
Machine hack is a community that hosts competitions or hackathons for data science and AI enthusiasts. There are a wide variety of challenges available from the data science pipeline, from machine learning to data visualization. You can also win cash prizes for some of the challenges.
4. DataCamp:
DataCamp has weekly competitions on their website. Each event has a cash prize associated with it as well. You can submit your solutions, and vote on the best solutions from other participants as well
5. DrivenData:
DrivenData provides a platform for data scientists who want to make a social impact with their work. The challenges on the platform focus on solving social issues through data science.
These challenges include things like predicting public health risks at restaurants, identifying endangered species in images, and matching students to schools where they are likely to succeed. The winning code gets a prize, and gets published under an open-source license for others to benefit as well
Are you excited to participate in data science competitions?
All of the above-mentioned data science events allow you to gain hands-on learning of data science skills. It offers a platform for the learner to improve problem-solving skills and prove their abilities in a competitive market.
Not only does participating in these competitions help you stand out, but they also let you brainstorm innovative ideas for the future.
Until a few months ago I didn’t know the answer to that question. If you don’t either that’s okay, we’re going to answer it together. But first, you need to know a little background information about this data science network.
Kaggle was founded in 2010 with the idea that data scientists need a place to come together and collaborate on projects. This has transformed into a network with more than 1,000,000 registered users, and has created a safe place for data science learning, sharing, and competition.
Using the human competitive spirit, Kaggle created a platform for organizations to host competitions that have fueled new methodology and techniques in data science, and given organizations new insights from the data they provided.
Being the competitive person I am, the competition aspect is what originally caught my eye, and gave me the desire to learn about the intricacies of a Kaggle Competition.
How Kaggle competition works
While combing through the Kaggle website and other informative articles, I found there are three basic steps in Kaggle Competitions.
Preparation: Each Kaggle competition has a host, and each host has to prepare and provide data. When providing data, the host has the opportunity to give additional information such as a description, evaluation method, timeline, and prize for winning.
2. Experimentation: At this time, you’ve had your morning coffee, you’ve read all the information in the overview 500 times, and you’re ready to win 1st place. Now is the time to experiment, submit, and learn. There are three ways to upload your work:
Kaggle Kernels
Manual Uploads
Kaggle API
If you don’t want anyone to really know what you’re doing, you should upload your experiments manually or by using the Kaggle API. Kaggle Kernels are a way for competitors to share what they’ve accomplished and get feedback from their peers. Kernels will give you ideas as to how to conquer the data, and I suggest you go through some of the popular ones.
Results: In every Kaggle competition, there are public and private leaderboards. Be warned, the leaderboards are VERY different. The public leaderboard is based on a small percentage of the test data decided by the host. Although it gives you a good idea, it does not always reflect who will win and lose. The private leaderboard is what really matters. Not calculated until the end of the competition, this leaderboard is based on a larger proportion of data and, ultimately, decides the winners and losers.
If you would like to dive deep into the different types or formats and datasets offered by Kaggle, take a look at Kaggle’s Help and Documentation.
Active Kaggle competitions
[Updated May 6, 2019]
Kaggle competitions have a limited amount of time you can enter your experiments. This list does not represent the amount of time left to enter or the level of difficulty associated with posted datasets. One way to determine the level of difficulty is to look at the prize. Typically, the larger the prize, the more difficult/advanced the problem is. You can also look at the type of competition. You can find the four categories and Kaggle’s description of them below.
Featured: “These are full-scale machine learning challenges which pose difficult, generally commercially-purposed prediction problems.”
Research: “Research competitions feature problems which are more experimental than featured competition problems.”
Getting Started: “These are semi-permanent competitions that are meant to be used by new users just getting their foot in the door
in the field of machine learning.”
Playground: “These are competitions which often provide relatively simple machine learning tasks, and are similarly targeted at newcomers or Kagglers interested in practicing
a new type of problem in a lower-stakes setting.”
I will try my best to keep this list as up-to-date as possible. Unfortunately, I’m not spending all my time on Kaggle’s website. So if you see something has ended, or a new competition has been added, please leave a comment below. Thanks and have fun!
Kaggle Days Dubai is a data science competition to improve your data science skillset. Here’s what you can expect to learn from the grandmasters.
Anyone interested in analytics or machine learning would certainly be aware of Kaggle. Kaggle is the world’s largest community of data scientists and offers companies to host prize money competitions for data scientists around the world to compete in. This has made it the largest online competition platform too. However, Kaggle has started to evolve itself to organize offline meetups globally.
One such initiative is the organization of Kaggle Days. Up till now, four Kaggle Days events have been organized in various cities around the world, the recent one being in Dubai. The format of Kaggle Days involves a 2-day session consisting of presentations, practical workshops, and brainstorming sessions during the first day followed by an offline data science competition the next day.
For a machine learning enthusiast with intermediate experience in this field, participating in a Kaggle-hosted competition and teaming up with a Kaggle Grandmaster to compete against other grandmasters was an enjoyable experience on its own for me. I couldn’t reach the top ranks in the data science competition, but competing with and networking with the dozens of grandmasters and other enthusiasts present during the 2-day event boosted my learning and abilities.
I desired to make the best use of this opportunity, learn to the utmost extent I could, and ask the right questions from the grandmasters present at the event to get the best out of their wisdom and learn the optimal ways to approach any data science problem. It was heart-whelming to discover how supportive they were as they shared tricks and advice to get to the top position in data science competitions and improve the performance of any machine learning project. In this blog, I’d like to share the insights that I gathered during my conversations and the noteworthy points I recorded during their presentations.
Strengthen your basic knowledge of Kaggle
My primary mentor during the offline competition was Yauhen Babakhin. Yauhen is a data scientist at H2O.ai and has worked on a range of domains including e-commerce, gaming, and banking, specializing in NLP-related problems.
He has an inspiring personality and is one of the youngest Kaggle Grandmasters. Fortunately, I got the opportunity to network with him the most. His profile defied my misconception that only someone with a doctoral degree can achieve the prestige of being a grandmaster.
During our conversations, the most significant advice that came from Yauhen was to strengthen our basic knowledge and have an intuition about various machine learning concepts and algorithms. One does not need to go extensively deep into these concepts or be extra knowledgeable to begin with. As he said, “Start learning a few important learning models, but get to know how they work!”
It will be ideal to start with the basics and extend your knowledge along the way by building experience through competitions, especially the ones hosted on Kaggle. For most of the queries, Yauhen suggests, one must know what to search on Google. This alone will prove to be an extremely handy tool on its own to get us through most of the problems despite having limited experience relative to our competitors.
Kaggle competition day 2
Furthermore, Yauhen emphasized how Kaggle single-handedly played a leading role in heightening his skills. Throughout this period, he stressed on how challenges triggered him to perform better and learn more.
It was such challenges that provoked him to learn beyond his current knowledge and explore areas beyond his specialization, such as computer vision, said the winner of the $100,000 TGS Salt Identification Challenge. It was these challenges that prompted him to dive into various areas of machine learning, and it was this trick that he suggested we use to accelerate career growth.
Through this conversation, I was able to learn the importance of going broad. Though Yauhen insisted on selecting problems that target a broad range of problems and cover various aspects of data science, he also suggested limiting it to the extent that it should align with our career pursuits and make us realize if we even need to target something beyond what we are ever going to use.
Lastly, the Grandmaster in his late 20’s also wanted us to practice with deep learning models as it’ll allow us to target a broad set of problems to discover the best approaches used by previous winners and to combine them in our projects or competition submissions. These approaches could be found in blogs, kernels, and forum discussions.
Remain persistent
My next detailed interaction was with Abhishek Thakur. The conversation provoked me to ask as many questions as I could, as every suggestion given by Abhishek seemed wise and encouraging. One of the rare examples of someone crowned with 2 Kaggle Grandmaster titles, competitions, and discussion grandmasters, Abhishek is the chief data scientist at boost.ai, having once attained the 3rd rank in global competitions at Kaggle.
What made his profile more convincing was Abhishek’s accelerated growth from a novice to a grandmaster within a year and a half. He started his career in machine learning from scratch and took this initiative from Kaggle itself. Initially starting with the lowest rank in competitions, Abhishek was adamant that Kaggle could be the only platform one could totally rely on to catapult his growth within such a short time.
Abhishek speaking at Kaggle
However, as Abhishek repeatedly said, it all required continuous persistence. From the beginning until now, even after being placed in the bottom ranks initially, Abhishek carried on and demonstrated how persistence was the key to his success. Upon inquiring about the significant tools that led him to get gold in his recent participation, Thakur emphasized immensely on feature engineering.
He insisted that this step was the most important of all in distinguishing the winner. Similarly, he suggested that a thorough exploratory data analysis can assist one in finding those magical features that can enable one to get the winning results.
Like other Grandmasters who have attained massive success in this domain, Abhishek also emphasized improving one’s personal profile through Kaggle. Not only does it offer you a distinct and fast-paced learning experience, as it did for all the grandmasters at the event, but it’s also recognized across various industries and major employees who value these rankings. Abhishek told how it enabled him to get numerous lucrative job offers over time.
Start instantly with data science competitions
On the first day, I was able to attend Pavel Pleskov’s workshop on ‘Building The Ultimate Binary Classification Pipeline’. Based in Russia, Pavel currently works for an NLP startup, PointAPI, and was once ranked number 2 among Kagglers globally. The workshop was fantastic, but the conversations during and after the workshop intrigued me the most as they mostly comprised tips for beginners.
Pavel, who quit his profitable business to compete on Kaggle, Pavel insisted on the ‘do what you love’ strategy as it leads to more life satisfaction and profit. Pavel told us how he started with some of the most popular online courses on machine learning but found them lacking practical skills and homework, which he covered using Kaggle.
For beginners, he strongly recommended not to put off Kaggle contests or wait until the completion of courses, but to start instantly. According to him, practical experience on Kaggle is more important than any other course assignment.
Some other noteworthy and touching tips from Pavel were that to win such competitions, unlike many students who approach Kaggle as an academic problem and start creating fancy architectures and ultimately do not score well, Pavel approaches a problem with a business mindset. He increased the probability of success by leveraging resources, such as including people in his team who had resources, like a GPU, or merging his team with another to improve the overall score.
Kaggle – data science competition day 2
Upon an inquiry related to keeping the right balance between taking time to build theoretical knowledge and using that time to generate new ideas, Pavel advised looking at forum threads on Kaggle. They can help you know how much theoretical knowledge you are missing while competing with others.
Pavel is an avid user of LightGBM and CatBoost models, which he claims have given him superior rankings during the competitions. One of his suggestions is to use the fast.ai library, which, despite receiving many critical reviews, has been a flexible and useful library that he mostly keeps in consideration.
Hunt for ideas and rework them
Due to the limitation of time during the 2-day event, I was able to hear less from another young grandmaster from Russia, coincidentally sharing the same first name with his fellow Russian grandmaster, Pavel Ostyakov. Remarkably, Pavel was still an undergrad student then and has been working for Yandex and Samsung AI for the past couple of years.
He brought a distinct set of advice that can prove to be extremely resourceful when one is targeting gold in data science competitions. He emphasized writing clean code that could be used in the future and allows easy collaboration with other teammates, a practice usually overlooked which later becomes troubling for participants. He also insisted on trying to read as many forums on Kaggle as one could.
Not just ones related to the same competition but those belonging to other data science competitions as well since most of them are similar. Apart from searching for workable solutions, Pavel suggested also looking for ideas that failed. As he recommended, one must try using (and reworking) those failed ideas as there are chances they may work.
Pavel also brought up the point that to surpass other competitors, reading research papers and implementing their solutions could increase your chances of success. However, during all this time he stressed a lot on to have a mindset that anyone can achieve gold in a competition, even if he/she possesses limited experience relative to others.
Experiment with diverse strategies
Other noteworthy tips and ideas that I collected while mingling with grandmasters and attending their presentations included those from Gilberto Titericz (Giba), the grandmaster from Brazil with 45 Gold medals! While personally inquiring about Giba, he repeatedly used the keyword ‘experiment’ and insisted that it is always important to experiment with new strategies, methods, and parameters. This is one simple, although tedious, way to learn quickly and get great results.
Training session of Kaggle
Giba also proposed, that to attain top performance, one must build models using different viewpoints of the data. This diversity can come from feature engineering, using varying training algorithms, or using different transformations. Therefore, one must explore all possibilities.
Furthermore, Giba suggested that fitting a model using default hyperparameters is good enough to start a data science competition and build a benchmark score to improve further. Regarding teaming up, he repeated that diversity is the key here as well, and choosing someone who thinks similar to you is not a good move.
A great piece of advice that came from Giba was to blend models. Combining models can help improve the performance of the final solution, especially if each model’s prediction has a low correlation. A blend can be something as simple as a weighted average. For instance, non-linear models like Gradient Boosting Machines blend very well with neural network-based models.
Blending models suggested by Giba
Conclusion
Considering the key takeaways from the suggestions given by these grandmasters and observing the way they competed during the offline data science competition, I noted that beginners in data science must use their efforts to try varying methodologies as much as they can. Moreover, a summary of the recommendations given above stresses the significance of taking part in online data science competitions no matter how much knowledge or experience one possesses.
I also noted that most of the experienced data scientists were fond of using ensemble techniques and one of the most prominent methods used by them was the creation of new features out of the existing ones. This is what was cited by the winners of the offline data science competition as their strategy for success. Conclusively, these sorts of meetups could enable one to interact with the top minds in the field and gain the maximum within a short time as I fortunately did.
Kaggle Days is a conference created by Kaggle and LogicAI for Kagglers to meet offline. It’s the “first global series of offline events for seasoned data scientists and Kagglers” as written on the Kaggle Days website.
These days take place all over the world, including current and past events in China, Dubai, San Francisco, Paris, Tokyo, and Warsaw. Attendees meet Grandmasters, win prizes, and compete in offline events.
They also have the opportunity to learn from seasoned professionals who are there to help grow the community.
Raja Iqbal, Chief Data Scientist and CEO at Data Science Dojo, is one of the seasoned professionals looking to help the community grow.
“We have 10 Meetup groups spread across the globe, but we’ve never been as far east as Tokyo. The closest we get is Singapore.” Raja said while counting on his fingers. “I just can’t wait to meet more people from a different part of the world who are excited to learn data science.”
What to expect at Kaggle days Tokyo
In Tokyo, attendees can expect to network, learn, compete, and earn prizes, like in many of the conferences. Kaggle Grandmasters, Masters, and data science experts will be in attendance to give presentations, talk shop, and network with everyone in attendance.
Data Science Dojo will be there to give a 90-minute workshop as well as network, hire, and learn from top Kagglers. The topic of DSD’s workshop has been narrowed down to two possibilities:
The Art of Building Machine Learning Models for Large Scale Machine Learning
Feature Engineering for Real-World Machine Learning Problems
Kaggle CTO, Ben Hamner, is the Keynote Speaker giving a talk titled Leveling-up Kaggle Competitions. Other talks from presenters include:
Computer Vision with Keras – Dimitris Katsios, ML Engineer at LPIXEL
Joining NN Competitions (for beginners) – Tomohiro Takesako, Competitions Master
My Journey to Grandmaster – Jin Zhan, Competitions Grandmaster
Intro to BigQuery ML for Kagglers – Polong Lin, Developer Advocate at Google
Two Kaggle Competitions team members will also be giving talks. Julia Elliott (Competitions Team Lead) and Walter Reade (Data Scientist).
Presentations are tentative and subject to change. This will be updated when the full agenda has been announced.
About Data Science Dojo
Data Science Dojo offers a 5 day, in-person, and top-ratedData Science Bootcamp around the world. During the course, students learn everything from predictive analytics and ensemble methods to recommender systems and the fundamentals of big data engineering.
Raja and his team of instructors have trained more than 4,000 individuals from nearly 1,000 different companies. Attendees come from diverse backgrounds, including software development, management consulting, medicine, education, project management, target=”_blank” public service, finance, not-for-profit, mining, oil and gas, and more.
Data Science Dojo sponsored Kaggle Days Tokyo. Here’s an overview of what Kaggle Days are and what to expect.
Overview
Kaggle is an online learning platform for data science and machine learning. The educator uses competitions to help its users (called Kaggelers) practice and grow their data science skillset with publicly available datasets.
Kaggle Days are events that take place around the world. They started as a partnership between LogicAI and Kaggle as a way to bring Kaggelers together for an offline event. Competitions, seminars, workshops, and networking opportunities are available for Kaggelers to participate in. These events take place as one-off local events (Meetups) as well as multiday global events (conferences).
Kaggle days Tokyo – Agenda
The global event in Tokyo is taking place this December 11-12. Registration closed within a matter of days of opening, which shows the amount of popularity these events have among their participants. The agenda is jam-packed with exciting talks and tutorials from Kaggle Grandmasters and data science professionals, and I’d like to highlight a few.
Kaggle Days Tokyo – Schedule
Raja Iqbal – Tutorial on model validation and parameter tuning
Raja Iqbal is the CEO, Chief Data Scientist, and Lead Instructor at Data Science Dojo. He has an MS from Stanford and a Ph.D. from Tulane University. He spent more than 6 years at Microsoft Bing and Bing Ads working on various data science and machine learning research projects. Below is a description, given by Raja, of his workshop:
“Cross-validation is a popular technique for model validation and parameter tuning. In this tutorial, we will discuss other model validation and parameter techniques in scenarios where k-fold cross-validation may not be the best choice. We will also discuss some parametric and non-parametric statistical tests for comparing models.”
Why should you attend?
Modern machine learning is about gathering the right data, feature engineering, validation, and parameter tuning. Not understanding the concepts or using the techniques correctly renders machine learning useless.
Date: 12/11/19
Time: 10:15 am – 11:45 am
Location: 27F Hanabi Room
Jin Zhan – My journey to grandmaster: Success and failure
Becoming a Kaggle Grandmaster (GM) is no small accomplishment. It takes years of practice to obtain this impressive title. Jin Zhan has multiple years of experience in data science and machine learning, as well as Hadoop. Currently, Zhan is a Data Scientist at Fast Retailing, where he focuses on demand forecasting, recommender systems, and customer comment analysis.
Why should you attend?
The original reason I chose this out of the bunch was that Jin is going to talk about his failures before becoming a grandmaster. Talking about our failures is often difficult, but we can learn more from them than our successes.
After (admittedly) combing through his LinkedIn profile, I found Zhan to be the perfect picture of success on Kaggle. His experience doesn’t come from one place and his education comes from multiple sources. Besides, Zhan’s a Grandmaster. What other reason to attend do you need?
Date: 12/11/19
Time: 4:35 pm – 4:45 pm
Location: 27F Matsuri Room
Kaggle competition
During a Kaggle competition, typically the only help or mentoring you receive is from your teammates or through Kaggle Kernels. At the competition in Tokyo (as well as the other global events) mentors will be available to help you along the way.
The mentors for the competition in Tokyo include:
Ryuji Sakata – Kaggle Grandmaster and Data Scientist/Researcher at Panasonic Corporation
Walter Reade – Data Scientist on Kaggle Competitions Team
Dimitry Gordeev – Kaggle Grandmaster and Data Scientist at UNIQA
Pawel Jankiewicz – Kaggle Grandmaster and Owner/Founder at LogicAI
Jin Zhan – Kaggle Grandmaster and Data Scientist at Fast Retailing
You should feel compelled to pick their brains as much as you can. All of these people are successful and established data scientists with extensive knowledge of Kaggle competitions. Get as much out of them as you can.
Date: 12/12/19
Time: 10:30 am – 6:30 pm
If you’re a Kaggeler who missed out on joining a global Kaggle Days event, keep an eye on their schedule. You can also join a local event and get to know your local Kaggelers!
