Kaggle

If you’re a data scientist or aspiring to become one, you’ve probably heard of Kaggle—the go-to platform for everything data science. But what makes it so special? Why do data scientists, from beginners to experts, flock to this platform?

Kaggle is more than just a website—it’s a thriving community of data enthusiasts where you can compete, collaborate, and learn from some of the best minds in the field. Whether you’re looking for real-world datasets, hands-on machine learning challenges, or a chance to showcase your skills, this platform has something for everyone.

In this blog, we’ll explore why this platform is best for data scientists—from its competitive environment to its endless learning opportunities. Ready to dive in? Let’s go!

 

What Makes Kaggle Unique?

Kaggle is a one-stop hub packed with resources, competitions, and a vibrant community. Here’s what sets it apart:

• Free access to datasets, tools, and community – It offers a massive collection of public datasets, pre-built machine learning notebooks, and a supportive community of data scientists, all available for free.
• Competitive yet collaborative environment – Its competitions push you to solve complex real-world problems, but the platform also encourages collaboration through code sharing, discussions, and public notebooks.
• Integration with cloud computing – With Kaggle Notebooks, free access to GPUs and TPUs, and seamless integration with cloud-based tools, you can train powerful models without expensive hardware.

This online data science community makes it easy to learn, experiment, and compete, all while connecting with top data science talent worldwide.

 

Also explore this: Insightful Kaggle Competitions

 

Benefits of Using Kaggle

Beyond its unique features, this collaborative AI platform provides countless opportunities for learning, growth, and career advancement in data science. Here’s how you can benefit from actively engaging with the platform:

1. Learning from the Community

Kaggle thrives on knowledge sharing. From expert-written notebooks to open-source solutions, you can learn directly from top-ranking data scientists. Discussions and code reviews help you grasp best practices and refine your own techniques.

2. Real-World Data Science Challenges

Many of its competitions are sponsored by companies looking for solutions to actual business problems. This means you’re not just working on toy datasets—you’re gaining practical experience with industry-relevant challenges.

3. Skill Development and Benchmarking

This data-driven community gives you hands-on exposure to machine learning, deep learning, and advanced techniques like feature engineering and model tuning. You can track your progress through rankings, medals, and leaderboards, helping you measure your skills against other data scientists.

4. Building a Strong Portfolio

Participating in competitions and publishing high-quality notebooks showcases your problem-solving skills. A well-documented Kaggle profile can act as an impressive portfolio when applying for jobs in data science.

 

A comprehensive guide on how to build a data science portfolio

 

5. Access to Diverse Datasets

Kaggle’s dataset repository covers domains like finance, healthcare, and natural language processing. Whether you’re experimenting with time series forecasting or training image classification models, you’ll find datasets to match your interests.

6. Networking and Career Growth

This platform connects you with data science professionals worldwide. Engaging in discussions, collaborating on projects, and ranking in competitions can open doors to job opportunities with top companies scouting for talent on the platform.

Whether you’re a beginner looking to learn or an experienced practitioner aiming to test and refine your skills, this platform provides the perfect playground for data science enthusiasts.

How to Get Started on Kaggle

Now that you understand why this is a valuable platform, it’s time to jump in. Whether you’re a beginner looking to learn or an experienced data scientist aiming to compete, it provides everything you need to start your journey. Here’s how you can make the most of it:

 

1. Create an Account and Explore Competitions

First, sign up on Kaggle.com and complete your profile. This helps you connect with the community and track your progress. Once you’re in, head over to the Competitions section. It hosts a variety of challenges, from beginner-friendly “Getting Started” competitions to high-stakes industry-sponsored contests. Even if you’re not ready to compete, analyzing past solutions will help you understand real-world machine learning workflows, feature engineering techniques, and evaluation metrics.

2. Get Comfortable with Notebooks and Kernels

Kaggle Notebooks (previously called Kernels) are cloud-based coding environments where you can write and execute Python and R scripts without needing to install anything on your computer. Browse through public notebooks to see how experienced Kagglers approach different problems—how they clean data, build models, and interpret results. Try running these notebooks yourself, modify the code, and experiment with different approaches to reinforce your learning.

 

You might also like: 6 data science projects that would boost your portfolio

 

3. Engage in Discussions and Learn from Top Kagglers

The Kaggle discussion forums are an excellent place to gain insights from top-ranked data scientists. Engage in discussions, ask questions, and follow high-performing Kagglers to stay updated on best practices, new techniques, and competition strategies. Many Kagglers share their thought processes, problem-solving approaches, and even detailed walkthroughs of their solutions. Learning from these discussions will help you avoid common pitfalls and improve your problem-solving skills.

By actively engaging with competitions, experimenting with notebooks, and participating in discussions, you’ll quickly gain the knowledge and confidence needed to excel in the Kaggle community.

Common Mistakes to Avoid on Kaggle

Kaggle is an incredible learning platform, but beginners often fall into common traps that slow their progress. Here are a few mistakes to watch out for:

1. Prioritizing Competition Scores Over Learning

It’s easy to get caught up in leaderboard rankings, but this site isn’t just about winning—it’s about improving your skills. Instead of solely optimizing for the best score, focus on understanding the data, experimenting with different models, and refining your approach. Even if you don’t rank highly, each competition is an opportunity to learn.

 

Another interesting read: Kaggle days Dubai

 

2. Ignoring Discussions and Community Contributions

Kaggle’s discussion forums and public notebooks are goldmines of knowledge. Many participants of it openly share their approaches, feature engineering techniques, and even full solution breakdowns. Failing to engage with the community means missing out on valuable insights that could help you grow as a data scientist. Read discussions, ask questions, and learn from those ahead of you.

3. Not Documenting and Explaining Your Work

A well-documented notebook doesn’t just help others—it reinforces your own learning. Instead of just writing code, take the time to explain your thought process, methodology, and results. This not only improves your understanding but also helps you build a strong portfolio to showcase to potential employers.

Avoiding these mistakes will make your experience on this platform far more rewarding, setting you up for long-term success in data science.

 

 

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. 

 

 

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.

You can learn more about Kaggle competitions on our blog here: Insightful Kaggle competitions and data science portfolios | Data Science Dojo 

 

 Read more about Kaggle Competitions in this blog by Nathan Piccini

 

2. IBM Call for Code 

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:

The Global Challenge, open source projects, racial justice, and deployments. You can find out more on the call for code page here: Call for Code | Tech for Good | IBM Developer  

 

3. Machine Hack: 

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 their 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.

 

Written by Arham Noman

What’s a Kaggle Competition? I didn’t know, so I looked it up. Get started by reading what I learned, and find an active list of competitions. 

First of all, what’s Kaggle?

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.

Read more:

Kick-off with Kaggle competitions to learn data science skills

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.

1. 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 1^st 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.

 

3. 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.

1. Featured: “These are full-scale machine learning challenges which pose difficult, generally commercially-purposed prediction problems.”
2. Research: “Research competitions feature problems which are more experimental than featured competition problems.”
3. 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.”
4. 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!

• Two Sigma Using News to Predict Stock Movements
  • Type: Featured
  • Teams: 2,902
  • Prize: $100,000
• LANL Earthquake Prediction
  • Type: Research
  • Teams: 573
  • Prize $50,000
• Google Landmark Recognition 2019
  • Type: Research
  • Teams: 96
  • Prize: $25,000
• Google Landmark Retrieval 2019
  • Type: Research
  • Teams: 96
  • Prize: $25,000
• Freesound Audio Tagging 2019
  • Type: Research
  • Teams: 521
  • Prize: $5,000
• Digital Recognizer
  • Type: Getting Started
  • Teams: 2,680
  • Prize: Knowledge
• Titanic: Machine Learning from Disaster
  • Type: Getting Started
  • Teams: 10,234
  • Knowledge
• House Prices: Advanced Regression Technniques
  • Type: Getting Started
  • Teams: 4,443
  • Prize: Knowledge
• ImageNet Object Localization Challenge
  • Type: Research
  • Teams: 31
  • Prize: Knowledge
• Predict Future Sales
  • Type: Playground
  • Teams: 2,170
  • Prize: Kudos
• iMaterialist
  • Type: Research
  • Teams: 36
  • Prize: Kudos
• iNaturalist
  • Type: Research
  • Teams: 120
  • Prize: Kudos
• iWildCam 2019 – FGVC6
  • Type: Research
  • Teams: 159
  • Prize: Kudos
• iMet Collection 2019 – FGVC6
  • Type: Research
  • Teams: 369
  • Prize: Kudos
• Aerial Cactus Identification
  • Type: Playground
  • Teams: 507
  • Prize: Knowledge
• TMD Box Office Prediction
  • Type: Playground
  • Teams: 971
  • Prize: Knowledge

Know more about Kaggle competitions

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.

 

 

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.

 

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.

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.

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.

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.

In 2019, Data Science Dojo sponsored Kaggle Days taking place from December 11 to 12.

Kaggle Days will give Data Science Dojo a platform to continue giving back to the data science community.

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-rated Data 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.

Helpful links

• Kaggle Days Tokyo – Home
• Data Science Dojo – Home
• Kaggle Days – Facebook Page

Data Science Dojo meetup groups

• Seattle
• Austin
• Silicone Valley
• New York
• Phoenix
• Washington DC
• Chicago
• Toronto
• Singapore
• Karachi

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.

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!