Winning these Kaggle competitions will be a Great Addition to Data Science Resumes.

If you are a data science professional or a machine learning engineer, you may have heard of Kaggle. Kaggle is the world’s largest data science community with numerous courses, books, and tutorials that address various concepts of this field. Apart from all the matter, another aspect that makes Kaggle exciting for tech enthusiasts is their competition.

Kaggle competitions are machine learning tasks made by Kaggle or other companies. These competitions vary on the basis of problems and complexity. For beginners or experienced professionals, there are competitions for everyone to participate with cash prizes.

Out of all the active ones, there are the most exciting competitions for ML engineers and data scientists, and analysts.

1. SIIM-FISABIO-RSNA COVID-19 Detection

Objective: To Identify and localize COVID-19 abnormalities on chest radiographs

Entry Deadline: August 2

Cash Prize: US$100,000

COVID-19 is wreaking havoc in the world and healthcare professionals are having a tough time controlling the epidemic. COVID-19 looks very similar to other viral and bacterial pneumonia on chest radiographs, which makes it difficult to diagnose. You have to build a computer vision model to detect and localize COVID-19. It would help doctors provide a quick and confident diagnosis. As a result, patients could get the right treatment before the most severe effects of the virus takes hold.

2. Optiver Realized Volatility Prediction

Objective: Apply your data science skills to make financial markets better

Entry Deadline: September 20

Cash Prize: US$100,000

Volatility is one of the most prominent terms you’ll hear on any trading floor – and for good reason. In financial markets, volatility captures the amount of fluctuation in prices. High volatility is associated with periods of market turbulence and large price swings, while low volatility describes more calm and quiet markets. For trading firms like Optiver, accurately predicting volatility is essential for the trading of options, whose price is directly related to the volatility of the underlying product.

In the first three months of this competition, you’ll build models that predict short-term volatility for hundreds of stocks across different sectors. You will have hundreds of millions of rows of highly granular financial data at your fingertips, with which you’ll design your model forecasting volatility over 10-minute periods. Your models will be evaluated against real market data collected in the three-month evaluation period after training.

3. CommonLit Readability Prize

Objective: Rate the complexity of literary passages for grades 3-12 classroom use

Entry Deadline: July 26

Cash Prize: US$60,000

Reading is an important fundamental skill, and currently, most educational texts are matched to readers using traditional readability methods or commercially available formulas. However, each has its issues. Tools like Flesch-Kincaid Grade Level are based on weak proxies of text decoding (i.e., characters or syllables per word) and syntactic complexity (i.e., number or words per sentence). As a result, they lack construct and theoretical validity. At the same time, commercially available formulas, such as Lexile, can be cost-prohibitive, lack suitable validation studies, and suffer from transparency issues when the formula’s features aren’t publicly available.

In this competition, you’ll build algorithms to rate the complexity of reading passages for grade 3-12 classroom use. To accomplish this, you’ll pair your machine learning skills with a dataset that includes readers from a wide variety of age groups and a large collection of texts taken from various domains. Winning models will be sure to incorporate text cohesion and semantics.

4.  MLB Player Digital Engagement Forecasting

Objective: Predict fan engagement with baseball player digital content

Entry Deadline: July 20

Cash Prize: US$50,000

You need to tend to the interests of baseball fans. Major League Baseball (MLB) and Google Cloud want the Kaggle community’s help to identify the many other factors which pique supporter engagement and create deeper relationships between players and fans. In this competition, you’ll predict how fans engage with MLB players’ digital content on a daily basis for a future date range. You’ll have access to player performance data, social media data, and team factors like market size. Successful models will provide new insights into what signals most strongly correlate with and influence engagement.

5. SETI Breakthrough Listen – E.T Signal Search

Objective: Find extraterrestrial signals in data from deep space

Entry Deadline: July 21

Cash Prize: US$15,000

“Are we alone in this universe” is a question that is a mystery in itself. To find the answers, the University of California, Berkeley, employs the world’s most powerful telescopes to scan millions of stars for signs of technology. Now it wants the Kaggle community to help interpret the signals they pick up.

In this competition, use your data science skills to help identify anomalous signals in scans of Breakthrough Listen to targets. Because there are no confirmed examples of alien signals to use to train machine learning algorithms, the team included some simulated signals (that they call “needles”) in the haystack of data from the telescope. They have identified some of the hidden needles so that you can train your model to find more. The data consist of two-dimensional arrays, so there may be approaches from computer vision that are promising, as well as digital signal processing, anomaly detection, and more. The algorithm that’s successful at identifying the most needles will win a cash prize but also has the potential to help answer one of the biggest questions in science.

6. Google Smartphone Decimeter Challenge

Objective: Improve high precision GNSS positioning and navigation accuracy on smartphones

Entry Deadline: July 28

Cash Prize: US$10,000

The world needs a navigation app that is more accurate with regards to bumps and potholes. Machine learning and precision GNSS algorithms are expected to improve this accuracy and provide billions of Android phone users with a more fine-tuned positioning experience. This competition, hosted by the Android GPS team, is being presented at the ION GNSS+ 2021 Conference. They seek to advance research in smartphone GNSS positioning accuracy and help people better navigate the world around them.

In this competition, you’ll use data collected from the host team’s own Android phones to compute location down to decimeter or even centimeter resolution, if possible. You’ll have access to precise ground truth, raw GPS measurements, and assistance data from nearby GPS stations, in order to train and test your submissions.

The post EXCITING KAGGLE COMPETITIONS WITH CASH PRIZES IN DATA SCIENCE appeared first on Artificial Intelligence.

Data scientists need to assemble predictive analytics workflow benefits to review on processes and algorithms. Data science techniques and tools are constantly evolving and only online resources can aid data scientists to keep up the pace. Even though when traditional models like reading books and journals give the understanding of fundamental concepts that benefit data scientists on a large-scale. The need for a community of experts to support the work of a data scientist has ignited a number of forums and groups where people seek help online. Henceforth, Analytics Insight is bringing a list of top 10 data science communities that professionals can take part in.

Kaggle

Kaggle is one of the world’s largest data science communities with powerful tools and resources. The community has around 3 million active members. Data scientists can find all the code and data they need for their data science work. They can use over 50,000 public datasets and 400,000 public notebooks to conquer any analysis in no time. Kaggle, a subsidiary of Google LLC, allows users to find and publish data sets, explore and build models in a web-based data-science environment, work with other data scientists and machine learning engineers, and enter competitions to solve data science challenges. Kaggle started its community in 2010 by offering machine learning competitions. It later extended its services to a public data platform, a cloud-based workbench for data science, and artificial intelligence education.

IBM Data Community

IBM Data Science Community offers a constant stream of freshly updated content including featured blogs and forums for discussion and collaboration. The community provides access to the latest white papers, webcasts, presentations, and research uniquely for members, by members.

Reddit

Reddit is a one-stop platform for reading on almost any topics with 40 million searches everyday and over 430 million monthly active users, and 1.2 million communities or subreddits. These subreddits represent the topic-based groups or community on Reddit that comprises users who want to publish and discuss news or stories with that topic. Data science is one of the famous subreddits that data scientists look for. Some of the top subreddits that every data scientists should join are r/datascience, r/dataisbeautiful, r/MachineLearning, etc.

Open Data Science

Open Data Science is a global community that unites all researchers, engineers and developers around data science and related areas. It creates data science projects, and conducts events and educational courses. Data scientists in the community share their experience, which helps other sprouting data scientists to develop their skills.

Data Science Central

Data Science Central is the industry’s online resource for data practitioners. It provides services starting from Statistics to Analytics, Machine Learning, Artificial Intelligence (AI) and Data Science Central. The platform gives a community experience that includes a rich editorial platform, social interaction, forum-based support, plus the latest information on technology, tools, trends, and careers.

Data Community DC

Data Community DC is a non-profit organization committed to connecting and promoting the work of data professionals by fostering education, opportunity, and professional development through high-quality, community-driven events, resources, products and services. Data Community DC or DC2 has six meetup groups with over 5000 unique members, a board of 12 people, a blog, occasional workshops, and plans for bigger events in the future.

Stack Overflow

Stack Overflow is an open community for anyone that codes. This mainly helps data scientists who code, to get the answer for their toughest data science questions and share knowledge with co-workers. Slack Overflow serves 100 million people every month making it one of the 50 most popular websites in the world.

Dataquest

Dataquest has a data science community that allows access for all its students. The trained moderators and other learners help the data science students with their queries. The community is a go-to resource if data science students are stuck on a mission, encounter a platform issue, need advice or want feedback on the project.

Data Science Society

Data Science Society is an international digital community. The platform has been building a strong core of members digitally around a body of data science knowledge while having fun. So far, data science Society has organized multiple data science meetups and international datathons with attendees from more than 20 countries.

DrivenData

DrivenData works on projects at the intersection of data science and social impact, in areas like international development, health, education, research and conservation, and public services. The platform works on giving organizations more access to the capabilities of data science, engage more data scientists with social challenges where their skills can make a difference. The DrivenData community has leveraged the luxury for the team to work with 35 organizations across 50 projects.

The post TOP 10 DATA SCIENCE COMMUNITIES EVERY DATA SCIENTIST MUST KNOW appeared first on Artificial Intelligence.

Having knowledge is not enough to survive, for survival, and for assured victory, one needs to test his/her capabilities. To test wisdom and capabilities, competitions are extremely beneficial. In the case of data science, data scientists mostly work theoretically and get rare chance to experiment with real-world data themselves. With data science competitions and platforms, aspiring professionals are offered with a medium to interact and compete in solving real-life problems.

According to Towards Data Science, data competitions serve many purposes. One of the radical benefits is that they are the perfect place to learn best practices, accrue feedback on your work, and augment your skills. They can also serve as a channel for problem-solving and brainstorming by probing the multitude of crowdsourced solutions to big problems.

Therefore, here we have brought you the list of top data science competitions to test your knowledge and capabilities.

Kaggle

Kaggle is a great, if not the best platform for Data Science. It offers everyone to have a chance to get into the biggest data science community in the world. Kaggle enables data scientists and other developers and to host datasets, to engage in running machine learning contests, and to write and share code. It’s a crowd-sourced platform to attract, nurture, train, and challenge data scientists from all around the world to solve data science, machine learning, and predictive analytics problems.

The types of data science problems posted on Kaggle can be anything from attempting to predict cancer occurrence by examining patient records to analyzing sentiment to evoke by movie reviews and how this affects audience reaction. Competitions hosted on Kaggle have had far-reaching impacts such as enhancing and enabling state of the art HIV/AIDS research and improving traffic forecasting. Fundamentally, Kaggle has bestowed companies the opportunity to seek solutions from the best data scientist in the world and to have external pairs of eyes to look at the problems they are trying to solve.

CrowdANALYTIX Community

CrowdANALYTIX also features data modeling competitions, diving into machine learning, artificial intelligence, deep learning, and natural language processing. These challenges are more informal, though no less rewarding. Like many of the other competitions listed here, some competitions are for the sake of learning, and others have a prize pool.

The platform consists of two “layers,” the machine layer of bots and the human layer of data scientists building those bots and algorithms. Here, the data competitions take a slight turn and are viewed as more of a work in progress for consistent iteration. Winning algorithms are moved to CrowdANALYTIX’s database and then monitored for fine-tuning. If the algorithm starts to degrade, it is moved back to the community to be adjusted or rebuilt.

Coda Lab Competitions

Coda Lab is an open-source platform for computational research. The competitions are held for the sake of collaborative research and code testing. While they don’t offer prestigious prizes, they work together to create more efficient and reproducible code. Coda Lab features heavily on the programming and code-building of data and can be a good way to dip your feet into collaborative projects and challenges.

Topcoder Open

Topcoder is similar to Coda Lab in that it is also a collaborative effort to compile code testing and research. They have a wide array of challenges and competitions on their main site, ranging from data science to coding to web design. Many of these offer decent prize rewards, though some are simply for the sake of a challenge.

The main draw is the annual Topcoder Open, the “Ultimate Programming and Design Tournament.” It features a range of competitions such as algorithms, development, UI design, and quality assurance. The initial competitions are online, with the winners earning points that net them additional prizes and a trip to the TCO finals hosted in the US. The TCO also has smaller regional events to bring the competition to even more people. These events are only a day or two, but offer more international opportunities to get involved.

International Data Analysis Olympiad (IDAHO)

IDAHO is an annual competition organized by the Higher School of Economics and Yandex. This event is open to all teams and individuals, be they undergraduate, postgraduate, or Ph.D. students, company employees, researchers, or new data, scientists.

The aim is to bridge the gap between the all-increasing complexity of Machine Learning models and performance bottlenecks of the industry. The participants will strive not only to maximize the quality of their predictions but also to devise resource-efficient algorithms.

This will be a team machine learning competition, divided into two stages. The first stage will be online, open to all participants. The second stage will be the offline on-site finals, in which the top 30 performing teams from the online round will compete at the Yandex office in Moscow.

DrivenData

DrivenData: Data Science Competitions for Social Good is an online challenge that usually lasts 2–3 months. where a global community of data scientists competes to come up with the best statistical model for difficult predictive problems that make a difference.

DrivenData brings cutting-edge practices in data science and crowdsourcing to some of the world’s biggest social challenges and the organizations taking them on. They run machine learning competitions to help non-profits, NGOs, governments, and other social impact organizations use data science in the service of humanity.

Part of DrivenData’s mission is to enable data scientists and non-profits to learn from the work that is done in these competitions. To this end, the code submitted by winners is released under an open-source license for others to learn from, use, and adapt.

The post TOP DATA SCIENCE COMPETITIONS TO TEST YOUR KNOWLEDGE appeared first on Artificial Intelligence.