This poster was presented at the AMIA 2017 Joint Summits on Translational Science as part of the Big Data Education Hackathon session/competition organized by Philip Payne (WUSTL). It is a product of my personal artistic efforts as RedPen/BlackPen (@redpenblackpen), a webcomic that is focused on research, peer review, and academic life. The poster provides an overview of educational areas and approaches for big data for computational biologists but does so in a very engaging way.

Dataset

This dataset was created by Liam Nguyen

Contents

Energy Hackathon Platform Market Outlook

According to our latest research, the global Energy Hackathon Platform market size reached USD 1.12 billion in 2024, and is projected to grow at a robust CAGR of 15.4% from 2025 to 2033. By the end of the forecast period in 2033, the market is expected to achieve a value of USD 3.74 billion. The primary growth driver for this market is the increasing demand for innovative and collaborative solutions to address complex energy sector challenges, fueled by the global push for renewable energy adoption, digital transformation, and sustainability initiatives.

The Energy Hackathon Platform market is witnessing remarkable growth due to the rising need for rapid innovation within the energy sector. As energy systems become more complex and interconnected, traditional R&D cycles are proving too slow to keep pace with the evolving landscape. Hackathons provide a dynamic and agile environment for ideation, prototyping, and solution development, enabling organizations to tap into diverse talent pools and crowdsource disruptive ideas. The proliferation of digital technologies and open-source tools has further lowered the barriers to participation, making hackathons an increasingly attractive option for corporates, startups, and public sector entities seeking to accelerate the development and deployment of new energy technologies.

Another significant growth factor is the escalating investments in renewable energy and smart grid infrastructure globally. Governments and private players alike are under pressure to meet ambitious sustainability targets, enhance energy efficiency, and ensure grid reliability. Energy hackathon platforms are emerging as critical enablers for these goals, providing structured frameworks for collaborative problem-solving and fostering partnerships between stakeholders from academia, industry, and government. These platforms not only catalyze the generation of innovative solutions but also play a pivotal role in talent identification, skills development, and ecosystem building, which are essential for the long-term transformation of the energy sector.

Furthermore, the increasing digitalization and data-driven nature of the energy industry are fueling the adoption of hackathon platforms. The integration of AI, IoT, cloud computing, and big data analytics into energy systems has created new opportunities and challenges that require rapid, multidisciplinary approaches to innovation. Hackathon platforms facilitate the convergence of expertise from software development, engineering, data science, and energy management, resulting in more holistic and impactful solutions. As organizations seek to leverage digital technologies to optimize operations, reduce costs, and enhance customer engagement, the demand for energy hackathon platforms is expected to intensify in the coming years.

Regionally, North America currently leads the Energy Hackathon Platform market, accounting for the largest share in 2024, driven by a mature technology ecosystem, strong corporate participation, and substantial government funding for energy innovation. Europe follows closely, benefiting from progressive energy policies and a vibrant research and startup community. The Asia Pacific region is poised for the fastest growth during the forecast period, propelled by rapid urbanization, increasing energy demand, and supportive government initiatives. Latin America and the Middle East & Africa are also witnessing growing interest, particularly in the context of renewable energy and grid modernization efforts, although market maturity and adoption rates vary significantly across countries.

Component Analysis

The Energy Hackathon Platform market is segmented by component into Platform and Services. The platform segment encompasses the core digital infrastructure that enables the organization, management, and execution of hackathons, including features such as team formation, challenge management, submission portals, judging s

MassDOT Visualizing Transportation Hackathon, December 2013. Informing the Future of Massachusetts Transportation through Data Analysis and Visualization. Introduction At the MassDOT Visualizing Transportation Hackathon, the Massachusetts Department of Transportation (MassDOT), in partnership with the Mass Big Data Initiative, will release a series of related data sets on travel in Massachusetts and will open a challenge to the public to collaborate around analyzing this data and visualizing resulting insights to help inform the future of transportation in the Commonwealth. We invite participants to explore a collection of transportation data with a specific focus on travel behavior, road-rail comparisons, and the energy, environmental, and social impacts of transportation mode-choice. Background Each day in Massachusetts, travelers throughout the state make individual decisions on how to reach their destinations. Together, the public?s transportation ?mode choice? translates into sign

ABSTRACT

Large p small n problem is a challenging problem in big data analytics. There are no de facto standard methods available to it. In this study, we propose a tensor decomposition (TD) based unsupervised feature extraction (FE) formalism applied to multiomics datasets, where the number of features is more than 100000 while the number of instances is as small as about 100. The proposed TD based unsupervised FE outperformed other conventional supervised feature selection methods, such as random forest, categorical regression (also known as analysis of variance, ANOVA), and penalized linear discriminant analysis when they are applied to not only multiomics datasets but also synthetic datasets. Genes selected by TD based unsupervised FE were biologically reliable. TD based unsupervised FE turned out to be not only the superior feature selection method but also the method that can select biologically reliable genes.

Instructions:

This is a supplementary file of paper submitted to bigdata2020

Inspiration:

This dataset uploaded to U-BRITE for "AI against CANCER DATA SCIENCE HACKATHON"

https://cancer.ubrite.org/hackathon-2021/

Acknowledgements

Y-h. Taguchi, July 17, 2020, "Metascape results for Prostate cancer multiomics data", IEEE Dataport, doi: https://dx.doi.org/10.21227/rdmb-jm40.

https://ieee-dataport.org/documents/metascape-results-prostate-cancer-multiomics-data

U-BRITE last update date: 07/21/2021

Overview

The current pandemic has dwindled the data science job market likewise recruiters are also facing difficulties filtering the right talent. To bridge this gap we bring a chance for the MachineHack community to compete for jobs with some of the key analytics players for a rewarding career in Data Science. In this competition, we are challenging the MachineHack community to come up with an algorithm to predict the price of retail items belonging to different categories. Foretelling the Retail price can be a daunting task due to the huge datasets with a variety of attributes ranging from Text, Numbers(floats, integers), and DateTime. Also, outliers can be a big problem when dealing with unit prices.

With a key focus on the Data Scientist role in an esteemed organization, this hackathon can help freshers and experienced folks prove their mettle and land up in a rewarding career.

By participating in this hackathon, every participant will be eligible for the Data Scientist job role by making sure their MachineHack Information with Resume is up to date.

Dataset Description:

• Train.csv - 284780 rows x 8 columns (Inlcudes UnitPrice Columns as Target)
• Test.csv - 122049 rows x 7 columns
• Sample Submission.csv - Please check the Evaluation section for more details on how to generate a valid submission

Attribute Description:

• Invoice No - Invoice ID, encoded as Label
• StockCode - Unique code per stock, encoded as Label
• Description - The Description, encoded as Label
• Quantity - Quantity purchased
• InvoiceDate - Date of purchase
• UnitPrice - The target value, price of every product
• CustomerID - Unique Identifier for every Customer
• Country - Country of sales, encoded as Label

Skills:

• Multivariate Regression *
• Big dataset, underfitting vs overfitting *
• Optimizing RMSE to generalize well on unseen data *

Overview

Note: This Dataset is taken from MachineHack - Deloitte Hackathon

Deloitte refers to one or more of Deloitte Touche Tohmatsu Limited (“DTTL”), its global network of member firms, and their related entities (collectively, the “Deloitte organization”). DTTL (also referred to as “Deloitte Global”) and each of its member firms and related entities are legally separate and independent entities, which cannot obligate or bind each other in respect of third parties. DTTL and each DTTL member firm and the related entity are liable only for their own acts and omissions, and not those of each other. DTTL does not provide services to clients. Please see www.deloitte.com/about learn more.

All the facts and figures that talk to our size and diversity and years of experiences, as notable and important as they may be, are secondary to the truest measure of Deloitte: the impact we make in the world.

So, when people ask, “what’s different about Deloitte?” the answer resides in the many specific examples of where we have helped Deloitte member firm clients, our people, and sections of society to achieve remarkable goals, solve complex problems or make meaningful progress. Deeper still, it’s in the beliefs, behaviours and fundamental sense of purpose that underpin all that we do. Deloitte Globally has grown in scale and diversity—more than 345,000 people in 150 countries, providing multidisciplinary services yet our shared culture remains the same.

(C) 2021 Deloitte Touche Tohmatsu India LLP”

Banks run into losses when a customer doesn't pay their loans on time. Because of this, every year, banks have losses in crores, and this also impacts the country's economic growth to a large extent. In this hackathon, we look at various attributes such as funded amount, location, loan, balance, etc., to predict if a person will be a loan defaulter or not.

To solve this problem, MachineHack has created a training dataset of 67,463 rows and 35 columns and a testing dataset of 28,913 rows and 34 columns. The hackathon demands a few pre-requisite skills like big dataset, underfitting vs overfitting, and the ability to optimise “log_loss” to generalise well on unseen data.

Data innovations happen daily: the semantic web, the cloud, visualization, mapping, sensors, spatial data infrastructures, etc. This portion of the Training Day will focus on recent access to public data initiatives in Canada with an emphasis on open government and open data. In this session participants will be introduced to data and participatory democracy, open data definitions and examples of good government policy. In addition, we will look at what some community groups are doing, the leadership in Canada’s big cities and the Province of BC by administrations and citizens. This will include licenses, open data initiatives, hackfests, hackathons, applications, challenges and opportunities. It is hoped that this overview will provide participants with insight about what is new in the Canadian access to public data world.

This is a large dataset of extracted text from public Oil and gas documents that was prepared in the run up to the FORCE 2023 Large Languagel model Hackathon in Stavanger, Norway

The dataset is uninque since it contains the largest public collection of extracted text from Ocr'ed oil and gas documents currently available. It has been created with the aim to make more oil and gas documents knowledge better embedded in language modelsAdditional the text has been classified in if the extracted pages are real text or mostly gibberish.Personal identifiable information has been removed as best as possibleA file with 1500 hand classified pages is part of the upload to further train text classifiers.