Data science hackathon

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The closing presentation of data science hackathone - public version. Event: https://www.facebook.com/events/1507692569541762

Data & Analytics

Big thanks to our organizer team

Big thanks to our general partner

Big thanks to our gift partners and supporters

Welcome “ASU Labs” initiative
- Goal: provide real research in the field of computer science in Ukraine, aligned
to state of the art world standards of the scientific work
- Main options:
- Publish papers in the peer reviewed top-most scientific journals
- Contribute to the open-source research community
- Develop new open program libraries and data sets for the future researches
- Who may participate:
- Academic researchers and professors, industry experts
- Students, PhD-students of the Ukrainian universities
http://asu-labs.github.io

Project presentations

3rd place:
Way.me

2nd place:
Allen-Ai-Googler
Deep Vehicle

1st place:
kuDATA
(Voice conversion)

See you next time!

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如何办理英国诺森比亚大学毕业证（NU毕业证书）成绩单原件一模一样

如何办理英国诺森比亚大学毕业证（NU毕业证书）成绩单原件一模一样

如何办理英国诺森比亚大学毕业证（NU毕业证书）成绩单原件一模一样

Yilin Xia (yilinx2@illinois.edu), Shawn Bowers (bowers@gonzaga.edu), Lan Li (lanl2@illinois.edu), and Bertram Ludäscher (ludaesch@illinois.edu) Presented at IDCC-2024 in Edinburg. ABSTRACT. We propose a new approach for modeling and reconciling conflicting data cleaning actions. Such conflicts arise naturally in collaborative data curation settings where multiple experts work independently and then aim to put their efforts together to improve and accelerate data cleaning. The key idea of our approach is to model conflicting updates as a formal argumentation framework (AF). Such argumentation frameworks can be automatically analyzed and solved by translating them to a logic program PAF whose declarative semantics yield a transparent solution with many desirable properties, e.g., uncontroversial updates are accepted, unjustified ones are rejected, and the remaining ambiguities are exposed and presented to users for further analysis. After motivating the problem, we introduce our approach and illustrate it with a detailed running example introducing both well-founded and stable semantics to help understand the AF solutions. We have begun to develop open source tools and Jupyter notebooks that demonstrate the practicality of our approach. In future work we plan to develop a toolkit for conflict resolution that can be used in conjunction with OpenRefine, a popular interactive data cleaning tool.

Reconciling Conflicting Data Curation Actions: Transparency Through Argument...

Reconciling Conflicting Data Curation Actions: Transparency Through Argument...

Reconciling Conflicting Data Curation Actions: Transparency Through Argument...

Bertram Ludäscher

20240412-SmartCityIndex-2024-Full-Report.pdf

20240412-SmartCityIndex-2024-Full-Report.pdf

20240412-SmartCityIndex-2024-Full-Report.pdf

RESEARCH-FINAL-DEFENSE-PPT-TEMPLATE.pptx

RESEARCH-FINAL-DEFENSE-PPT-TEMPLATE.pptx

RESEARCH-FINAL-DEFENSE-PPT-TEMPLATE.pptx

ronsairoathenadugay

Building Real-Time Pipelines With FLaNK Timothy Spann, Principal Developer Advocate, Streaming - Cloudera Future of Data meetup, startup grind, AI Camp The combination of Apache Flink, Apache NiFi, and Apache Kafka for building real-time data processing pipelines is extremely powerful, as demonstrated by this case study using the FLaNK-MTA project. The project leverages these technologies to process and analyze real-time data from the New York City Metropolitan Transportation Authority (MTA). FLaNK-MTA demonstrates how to efficiently collect, transform, and analyze high-volume data streams, enabling timely insights and decision-making. Apache NiFi Apache Kafka Apache Flink Apache Iceberg LLM Generative AI Slack Postgresql

DATA SUMMIT 24 Building Real-Time Pipelines With FLaNK

DATA SUMMIT 24 Building Real-Time Pipelines With FLaNK

DATA SUMMIT 24 Building Real-Time Pipelines With FLaNK

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Digital advertising, or paid media, encompasses the strategic deployment of online advertisements to reach target audiences efficiently and effectively. This includes any digital platform that supports advertising to deliver unique messages for any objective. Understanding the mechanics of digital advertising platforms, along with insights into audience behaviors and preferences, allows marketers to optimize their ad spend and achieve significant engagement and conversion rates. This lecture is for Advanced Digital & Social Media Strategy (MGMTX 466.05) at UCLA Extension.

Digital Advertising Lecture for Advanced Digital & Social Media Strategy at U...

Digital Advertising Lecture for Advanced Digital & Social Media Strategy at U...

Digital Advertising Lecture for Advanced Digital & Social Media Strategy at U...

Valters Lauzums

Context 1. Housing Agent collected resale prices on HDB apartments in Singapore. Objective 2. To predict resale prices in to advise his potential clients. Strategies 3. Explore & Clean data for analysis. 4. Perform K-Means Clustering, in Orange, to find possible segments in the customer data. 5. Tune the model to improve its performance. 6. Visualise the findings, share conclusions, and give insight-driven recommendations. Author: Anthony mok Date: 18 Nov 2023 Email: xxiaohao@yahoo.com

Predicting HDB Resale Prices - Conducting Linear Regression Analysis With Orange

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ThinkInnovation

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5CL-ADBA,5cladba, Chinese supplier, safety is guaranteed

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如何办理英国诺森比亚大学毕业证（NU毕业证书）成绩单原件一模一样

如何办理英国诺森比亚大学毕业证（NU毕业证书）成绩单原件一模一样

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Reconciling Conflicting Data Curation Actions: Transparency Through Argument...

Reconciling Conflicting Data Curation Actions: Transparency Through Argument...

20240412-SmartCityIndex-2024-Full-Report.pdf

20240412-SmartCityIndex-2024-Full-Report.pdf

20240412-SmartCityIndex-2024-Full-Report.pdf

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RESEARCH-FINAL-DEFENSE-PPT-TEMPLATE.pptx

RESEARCH-FINAL-DEFENSE-PPT-TEMPLATE.pptx

DATA SUMMIT 24 Building Real-Time Pipelines With FLaNK

DATA SUMMIT 24 Building Real-Time Pipelines With FLaNK

DATA SUMMIT 24 Building Real-Time Pipelines With FLaNK

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7. Epi of Chronic respiratory diseases.ppt

7. Epi of Chronic respiratory diseases.ppt

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Digital Advertising Lecture for Advanced Digital & Social Media Strategy at U...

Digital Advertising Lecture for Advanced Digital & Social Media Strategy at U...

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Data science hackathon

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2. Big thanks to our organizer team

3. Big thanks to our general partner

4. Big thanks to our gift partners and supporters

5. Welcome “ASU Labs” initiative - Goal: provide real research in the field of computer science in Ukraine, aligned to state of the art world standards of the scientific work - Main options: - Publish papers in the peer reviewed top-most scientific journals - Contribute to the open-source research community - Develop new open program libraries and data sets for the future researches - Who may participate: - Academic researchers and professors, industry experts - Students, PhD-students of the Ukrainian universities http://asu-labs.github.io

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45. Mentors’ contacts Stefan Yurkevitch - urbnwanderr@gmail.com Oleksandr Baiev - http://facebook.com/baiev Sergii Gavrylov - https://www.facebook.com/profile.php?id=100006450584630 Dmitry Dziuba - aka.3d6@gmail.com Artem Chernodub - a.chernodub@gmail.com

46. 3rd place: Way.me

47. 2nd place: Allen-Ai-Googler Deep Vehicle

48. 1st place: kuDATA (Voice conversion)

49. See you next time!