Data science
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This document provides an overview of common data science tasks, algorithms, and examples. It describes tasks like classification, regression, clustering, anomaly detection, time series analysis, and association analysis. For each task, it lists example algorithms like decision trees, k-means clustering, and Apriori association rules. It also provides real-world examples of applying each task, such as predicting political affiliations through classification or identifying customer segments through clustering. Finally, it outlines the topics that will be covered in a data science course, including algorithms, applications, and the data science process.
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The discussion will extend to how AI can be used to transform XML content. In particular, the focus will be on the use of AI XPath extension functions in XSLT, Schematron, Schematron Quick Fixes, or for XML content refactoring.
The presentation aims to deliver a comprehensive overview of AI usage in XML development, providing attendees with the necessary knowledge to make informed decisions. Whether you’re at the early stages of adopting AI or considering integrating it in advanced XML development, this presentation will cover all levels of expertise.
By highlighting the potential advantages and challenges of integrating AI with XML development tools and languages, the presentation seeks to inspire thoughtful conversation around the future of XML development. We’ll not only delve into the technical aspects of AI-powered XML development but also discuss practical implications and possible future directions.
Let's Integrate MuleSoft RPA, COMPOSER, APM with AWS IDP along with Slack
Discover the seamless integration of RPA (Robotic Process Automation), COMPOSER, and APM with AWS IDP enhanced with Slack notifications. Explore how these technologies converge to streamline workflows, optimize performance, and ensure secure access, all while leveraging the power of AWS IDP and real-time communication via Slack notifications.
Enchancing adoption of Open Source Libraries. A case study on Albumentations.AI
Enchancing adoption of Open Source Libraries. A case study on Albumentations.AIVladimir Iglovikov, Ph.D.
Presented by Vladimir Iglovikov:
- https://www.linkedin.com/in/iglovikov/
- https://x.com/viglovikov
- https://www.instagram.com/ternaus/
This presentation delves into the journey of Albumentations.ai, a highly successful open-source library for data augmentation.
Created out of a necessity for superior performance in Kaggle competitions, Albumentations has grown to become a widely used tool among data scientists and machine learning practitioners.
This case study covers various aspects, including:
People: The contributors and community that have supported Albumentations.
Metrics: The success indicators such as downloads, daily active users, GitHub stars, and financial contributions.
Challenges: The hurdles in monetizing open-source projects and measuring user engagement.
Development Practices: Best practices for creating, maintaining, and scaling open-source libraries, including code hygiene, CI/CD, and fast iteration.
Community Building: Strategies for making adoption easy, iterating quickly, and fostering a vibrant, engaged community.
Marketing: Both online and offline marketing tactics, focusing on real, impactful interactions and collaborations.
Mental Health: Maintaining balance and not feeling pressured by user demands.
Key insights include the importance of automation, making the adoption process seamless, and leveraging offline interactions for marketing. The presentation also emphasizes the need for continuous small improvements and building a friendly, inclusive community that contributes to the project's growth.
Vladimir Iglovikov brings his extensive experience as a Kaggle Grandmaster, ex-Staff ML Engineer at Lyft, sharing valuable lessons and practical advice for anyone looking to enhance the adoption of their open-source projects.
Explore more about Albumentations and join the community at:
GitHub: https://github.com/albumentations-team/albumentations
Website: https://albumentations.ai/
LinkedIn: https://www.linkedin.com/company/100504475
Twitter: https://x.com/albumentationsCommunications Mining Series - Zero to Hero - Session 1
This session provides introduction to UiPath Communication Mining, importance and platform overview. You will acquire a good understand of the phases in Communication Mining as we go over the platform with you. Topics covered:
• Communication Mining Overview
• Why is it important?
• How can it help today’s business and the benefits
• Phases in Communication Mining
• Demo on Platform overview
• Q/A
“I’m still / I’m still / Chaining from the Block”
“An Outlook of the Ongoing and Future Relationship between Blockchain Technologies and Process-aware Information Systems.” Invited talk at the joint workshop on Blockchain for Information Systems (BC4IS) and Blockchain for Trusted Data Sharing (B4TDS), co-located with with the 36th International Conference on Advanced Information Systems Engineering (CAiSE), 3 June 2024, Limassol, Cyprus.
20240607 QFM018 Elixir Reading List May 2024
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TrustArc Webinar - 2024 Global Privacy Survey
How does your privacy program stack up against your peers? What challenges are privacy teams tackling and prioritizing in 2024?
In the fifth annual Global Privacy Benchmarks Survey, we asked over 1,800 global privacy professionals and business executives to share their perspectives on the current state of privacy inside and outside of their organizations. This year’s report focused on emerging areas of importance for privacy and compliance professionals, including considerations and implications of Artificial Intelligence (AI) technologies, building brand trust, and different approaches for achieving higher privacy competence scores.
See how organizational priorities and strategic approaches to data security and privacy are evolving around the globe.
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GraphSummit Singapore | Enhancing Changi Airport Group's Passenger Experience...
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Data science
- 1. READ MORE ABOUT DATA SCIENCE IN NEXT SLIDES
- 5. What is Data Science
- 6. Models
- 8. Types of Data Science
- 9. Tasks Description Algorithms Examples Classification Predict if a data point belongs to one of predefined classes. The prediction will be based on learning from known data set. Decision Trees, Neural networks, Bayesian models, Induction rules, K nearest neighbors Assigning voters into known buckets by political parties eg: soccer moms. Bucketing new customers into one of known customer groups. Regression Predict the numeric target label of a data point. The prediction will be based on learning from known data set. Linear regression, Logistic regression Predicting unemployment rate for next year. Estimating insurance premium. Anomaly detection Predict if a data point is an outlier compared to other data points in the data set. Distance based, Density based, LOF Fraud transaction detection in credit cards. Network intrusion detection. Time series Predict if the value of the target variable for future time frame based on history values. Exponential smoothing, ARIMA, regression Sales forecasting, production forecasting, virtually any growth phenomenon that needs to be extrapolated Clustering Identify natural clusters within the data set based on inherit properties within the data set. K means, density based clustering - DBSCAN Finding customer segments in a company based on transaction, web and customer call data. Association analysis Identify relationships within an itemset based on transaction data. FP Growth, Apriori Find cross selling opportunities for a retailor based on transaction purchase history.
- 10. Course outline Data Science Process Data Exploration Model Evaluation Classification Decision Trees Rule Induction k-Nearest Neighbors Naïve Bayesian Artificial Neural Networks Support Vector Machines Ensemble Learners Regression Linear Regression Logistic Regression Association Analysis Apriori FP-Growth Clustering k-Means DBSCAN Self-Organizing Maps Text Mining Time Series Forecasting Anomaly Detection Feature Selection Process Basics Core Algorithms Common Applications