This talk demonstrate a complete Data Science process, involving Obtaining, Scrubbing, Exploring, Modeling and Interpreting data using Python ecosystem tools, like IPython Notebook, Pandas, Matplotlib, NumPy, SciPy and Scikit-learn.
Deep Recommender Systems - PAPIs.io LATAM 2018Gabriel Moreira
In this talk, we provide an overview of the state on how Deep Learning techniques have been recently applied to Recommender Systems. Furthermore, I provide an brief view of my ongoing Phd. research on News Recommender Systems with Deep Learning
This document provides an introduction to data science, including:
- Why data science has gained popularity due to advances in AI research and commoditized hardware.
- Examples of where data science is applied, such as e-commerce, healthcare, and marketing.
- Definitions of data science, data scientists, and their roles.
- Overviews of machine learning techniques like supervised learning, unsupervised learning, deep learning and examples of their applications.
- How data science can be used by businesses to understand customers, create personalized experiences, and optimize processes.
This document summarizes Ted Dunning's approach to recommendations based on his 1993 paper. The approach involves:
1. Analyzing user data to determine which items are statistically significant co-occurrences
2. Indexing items in a search engine with "indicator" fields containing IDs of significantly co-occurring items
3. Providing recommendations by searching the indicator fields for a user's liked items
The approach is demonstrated in a simple web application using the MovieLens dataset. Further work could optimize and expand on the approach.
Slide presentasi ini dibawakan oleh Imron Zuhri dalam acara Seminar & Workshop Pengenalan & Potensi Big Data & Machine Learning yang diselenggarakan oleh KUDO pada tanggal 14 Mei 2016.
This document provides an introduction to machine learning. It begins with an agenda that lists topics such as introduction, theory, top 10 algorithms, recommendations, classification with naive Bayes, linear regression, clustering, principal component analysis, MapReduce, and conclusion. It then discusses what big data is and how data is accumulating at tremendous rates from various sources. It explains the volume, variety, and velocity aspects of big data. The document also provides examples of machine learning applications and discusses extracting insights from data using various algorithms. It discusses issues in machine learning like overfitting and underfitting data and the importance of testing algorithms. The document concludes that machine learning has vast potential but is very difficult to realize that potential as it requires strong mathematics skills.
Introduction to Data Science and AnalyticsSrinath Perera
This webinar serves as an introduction to WSO2 Summer School. It will discuss how to build a pipeline for your organization and for each use case, and the technology and tooling choices that need to be made for the same.
This session will explore analytics under four themes:
Hindsight (what happened)
Oversight (what is happening)
Insight (why is it happening)
Foresight (what will happen)
Recording http://t.co/WcMFEAJHok
This document provides an introduction and overview of a summer school course on business analytics and data science. It begins by introducing the instructor and their qualifications. It then outlines the course schedule and topics to be covered, including introductions to data science, analytics, modeling, Google Analytics, and more. Expectations and support resources are also mentioned. Key concepts from various topics are then defined at a high level, such as the data-information-knowledge hierarchy, data mining, CRISP-DM, machine learning techniques like decision trees and association analysis, and types of models like regression and clustering.
The document discusses current and upcoming trends in search and AI. It notes that large datasets are less important than actionable intelligence. Assistive search using personalization, voice, images, conversations, context and providing answers and actions rather than just links is the new paradigm. The future of search and AI involves driving relevant interactions and experiences for customers through digital moments.
This document provides an overview of machine learning tools and languages. It discusses Python, R, and MATLAB as the most commonly used tools. For each tool, it lists advantages and disadvantages. Python is highlighted as the number one language for machine learning due to its many libraries and large user community. R is best for time series analysis and causal inference. MATLAB is still a leading tool for signal processing but lacks machine learning libraries. The document also provides resources for learning machine learning foundations and examples.
Natural Language Search with Knowledge Graphs (Haystack 2019)Trey Grainger
To optimally interpret most natural language queries, it is necessary to understand the phrases, entities, commands, and relationships represented or implied within the search. Knowledge graphs serve as useful instantiations of ontologies which can help represent this kind of knowledge within a domain.
In this talk, we'll walk through techniques to build knowledge graphs automatically from your own domain-specific content, how you can update and edit the nodes and relationships, and how you can seamlessly integrate them into your search solution for enhanced query interpretation and semantic search. We'll have some fun with some of the more search-centric use cased of knowledge graphs, such as entity extraction, query expansion, disambiguation, and pattern identification within our queries: for example, transforming the query "bbq near haystack" into
{ filter:["doc_type":"restaurant"], "query": { "boost": { "b": "recip(geodist(38.034780,-78.486790),1,1000,1000)", "query": "bbq OR barbeque OR barbecue" } } }
We'll also specifically cover use of the Semantic Knowledge Graph, a particularly interesting knowledge graph implementation available within Apache Solr that can be auto-generated from your own domain-specific content and which provides highly-nuanced, contextual interpretation of all of the terms, phrases and entities within your domain. We'll see a live demo with real world data demonstrating how you can build and apply your own knowledge graphs to power much more relevant query understanding within your search engine.
This talk is a primer to Machine Learning. I will provide a brief introduction what is ML and how it works. I will walk you down the Machine Learning pipeline from data gathering, data normalizing and feature engineering, common supervised and unsupervised algorithms, training models, and delivering results to production. I will also provide recommendations to tools that help you provide the best ML experience, include programming languages and libraries.
If there is time at the end of the talk, I will walk through two coding examples, using the HMS Titanic Passenger List, present with Python scikit-learn using algorithm random-trees to check if ML can correctly predict passenger survival and with R programming for feature engineering of the same dataset
Note to data-scientists and programmers: If you sign up to attend, plan to visit my Github repository! I have many Machine Learning coding examples in Python scikit-learn, GNU Octave, and R Programming.
https://github.com/jefftune/gitw-2017-ml
Machine learning with Big Data power point presentationDavid Raj Kanthi
This is an article made form the articles of IEEE published in the year 2017
The following presentation has the slides for the Title called the
Machine Learning with Big data. that following presentation which has the challenges and approaches of machine learning with big data.
The integration of the Big Data with Machine Learning has so many challenges that Big data has and what is the approach made by the machine learning mechanism for those challenges.
Meetup sthlm - introduction to Machine Learning with demo casesZenodia Charpy
This document provides an agenda and overview of topics related to data science and machine learning. It discusses data science processes including data preparation, algorithm selection, model deployment, and performance measurement. It also distinguishes machine learning from artificial intelligence and describes common machine learning algorithms like supervised and unsupervised learning. Examples of supervised and unsupervised learning applications are presented along with generic workflows. Machine learning algorithm selection and example cases are also summarized.
The Next Generation of AI-powered SearchTrey Grainger
What does it really mean to deliver an "AI-powered Search" solution? In this talk, we’ll bring clarity to this topic, showing you how to marry the art of the possible with the real-world challenges involved in understanding your content, your users, and your domain. We'll dive into emerging trends in AI-powered Search, as well as many of the stumbling blocks found in even the most advanced AI and Search applications, showing how to proactively plan for and avoid them. We'll walk through the various uses of reflected intelligence and feedback loops for continuous learning from user behavioral signals and content updates, also covering the increasing importance of virtual assistants and personalized search use cases found within the intersection of traditional search and recommendation engines. Our goal will be to provide a baseline of mainstream AI-powered Search capabilities available today, and to paint a picture of what we can all expect just on the horizon.
This video will give you an idea about Data science for beginners.
Also explain Data Science Process , Data Science Job Roles , Stages in Data Science Project
Data Tactics Analytics Brown Bag (November 2013)Rich Heimann
This document summarizes a brown bag presentation on analytics by Data Tactics Corporation. It introduces new analytic tools from the company including work on cyber intelligence and detection, the open source RAccumulo library, and Data Science for Program Managers. Case studies on discontinuities analysis and data science in Afghanistan are also mentioned. The document concludes by discussing the Shiny tool for building interactive web apps in R and providing contact information.
Tutorial@BDA 2017 -- Knowledge Graph Expansion and Enrichment Paris Sud University
This document outlines techniques for expanding and enriching knowledge graphs through data linking, key discovery, and link invalidation. It begins with an introduction to linked open data and knowledge graphs. The technical part discusses approaches to data linking, including instance-based methods that consider attribute similarity and graph-based methods that propagate similarity across object properties. Supervised methods use training data while rule-based methods apply expert-defined rules. Evaluation measures effectiveness using recall, precision and F1-score, and efficiency. The document also covers similarity measures and techniques for knowledge graph expansion and enrichment.
1) The document discusses a self-study approach to learning data science through project-based learning using various online resources.
2) It recommends breaking down projects into 5 steps: defining problems/solutions, data extraction/preprocessing, exploration/engineering, model implementation, and evaluation.
3) Each step requires different skillsets from domains like statistics, programming, SQL, visualization, mathematics, and business knowledge.
Thought Vectors and Knowledge Graphs in AI-powered SearchTrey Grainger
While traditional keyword search is still useful, pure text-based keyword matching is quickly becoming obsolete; today, it is a necessary but not sufficient tool for delivering relevant results and intelligent search experiences.
In this talk, we'll cover some of the emerging trends in AI-powered search, including the use of thought vectors (multi-level vector embeddings) and semantic knowledge graphs to contextually interpret and conceptualize queries. We'll walk through some live query interpretation demos to demonstrate the power that can be delivered through these semantic search techniques leveraging auto-generated knowledge graphs learned from your content and user interactions.
This document provides an overview of programming in Python for data science. It discusses Python's history and timeline, its versatile capabilities across different programming paradigms, and its simple and clear syntax. Key features that make Python popular for data science are highlighted, such as its comprehensive standard library and support for numeric, scientific, and GUI programming. The document also compares Python 2 and 3, describes different ways to run Python programs, and lists popular Python packages for data science. Overall, it serves as an introduction to Python for newcomers and outlines its relevance and widespread adoption in the field of data science.
Reflected Intelligence: Real world AI in Digital TransformationTrey Grainger
The goal of most digital transformations is to create competitive advantage by enhancing customer experience and employee success, so giving these stakeholders the ability to find the right information at their moment of need is paramount. Employees and customers increasingly expect an intuitive, interactive experience where they can simply type or speak their questions or keywords into a search box, their intent will be understood, and the best answers and content are then immediately presented.
Providing this compelling experience, however, requires a deep understanding of your content, your unique business domain, and the collective and personalized needs of each of your users. Modern artificial intelligence (AI) approaches are able to continuously learn from both your content and the ongoing stream of user interactions with your applications, and to automatically reflect back that learned intelligence in order to instantly and scalably deliver contextually-relevant answers to employees and customers.
In this talk, we'll discuss how AI is currently being deployed across the Fortune 1000 to accomplish these goals, both in the digital workplace (helping employees more efficiently get answers and make decisions) and in digital commerce (understanding customer intent and connecting them with the best information and products). We'll separate fact from fiction as we break down the hype around AI and show how it is being practically implemented today to power many real-world digital transformations for the next generation of employees and customers.
Moving Your Machine Learning Models to Production with TensorFlow ExtendedJonathan Mugan
TensorFlow Extended (TFX) is a platform for deploying and managing machine learning models in production. It represents a machine learning pipeline as a sequence of components that ingest data, validate data quality, transform features, train and evaluate models, and deploy models to a serving system. TFX uses TensorFlow and is open-sourced by Google. It provides tools to track metadata and metrics throughout the pipeline and helps keep models organized as they are updated and deployed over time in a production environment.
Key Lessons Learned Building Recommender Systems for Large-Scale Social Netw...Christian Posse
Invited Talk at KDD 2012 (Industry Practice Expo)
http://kdd2012.sigkdd.org/indexpo.shtml#posse
Abstract: By helping members to connect, discover and share relevant content or find a new career opportunity, recommender systems have become a critical component of user growth and engagement for social networks. The multidimensional nature of engagement and diversity of members on large-scale social networks have generated new infrastructure and modeling challenges and opportunities in the development, deployment and operation of recommender systems.
This presentation will address some of these issues, focusing on the modeling side for which new research is much needed while describing a recommendation platform that enables real-time recommendation updates at scale as well as batch computations, and cross-leverage between different product recommendations. Topics covered on the modeling side will include optimizing for multiple competing objectives, solving contradicting business goals, modeling user intent and interest to maximize placement and timeliness of the recommendations, utility metrics beyond CTR that leverage both real-time tracking of explicit and implicit user feedback, gathering training data for new product recommendations, virility preserving online testing and virtual profiling.
"Searching for Meaning: The Hidden Structure in Unstructured Data". Presentation by Trey Grainger at the Southern Data Science Conference (SDSC) 2018. Covers linguistic theory, application in search and information retrieval, and knowledge graph and ontology learning methods for automatically deriving contextualized meaning from unstructured (free text) content.
This document discusses various data skills needed for the digital era, including data science, business intelligence, big data, and data engineering. It provides overviews of these fields and lists important programming languages, tools, and skills for each, such as Python, R, SQL, Tableau, and Hadoop for data science; SQL, data warehousing, Tableau for business intelligence; Java, Python, Scala, Hadoop for big data; and Linux, NoSQL, Python, data ingestion tools for data engineering. It also recommends courses from universities like Michigan and Berkeley for gaining skills in these areas.
Natural Language Search with Knowledge Graphs (Activate 2019)Trey Grainger
The document discusses natural language search using knowledge graphs. It provides an overview of knowledge graphs and how they can help with natural language search. Specifically, it discusses how knowledge graphs can represent relationships and semantics in unstructured text. It also describes how semantic knowledge graphs are generated in Solr and how they can be used for tasks like query understanding, expansion and disambiguation.
In this talk, we introduce the Data Scientist role , differentiate investigative and operational analytics, and demonstrate a complete Data Science process using Python ecosystem tools, like IPython Notebook, Pandas, Matplotlib, NumPy, SciPy and Scikit-learn. We also touch the usage of Python in Big Data context, using Hadoop and Spark.
The amount of data available to us is growing rapidly, but what is required to make useful conclusions out of it?
Outline
1. Different tactics to gather your data
2. Cleansing, scrubbing, correcting your data
3. Running analysis for your data
4. Bring your data to live with visualizations
5. Publishing your data for rest of us as linked open data
This document provides an overview of machine learning tools and languages. It discusses Python, R, and MATLAB as the most commonly used tools. For each tool, it lists advantages and disadvantages. Python is highlighted as the number one language for machine learning due to its many libraries and large user community. R is best for time series analysis and causal inference. MATLAB is still a leading tool for signal processing but lacks machine learning libraries. The document also provides resources for learning machine learning foundations and examples.
Natural Language Search with Knowledge Graphs (Haystack 2019)Trey Grainger
To optimally interpret most natural language queries, it is necessary to understand the phrases, entities, commands, and relationships represented or implied within the search. Knowledge graphs serve as useful instantiations of ontologies which can help represent this kind of knowledge within a domain.
In this talk, we'll walk through techniques to build knowledge graphs automatically from your own domain-specific content, how you can update and edit the nodes and relationships, and how you can seamlessly integrate them into your search solution for enhanced query interpretation and semantic search. We'll have some fun with some of the more search-centric use cased of knowledge graphs, such as entity extraction, query expansion, disambiguation, and pattern identification within our queries: for example, transforming the query "bbq near haystack" into
{ filter:["doc_type":"restaurant"], "query": { "boost": { "b": "recip(geodist(38.034780,-78.486790),1,1000,1000)", "query": "bbq OR barbeque OR barbecue" } } }
We'll also specifically cover use of the Semantic Knowledge Graph, a particularly interesting knowledge graph implementation available within Apache Solr that can be auto-generated from your own domain-specific content and which provides highly-nuanced, contextual interpretation of all of the terms, phrases and entities within your domain. We'll see a live demo with real world data demonstrating how you can build and apply your own knowledge graphs to power much more relevant query understanding within your search engine.
This talk is a primer to Machine Learning. I will provide a brief introduction what is ML and how it works. I will walk you down the Machine Learning pipeline from data gathering, data normalizing and feature engineering, common supervised and unsupervised algorithms, training models, and delivering results to production. I will also provide recommendations to tools that help you provide the best ML experience, include programming languages and libraries.
If there is time at the end of the talk, I will walk through two coding examples, using the HMS Titanic Passenger List, present with Python scikit-learn using algorithm random-trees to check if ML can correctly predict passenger survival and with R programming for feature engineering of the same dataset
Note to data-scientists and programmers: If you sign up to attend, plan to visit my Github repository! I have many Machine Learning coding examples in Python scikit-learn, GNU Octave, and R Programming.
https://github.com/jefftune/gitw-2017-ml
Machine learning with Big Data power point presentationDavid Raj Kanthi
This is an article made form the articles of IEEE published in the year 2017
The following presentation has the slides for the Title called the
Machine Learning with Big data. that following presentation which has the challenges and approaches of machine learning with big data.
The integration of the Big Data with Machine Learning has so many challenges that Big data has and what is the approach made by the machine learning mechanism for those challenges.
Meetup sthlm - introduction to Machine Learning with demo casesZenodia Charpy
This document provides an agenda and overview of topics related to data science and machine learning. It discusses data science processes including data preparation, algorithm selection, model deployment, and performance measurement. It also distinguishes machine learning from artificial intelligence and describes common machine learning algorithms like supervised and unsupervised learning. Examples of supervised and unsupervised learning applications are presented along with generic workflows. Machine learning algorithm selection and example cases are also summarized.
The Next Generation of AI-powered SearchTrey Grainger
What does it really mean to deliver an "AI-powered Search" solution? In this talk, we’ll bring clarity to this topic, showing you how to marry the art of the possible with the real-world challenges involved in understanding your content, your users, and your domain. We'll dive into emerging trends in AI-powered Search, as well as many of the stumbling blocks found in even the most advanced AI and Search applications, showing how to proactively plan for and avoid them. We'll walk through the various uses of reflected intelligence and feedback loops for continuous learning from user behavioral signals and content updates, also covering the increasing importance of virtual assistants and personalized search use cases found within the intersection of traditional search and recommendation engines. Our goal will be to provide a baseline of mainstream AI-powered Search capabilities available today, and to paint a picture of what we can all expect just on the horizon.
This video will give you an idea about Data science for beginners.
Also explain Data Science Process , Data Science Job Roles , Stages in Data Science Project
Data Tactics Analytics Brown Bag (November 2013)Rich Heimann
This document summarizes a brown bag presentation on analytics by Data Tactics Corporation. It introduces new analytic tools from the company including work on cyber intelligence and detection, the open source RAccumulo library, and Data Science for Program Managers. Case studies on discontinuities analysis and data science in Afghanistan are also mentioned. The document concludes by discussing the Shiny tool for building interactive web apps in R and providing contact information.
Tutorial@BDA 2017 -- Knowledge Graph Expansion and Enrichment Paris Sud University
This document outlines techniques for expanding and enriching knowledge graphs through data linking, key discovery, and link invalidation. It begins with an introduction to linked open data and knowledge graphs. The technical part discusses approaches to data linking, including instance-based methods that consider attribute similarity and graph-based methods that propagate similarity across object properties. Supervised methods use training data while rule-based methods apply expert-defined rules. Evaluation measures effectiveness using recall, precision and F1-score, and efficiency. The document also covers similarity measures and techniques for knowledge graph expansion and enrichment.
1) The document discusses a self-study approach to learning data science through project-based learning using various online resources.
2) It recommends breaking down projects into 5 steps: defining problems/solutions, data extraction/preprocessing, exploration/engineering, model implementation, and evaluation.
3) Each step requires different skillsets from domains like statistics, programming, SQL, visualization, mathematics, and business knowledge.
Thought Vectors and Knowledge Graphs in AI-powered SearchTrey Grainger
While traditional keyword search is still useful, pure text-based keyword matching is quickly becoming obsolete; today, it is a necessary but not sufficient tool for delivering relevant results and intelligent search experiences.
In this talk, we'll cover some of the emerging trends in AI-powered search, including the use of thought vectors (multi-level vector embeddings) and semantic knowledge graphs to contextually interpret and conceptualize queries. We'll walk through some live query interpretation demos to demonstrate the power that can be delivered through these semantic search techniques leveraging auto-generated knowledge graphs learned from your content and user interactions.
This document provides an overview of programming in Python for data science. It discusses Python's history and timeline, its versatile capabilities across different programming paradigms, and its simple and clear syntax. Key features that make Python popular for data science are highlighted, such as its comprehensive standard library and support for numeric, scientific, and GUI programming. The document also compares Python 2 and 3, describes different ways to run Python programs, and lists popular Python packages for data science. Overall, it serves as an introduction to Python for newcomers and outlines its relevance and widespread adoption in the field of data science.
Reflected Intelligence: Real world AI in Digital TransformationTrey Grainger
The goal of most digital transformations is to create competitive advantage by enhancing customer experience and employee success, so giving these stakeholders the ability to find the right information at their moment of need is paramount. Employees and customers increasingly expect an intuitive, interactive experience where they can simply type or speak their questions or keywords into a search box, their intent will be understood, and the best answers and content are then immediately presented.
Providing this compelling experience, however, requires a deep understanding of your content, your unique business domain, and the collective and personalized needs of each of your users. Modern artificial intelligence (AI) approaches are able to continuously learn from both your content and the ongoing stream of user interactions with your applications, and to automatically reflect back that learned intelligence in order to instantly and scalably deliver contextually-relevant answers to employees and customers.
In this talk, we'll discuss how AI is currently being deployed across the Fortune 1000 to accomplish these goals, both in the digital workplace (helping employees more efficiently get answers and make decisions) and in digital commerce (understanding customer intent and connecting them with the best information and products). We'll separate fact from fiction as we break down the hype around AI and show how it is being practically implemented today to power many real-world digital transformations for the next generation of employees and customers.
Moving Your Machine Learning Models to Production with TensorFlow ExtendedJonathan Mugan
TensorFlow Extended (TFX) is a platform for deploying and managing machine learning models in production. It represents a machine learning pipeline as a sequence of components that ingest data, validate data quality, transform features, train and evaluate models, and deploy models to a serving system. TFX uses TensorFlow and is open-sourced by Google. It provides tools to track metadata and metrics throughout the pipeline and helps keep models organized as they are updated and deployed over time in a production environment.
Key Lessons Learned Building Recommender Systems for Large-Scale Social Netw...Christian Posse
Invited Talk at KDD 2012 (Industry Practice Expo)
http://kdd2012.sigkdd.org/indexpo.shtml#posse
Abstract: By helping members to connect, discover and share relevant content or find a new career opportunity, recommender systems have become a critical component of user growth and engagement for social networks. The multidimensional nature of engagement and diversity of members on large-scale social networks have generated new infrastructure and modeling challenges and opportunities in the development, deployment and operation of recommender systems.
This presentation will address some of these issues, focusing on the modeling side for which new research is much needed while describing a recommendation platform that enables real-time recommendation updates at scale as well as batch computations, and cross-leverage between different product recommendations. Topics covered on the modeling side will include optimizing for multiple competing objectives, solving contradicting business goals, modeling user intent and interest to maximize placement and timeliness of the recommendations, utility metrics beyond CTR that leverage both real-time tracking of explicit and implicit user feedback, gathering training data for new product recommendations, virility preserving online testing and virtual profiling.
"Searching for Meaning: The Hidden Structure in Unstructured Data". Presentation by Trey Grainger at the Southern Data Science Conference (SDSC) 2018. Covers linguistic theory, application in search and information retrieval, and knowledge graph and ontology learning methods for automatically deriving contextualized meaning from unstructured (free text) content.
This document discusses various data skills needed for the digital era, including data science, business intelligence, big data, and data engineering. It provides overviews of these fields and lists important programming languages, tools, and skills for each, such as Python, R, SQL, Tableau, and Hadoop for data science; SQL, data warehousing, Tableau for business intelligence; Java, Python, Scala, Hadoop for big data; and Linux, NoSQL, Python, data ingestion tools for data engineering. It also recommends courses from universities like Michigan and Berkeley for gaining skills in these areas.
Natural Language Search with Knowledge Graphs (Activate 2019)Trey Grainger
The document discusses natural language search using knowledge graphs. It provides an overview of knowledge graphs and how they can help with natural language search. Specifically, it discusses how knowledge graphs can represent relationships and semantics in unstructured text. It also describes how semantic knowledge graphs are generated in Solr and how they can be used for tasks like query understanding, expansion and disambiguation.
In this talk, we introduce the Data Scientist role , differentiate investigative and operational analytics, and demonstrate a complete Data Science process using Python ecosystem tools, like IPython Notebook, Pandas, Matplotlib, NumPy, SciPy and Scikit-learn. We also touch the usage of Python in Big Data context, using Hadoop and Spark.
The amount of data available to us is growing rapidly, but what is required to make useful conclusions out of it?
Outline
1. Different tactics to gather your data
2. Cleansing, scrubbing, correcting your data
3. Running analysis for your data
4. Bring your data to live with visualizations
5. Publishing your data for rest of us as linked open data
This document provides an introduction and overview of resources for learning Python for data science. It introduces the presenter, Karlijn Willems, a data science journalist who has worked as a big data developer. It then lists several useful links for learning Python, statistics, machine learning, databases, and data science tools like Apache Spark. Finally, it recommends people to follow in data science and analytics fields.
Discover why Python is better for Data Science: the whole workflow of Data Analysis is covered by Python. Tools for various tasks are shown, including: workflow, data analysis, data visualization, integration with Hadoop ecosystem, and communication.
In this presentation its given an introduction about Data Science, Data Scientist role and features, and how Python ecosystem provides great tools for Data Science process (Obtain, Scrub, Explore, Model, Interpret).
For that, an attached IPython Notebook ( http://bit.ly/python4datascience_nb ) exemplifies the full process of a corporate network analysis, using Pandas, Matplotlib, Scikit-learn, Numpy and Scipy.
Discovering User's Topics of Interest in Recommender SystemsGabriel Moreira
This talk introduces the main techniques of Recommender Systems and Topic Modeling.
Then, we present a case of how we've combined those techniques to build Smart Canvas (www.smartcanvas.com), a service that allows people to bring, create and curate content relevant to their organization, and also helps to tear down knowledge silos.
We present some of Smart Canvas features powered by its recommender system, such as:
- Highlight relevant content, explaining to the users which of his topics of interest have generated each recommendation.
- Associate tags to users’ profiles based on topics discovered from content they have contributed. These tags become searchable, allowing users to find experts or people with specific interests.
- Recommends people with similar interests, explaining which topics brings them together.
We give a deep dive into the design of our large-scale recommendation algorithms, giving special attention to our content-based approach that uses topic modeling techniques (like LDA and NMF) to discover people’s topics of interest from unstructured text, and social-based algorithms using a graph database connecting content, people and teams around topics.
Our typical data pipeline that includes the ingestion millions of user events (using Google PubSub and BigQuery), the batch processing of the models (with PySpark, MLib, and Scikit-learn), the online recommendations (with Google App Engine, Titan Graph Database and Elasticsearch), and the data-driven evaluation of UX and algorithms through A/B testing experimentation. We also touch topics about non-functional requirements of a software-as-a-service like scalability, performance, availability, reliability and multi-tenancy and how we addressed it in a robust architecture deployed on Google Cloud Platform.
The document describes different approaches for predicting the percentage of agricultural land irrigated in Indian villages. It tests random forest, clustering, bagging, boosting, linear regression, lasso, ridge and principal component analysis models. Bagging outperforms other models with the lowest RMSE and highest lift. The most important predictive features are found to be power supply, electricity access and education levels. The model could help governments forecast irrigation needs and support farmers' planning.
Learning lines for geoSpatial thinking: GI Learner ProjectKarl Donert
Almost all aspects of our economy and society are based on geoinformation and geotechnologies. People are tracking, mapping and communicating geographically on an unprecedented scale. Citizens can be empowered by geospatial technologies and open geodata. The sector is booming, however there has been a clear mismatch between workforce demand and supply. Study programmes focus more on informatics than on the scientific background of spatial thinking.
This presentation seeks to introduce a newly EU funded project titled, GI-Learner: Developing a learning line on GIScience in school education. This project aims to support the introduction of GI Science in secondary (high school) education, by addressing policy developments and deliver materials with the capacity and capability to raise awareness of the GI sector, create a geospatially literate workforce and citizens who can benefit from these developments.
This document outlines a data competition to visualize deals based on revenue versus various metrics. It describes cleaning the data by removing missing data and normalizing state names. Then it lists the sections that will analyze revenue compared to Yelp categories, state, rating, price, website, duration of deal, and a multiplication of rating and number of reviews. Each section will provide a visualization of revenue compared to the specified metric.
This presentation is all about various built in
datastructures which we have in python.
List
Dictionary
Tuple
Set
and various methods present in each data structure
Introduction to Python Language and Data TypesRavi Shankar
This document provides information about the Python programming language. It discusses that Python was invented in the 1990s in the Netherlands by Guido van Rossum and was named after Monty Python. It is an open source, general-purpose, interpreted programming language that is widely used. The document then covers various Python implementations, popular Python editors and IDEs, tips for getting started with Python, basic syntax, data types, operators, and lists.
Modak Analytics provides predictive modeling solutions to help companies analyze customer data and make reliable decisions. Predictive modeling involves [1] analyzing piled up customer data to derive useful insights, [2] designing a predictive model using various techniques like clustering, decision trees, regression, and scorecards, and [3] implementing the model to better understand customers and make profitable decisions. Predictive analysis allows companies to segment markets, rank products, predict customer responses, and reduce fraud. Modak Analytics' customized solutions leverage different modeling techniques to create ensemble models that extract the strengths of each technique.
Smart Canvas is a machine learning platform that delivers personalized recommendations for web and mobile content using a hybrid recommender system. It analyzes user interactions and ingests content from various sources to provide recommendations using algorithms like collaborative filtering, content-based filtering, and popularity rankings. The system is evaluated using metrics like nDCG, CTR, coverage, and user engagement to analyze recommendation quality and make improvements.
The Pandas library provides easy-to-use data structures and analysis tools for Python. It uses NumPy and allows import of data into Series (one-dimensional arrays) and DataFrames (two-dimensional labeled data structures). Data can be accessed, filtered, and manipulated using indexing, booleans, and arithmetic operations. Pandas supports reading and writing data to common formats like CSV, Excel, SQL, and can help with data cleaning, manipulation, and analysis tasks.
Kathleen Breitman at the Hyperledger Meetup Altoros
Distributed ledgers offer advantages for capital markets by providing a single record of transactions maintained across multiple nodes, avoiding political battles over control. They could help reconcile complex transactions recorded across old, fragile software systems in bank back offices. A distributed ledger maintained between different financial players could more easily track a swap sold and resold between custodians than traditional systems. Potential use cases involve a lack of central authority, robust regulatory needs, and transactional processes followable as smart contracts. Examples include repo agreements, FX settlement, and trade reconciliations.
This document discusses containers and their history in Cloud Foundry. It describes the evolution from Warden to Garden container managers, and Garden's modular architecture and Linux-based backends like Aufs and Docker. It also mentions other backends for Garden like Greenhouse (Windows) and Guardian (supporting additional technologies like Docker, LXC, etc). Finally, it discusses the Open Containers Initiative standards and provides some debugging tips.
Kickstart your data science journey with this Python cheat sheet that contains code examples for strings, lists, importing libraries and NumPy arrays.
Find more cheat sheets and learn data science with Python at www.datacamp.com.
Secrets of Enterprise Data Mining: SQL Saturday 328 Birmingham ALMark Tabladillo
This document discusses secrets of enterprise data mining. It begins by defining data mining as the automated or semi-automated process of discovering patterns in data. It then discusses how data mining can be applied in various industries like telecommunications, oil and gas, and Volkswagen Group. Finally, it discusses how Microsoft offers solutions for enterprise data mining through SQL Server Analysis Services and Microsoft Azure Machine Learning.
TechEvent 2019: Artificial Intelligence in Dev & Ops; Martin Luckow - TrivadisTrivadis
This document provides an overview of artificial intelligence trends and applications in development and operations. It discusses how AI is being used for rapid prototyping, intelligent programming assistants, automatic error handling and code refactoring, and strategic decision making. Examples are given of AI tools from Microsoft, Facebook, and Codota. The document also discusses challenges like interpretability of neural networks and outlines a vision of "Software 2.0" where programs are generated automatically to satisfy goals. It emphasizes that AI will transform software development over the next 10 years.
Building Enterprise Mashups - Web 2.0 conferencemogrinz
The document discusses enterprise mashups, which allow organizations to quickly combine internal and external data sources to build applications. It notes that the enterprise mashup market is growing and will be worth $1.74 billion by 2013. The document outlines some key capabilities of enterprise mashup platforms like data extraction, visualization, and publishing completed mashups. It argues that mashups can improve flexibility for users while maintaining reliability for IT departments.
How to Leverage Machine Learning (R, Hadoop, Spark, H2O) for Real Time Proces...Codemotion
Big Data is key for innovation in many industries today. Large amounts of historical data are stored and analyzed in Hadoop, Spark or other clusters to find patterns, e.g. for predictive maintenance or cross-selling. However: How do you increase revenue or reduce risks in new transactions proactively? Stream processing is the solution to embed patterns into future actions in real-time. This session discusses and demos how machine learning and analytic models with R, Spark MLlib, H2O, etc. can be build and integrated into real-time event processing frameworks. The session focuses on live demos
R, Spark, Tensorflow, H20.ai Applied to Streaming AnalyticsKai Wähner
Slides from my talk at Codemotion Rome in March 2017. Development of analytic machine learning / deep learning models with R, Apache Spark ML, Tensorflow, H2O.ai, RapidMinder, KNIME and TIBCO Spotfire. Deployment to real time event processing / stream processing / streaming analytics engines like Apache Spark Streaming, Apache Flink, Kafka Streams, TIBCO StreamBase.
How to Leverage Machine Learning (R, Hadoop, Spark, H2O) for Real Time Proces...Codemotion Tel Aviv
The document discusses applying machine learning models to real-time data streams. It covers building analytic models from historical data using tools like R, TensorFlow and Hadoop. It then discusses applying these pre-built models to real-time streaming data using frameworks like Apache Spark, TIBCO StreamBase and H2O to power applications like predictive maintenance and manufacturing analytics. The key takeaway is that machine learning on historical data finds insights, while stream processing applies these models in real-time to drive closed-loop analytics and enable real-time action.
Build Machine Learning Models with Amazon SageMaker (April 2019)Julien SIMON
The document discusses Amazon SageMaker, a fully managed machine learning platform. It describes how SageMaker allows users to build, train, and deploy machine learning models at scale. Key features include pre-built algorithms and notebooks, tools for data labeling and preparation, one-click training and tuning of models, and deployment of trained models into production. The document also provides examples of using SageMaker for tasks like image classification and text analysis.
Keynote presentation from ECBS conference. The talk is about how to use machine learning and AI in improving software engineering. Experiences from our project in Software Center (www.software-center.se).
Azure Functions, AI & Xamarin - How to use the Cloud to Your AdvantageMark Arteaga
With the fast pace that technology is moving these days you might see terms such as AI, machine learning, and serverless computing. Ever wonder how you can use these in your development projects?
During this session we will cover high level ‘What is ‘serverless’ computing,’ see how to leverage Azure functions with Xamarin, and inject some AI into our functions all initiated from an iOS and Android app. We will work through a sample application built using Xamarin Forms and slowly add features to make our app better!
If this interests you make sure to sign up and join us! At the end of this presentation, you’ll walk away with ideas on how to leverage these terms in the headlines and see it’s easy to use them for your advantage!
Documenting serverless architectures could we do it better - o'reily sa con...Asher Sterkin
The document discusses documenting serverless architectures. It introduces serverless architecture and some of its benefits and challenges, including the lack of clear guidelines around choosing different serverless computing options. It proposes using several views - use case view, logical view, process view, implementation view, and deployment view - based on the 4+1 architectural view model to document serverless architectures. Examples of using sequence diagrams and collaboration diagrams for the logical view and process view are provided to illustrate how different views can capture various aspects of the system architecture.
The document proposes a solution called Project: PACENAME to address issues with Internet search engines returning unusable or irrelevant data. PACENAME would involve defining catalogs of data and their properties, including keywords for artificial intelligence pattern matching. A search bot would compare keywords to catalogs stored in an XML database. The artificial intelligence would then decide which catalog(s) to search within to return more useful results to the user.
Power BI dataflows と Power Platform Data Integration の使いどころYugo Shimizu
Microsoft Ignite The Tour Tokyo と Osaka でお話した同タイトルのセッション資料です
BRK30034 @ Tokyo 2019/12/06 15:15 - 16:00 JST
BRK30156 @ Osaka 2020/01/24 12:15 - 13:00 JST
セッション概要:
Power BI dataflows と Power Platform dataflows は両方とも Power Query でデータを集め、使いたい形に変えることが可能ですが、その用途は使い分けるべきです。(=Data Preparation, ETL)
似たような機能がなぜそれぞれに存在するのか?それは目的が異なるからです。本セッションでは、シナリオベースでそれぞれの使い方をご紹介します。
This document discusses Randall Hunt's Twitter bot @WhereML, which uses Amazon SageMaker and AWS Lambda to determine the location from photos tweeted at the bot. It was built using the LocationNet model trained on over 33 million geo-tagged images. The architecture uses API Gateway to invoke a Lambda function when tweets are sent to @WhereML. The Lambda function calls a SageMaker inference endpoint running the LocationNet model to classify the image location, then posts the results back to Twitter. Details are provided on the model architecture, infrastructure components, and code snippets from the Lambda function.
Predictive analytics is touching more and more lives every day. Machine Learning lets you predict and change the future. Do you know that Microsoft products like Xbox and Bing integrate some machine learning capabilities in their workflows? Come to the session and take a look of the new cloud-based machine learning platform called AzureML from a BI architect perspective, without all the data scientist knowledge.
Data Science as a Service: Intersection of Cloud Computing and Data SciencePouria Amirian
Dr. Pouria Amirian from the University of Oxford explains Data Science and its relationship with Big Data and Cloud Computing. Then he illustrates using AzureML to perform a simple data science analytics.
Data Science as a Service: Intersection of Cloud Computing and Data SciencePouria Amirian
Dr. Pouria Amirian explains data science, steps in a data science workflow and show some experiments in AzureML. He also mentions about big data issues in a data science project and solutions to them.
1. The document discusses how to repeatedly translate models into business value through interventions and experiments.
2. It identifies key stage-transition tasks (STTs) in the process from defining problems to testing models and proposes tools to standardize, automate, and increase collaboration at each stage.
3. The goal is to increase the "velocity of the vortex" by speeding up the cycle from data to models to experiments and back to improve models.
ChatGPT and Beyond - Elevating DevOps ProductivityVictorSzoltysek
In the dynamic field of DevOps, the quest for efficiency and productivity is endless. This talk introduces a revolutionary toolkit: Large Language Models (LLMs), including ChatGPT, Gemini, and Claude, extending far beyond traditional coding assistance. We'll explore how LLMs can automate not just code generation, but also transform day-to-day operations such as crafting compelling cover letters for TPS reports, streamlining client communications, and architecting innovative DevOps solutions. Attendees will learn effective prompting strategies and examine real-life use cases, demonstrating LLMs' potential to redefine productivity in the DevOps landscape. Join us to discover how to harness the power of LLMs for a comprehensive productivity boost across your DevOps activities.
Learning Azure Synapse Analytics (Third Early Release) Paul Andrewalabodzeema
Learning Azure Synapse Analytics (Third Early Release) Paul Andrew
Learning Azure Synapse Analytics (Third Early Release) Paul Andrew
Learning Azure Synapse Analytics (Third Early Release) Paul Andrew
[Phd Thesis Defense] CHAMELEON: A Deep Learning Meta-Architecture for News Re...Gabriel Moreira
Presentation of the Phd. thesis defense of Gabriel de Souza Pereira Moreira at Instituto Tecnológico de Aeronáutica (ITA), on Dec. 09, 2019, in São José dos Campos, Brazil.
Abstract:
Recommender systems have been increasingly popular in assisting users with their choices, thus enhancing their engagement and overall satisfaction with online services. Since the last decade, recommender systems became a topic of increasing interest among machine learning, human-computer interaction, and information retrieval researchers.
News recommender systems are aimed to personalize users experiences and help them discover relevant articles from a large and dynamic search space. Therefore, it is a challenging scenario for recommendations. Large publishers release hundreds of news daily, implying that they must deal with fast-growing numbers of items that get quickly outdated and irrelevant to most readers. News readers exhibit more unstable consumption behavior than users in other domains such as entertainment. External events, like breaking news, affect readers interests. In addition, the news domain experiences extreme levels of sparsity, as most users are anonymous, with no past behavior tracked.
Since 2016, Deep Learning methods and techniques have been explored in Recommender Systems research. In general, they can be divided into methods for: Deep Collaborative Filtering, Learning Item Embeddings, Session-based Recommendations using Recurrent Neural Networks (RNN), and Feature Extraction from Items' Unstructured Data such as text, images, audio, and video.
The main contribution of this research was named CHAMELEON a meta-architecture designed to tackle the specific challenges of news recommendation. It consists of a modular reference architecture which can be instantiated using different neural building blocks.
As information about users' past interactions is scarce in the news domain, information such as the user context (e.g., time, location, device, the sequence of clicks within the session), static and dynamic article features like the article textual content and its popularity and recency, are explicitly modeled in a hybrid session-based recommendation approach using RNNs.
The recommendation task addressed in this work is the next-item prediction for user sessions, i.e., "what is the next most likely article a user might read in a session?". A temporal offline evaluation is used for a realistic offline evaluation of such task, considering factors that affect global readership interests like popularity, recency, and seasonality.
Experiments performed with two large datasets have shown the effectiveness of the CHAMELEON for news recommendation on many quality factors such as accuracy, item coverage, novelty, and reduced item cold-start problem, when compared to other traditional and state-of-the-art session-based algorithms.
PAPIs LATAM 2019 - Training and deploying ML models with Kubeflow and TensorF...Gabriel Moreira
The document discusses training and deploying machine learning models with Kubeflow and TensorFlow Extended (TFX). It provides an overview of Kubeflow as a platform for building ML products using containers and Kubernetes. It then describes key TFX components like TensorFlow Data Validation (TFDV) for data exploration and validation, TensorFlow Transform (TFT) for preprocessing, and TensorFlow Estimators for training and evaluation. The document demonstrates these components in a Kubeflow pipeline for a session-based news recommender system, covering data validation, transformation, training, and deployment.
Deep Learning for Recommender Systems @ TDC SP 2019Gabriel Moreira
This document provides an overview of deep learning for recommender systems. It discusses how deep learning can be used to extract features from content like text, images, and audio for recommendations. It also describes how deep learning models like convolutional and recurrent neural networks can learn complex representations of users and items for collaborative filtering. The document then presents CHAMELEON, a meta-architecture for news recommendations that uses different deep learning techniques for tasks like article embedding, metadata prediction, and next-article recommendation. It evaluates CHAMELEON on a real-world news dataset and finds it outperforms other baseline methods on metrics like hit rate and mean reciprocal rank.
PAPIs LATAM 2019 - Training and deploying ML models with Kubeflow and TensorF...Gabriel Moreira
For real-world ML systems, it is crucial to have scalable and flexible platforms to build ML workflows. In this workshop, we will demonstrate how to build an ML DevOps pipeline using Kubeflow and TensorFlow Extended (TFX). Kubeflow is a flexible environment to implement ML workflows on top of Kubernetes - an open-source platform for managing containerized workloads and services, which can be deployed either on-premises or on a Cloud platform. TFX has a special integration with Kubeflow and provides tools for data pre-processing, model training, evaluation, deployment, and monitoring.
In this workshop, we will demonstrate a pipeline for training and deploying an RNN-based Recommender System model using Kubeflow.
https://papislatam2019.sched.com/event/OV1M/training-and-deploying-ml-models-with-kubeflow-and-tensorflow-extended-tfx-sponsored-by-cit
Nesta palestra no evento GDG DataFest, apresentei uma introdução prática sobre as principais técnicas de sistemas de recomendação, incluindo arquiteturas recentes baseadas em Deep Learning. Foram apresentados exemplos utilizando Python, TensorFlow e Google ML Engine, e fornecidos datasets para exercitarmos um cenário de recomendação de artigos e notícias.
CI&T Tech Summit 2017 - Machine Learning para Sistemas de RecomendaçãoGabriel Moreira
Este documento discute sistemas de recomendação, apresentando dois tipos principais: filtragem colaborativa e filtragem baseada em conteúdo. A filtragem colaborativa faz recomendações baseadas na similaridade entre usuários, enquanto a filtragem baseada em conteúdo analisa os atributos dos itens para fazer recomendações. O documento também fornece exemplos de como implementar esses sistemas usando ferramentas como Mahout e scikit-learn.
Feature Engineering - Getting most out of data for predictive models - TDC 2017Gabriel Moreira
How should data be preprocessed for use in machine learning algorithms? How to identify the most predictive attributes of a dataset? What features can generate to improve the accuracy of a model?
Feature Engineering is the process of extracting and selecting, from raw data, features that can be used effectively in predictive models. As the quality of the features greatly influences the quality of the results, knowing the main techniques and pitfalls will help you to succeed in the use of machine learning in your projects.
In this talk, we will present methods and techniques that allow us to extract the maximum potential of the features of a dataset, increasing flexibility, simplicity and accuracy of the models. The analysis of the distribution of features and their correlations, the transformation of numeric attributes (such as scaling, normalization, log-based transformation, binning), categorical attributes (such as one-hot encoding, feature hashing, Temporal (date / time), and free-text attributes (text vectorization, topic modeling).
Python, Python, Scikit-learn, and Spark SQL examples will be presented and how to use domain knowledge and intuition to select and generate features relevant to predictive models.
Feature Engineering - Getting most out of data for predictive modelsGabriel Moreira
How should data be preprocessed for use in machine learning algorithms? How to identify the most predictive attributes of a dataset? What features can generate to improve the accuracy of a model?
Feature Engineering is the process of extracting and selecting, from raw data, features that can be used effectively in predictive models. As the quality of the features greatly influences the quality of the results, knowing the main techniques and pitfalls will help you to succeed in the use of machine learning in your projects.
In this talk, we will present methods and techniques that allow us to extract the maximum potential of the features of a dataset, increasing flexibility, simplicity and accuracy of the models. The analysis of the distribution of features and their correlations, the transformation of numeric attributes (such as scaling, normalization, log-based transformation, binning), categorical attributes (such as one-hot encoding, feature hashing, Temporal (date / time), and free-text attributes (text vectorization, topic modeling).
Python, Python, Scikit-learn, and Spark SQL examples will be presented and how to use domain knowledge and intuition to select and generate features relevant to predictive models.
Discovering User's Topics of Interest in Recommender Systems @ Meetup Machine...Gabriel Moreira
This talk introduces the main techniques of Recommender Systems and Topic Modeling. Then, we present a case of how we've combined those techniques to build Smart Canvas, a SaaS that allows people to bring, create and curate content relevant to their organization, and also helps to tear down knowledge silos.
We give a deep dive into the design of our large-scale recommendation algorithms, giving special attention to a content-based approach that uses topic modeling techniques (like LDA and NMF) to discover people’s topics of interest from unstructured text, and social-based algorithms using a graph database connecting content, people and teams around topics.
Our typical data pipeline that includes the ingestion millions of user events (using Google PubSub and BigQuery), the batch processing of the models (with PySpark, MLib, and Scikit-learn), the online recommendations (with Google App Engine, Titan Graph Database and Elasticsearch), and the data-driven evaluation of UX and algorithms through A/B testing experimentation. We also touch topics about non-functional requirements of a software-as-a-service like scalability, performance, availability, reliability and multi-tenancy and how we addressed it in a robust architecture deployed on Google Cloud Platform.
Short-Bio: Gabriel Moreira is a scientist passionate about solving problems with data. He is Head of Machine Learning at CI&T and Doctoral student at Instituto Tecnológico de Aeronáutica - ITA. where he has also got his Masters on Science. His current research interests are recommender systems and deep learning.
https://www.meetup.com/pt-BR/machine-learning-big-data-engenharia/events/239037949/
Using Neural Networks and 3D sensors data to model LIBRAS gestures recognitio...Gabriel Moreira
Paper entitled "Using Neural Networks and 3D sensors data to model LIBRAS gestures recognition", presented at II Symposium on Knowledge Discovery, Mining and Learning – KDMILE, USP, São Carlos, SP, Brazil.
Developing GeoGames for Education with Kinect and Android for ArcGIS RuntimeGabriel Moreira
This presentation is about Where Is That, a game developed for geography and history education. There are two versions, one for Android, available on Google Play, and the other for Windows.
O documento discute um encontro de programação onde desenvolvedores trabalham juntos em desafios. Eles se reúnem para se divertir e melhorar suas habilidades em programação e trabalho em equipe através de uma metodologia pragmática. O documento também descreve um projeto de um jogo Tic-Tac-Toe para Android com diferentes histórias de usuário.
O documento apresenta uma introdução sobre testes ágeis, com foco em valores, tipos de teste e exemplos de user stories e critérios de aceitação. Os palestrantes discutem como implementar testes no desenvolvimento ágil de software, incluindo TDD, e fornecem referências sobre o tema.
The document discusses the ArcGIS Runtime for Android SDK, including that version 1.0 was released in December 2011 and version 2.0 is scheduled for summer, and it provides an overview of dependencies, supported Android platforms, environment setup, map layer types, and demos of editing and offline functionality. Samples and documentation are available on Esri's website and developer forums.
EARLY-FIX: Um Framework para Predição de Manutenção Corretiva de Software uti...Gabriel Moreira
Este documento apresenta o framework EARLY-FIX para predição de manutenção corretiva de software utilizando métricas de produto. O framework inclui modelos conceituais para indicadores de volume e predição de volume, métodos para medição de produto, histórico de manutenção e calibração de modelos preditivos, e técnicas para detecção de módulos propensos a defeitos. O framework foi implementado e testado em dois projetos da indústria para validar sua aplicabilidade.
Continuous Inspection - An effective approch towards Software Quality Product...Gabriel Moreira
O documento discute a abordagem de inspeção contínua para melhoria contínua da qualidade de software. A inspeção contínua envolve análise estática de código como parte do processo de integração contínua para identificar problemas de qualidade como complexidade condicional, código duplicado, métodos longos e dependências excessivas. As métricas e cheiros ruins identificados são então refatorados para manter a qualidade do código ao longo do tempo.
An Investigation Of EXtreme Programming PracticesGabriel Moreira
Paper presented in Workshop Brasileiro de Métodos Ágeis (WBMA) na AgileBrazil 2011
Abstract: This work presents an investigation of three different industrial
projects of software development by a Brazilian enterprise. During projects’
execution, the company has changed its approach on software processes from
RUP based process to agile like processes. To assess software product quality
metrics evolution, an investigation of product metrics history was conducted in
those three projects. This paper characterizes the use of eXtreme
Programming practices within the analyzed projects and the observed
measures of quality metrics in the developed software products.
METACOM – Uma análise de correlação entre métricas de produto e propensão à m...Gabriel Moreira
Artigo apresentado no SBQS 2011 - Simpósio Brasileiro de Métodos Ágeis, em Curitiba no dia 08/06/2011.
Abstract: Considerando-se que as características de qualidade de um software
influenciam no esforço de sua manutenção, este artigo apresenta um Método
para Análise de Correlação entre Métricas de Produto de Software e
Propensão à Manutenção denominado METACOM. O método proposto define
um processo de extração, transformação e carga de métricas de software
orientado a objetos e de volume de manutenções. O METACOM é composto
por um modelo de análise de correlação entre as medidas obtidas, visando
identificar métricas de produto mais preditivas. Descrevem-se também a
aplicação do METACOM na análise de projetos reais da indústria de software
e as considerações de especialistas sobre os principais resultados.
Software Product Measurement and Analysis in a Continuous Integration Environ...Gabriel Moreira
Presentation of a paper presented in the International Conference ITNG 2010, about a framework constructed for software internal quality measurement program with automatic metrics extraction, implemented at a Software Factory.
This project demonstrates the application of machine learning—specifically K-Means Clustering—to segment customers based on behavioral and demographic data. The objective is to identify distinct customer groups to enable targeted marketing strategies and personalized customer engagement.
The presentation walks through:
Data preprocessing and exploratory data analysis (EDA)
Feature scaling and dimensionality reduction
K-Means clustering and silhouette analysis
Insights and business recommendations from each customer segment
This work showcases practical data science skills applied to a real-world business problem, using Python and visualization tools to generate actionable insights for decision-makers.
How to regulate and control your it-outsourcing provider with process miningProcess mining Evangelist
Oliver Wildenstein is an IT process manager at MLP. As in many other IT departments, he works together with external companies who perform supporting IT processes for his organization. With process mining he found a way to monitor these outsourcing providers.
Rather than having to believe the self-reports from the provider, process mining gives him a controlling mechanism for the outsourced process. Because such analyses are usually not foreseen in the initial outsourcing contract, companies often have to pay extra to get access to the data for their own process.
Just-in-time: Repetitive production system in which processing and movement of materials and goods occur just as they are needed, usually in small batches
JIT is characteristic of lean production systems
JIT operates with very little “fat”
Bram Vanschoenwinkel is a Business Architect at AE. Bram first heard about process mining in 2008 or 2009, when he was searching for new techniques with a quantitative approach to process analysis. By now he has completed several projects in payroll accounting, public administration, and postal services.
The discovered AS IS process models are based on facts rather than opinions and, therefore, serve as the ideal starting point for change. Bram uses process mining not as a standalone technique but complementary and in combination with other techniques to focus on what is really important: Actually improving the process.
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Johan Lammers from Statistics Netherlands has been a business analyst and statistical researcher for almost 30 years. In their business, processes have two faces: You can produce statistics about processes and processes are needed to produce statistics. As a government-funded office, the efficiency and the effectiveness of their processes is important to spend that public money well.
Johan takes us on a journey of how official statistics are made. One way to study dynamics in statistics is to take snapshots of data over time. A special way is the panel survey, where a group of cases is followed over time. He shows how process mining could test certain hypotheses much faster compared to statistical tools like SPSS.
12. INQUIRE
1. Which communities are more popular?
2. Is the user engagement increasing?
3. What is the distribution of user interactions?
4. Is there a relationship between publishing hour
and number of interactions?
14. OBTAIN
•Download data from another location (e.g., a web
page or server)
•Query data from a database (e.g., MySQL or Oracle)
•Extract data from an API (e.g.,Twitter, Facebook)
•Extract data from another file (e.g., an HTML file or
spreadsheet)
•Generate data yourself (e.g., reading sensors or
taking surveys)
28. 3 - HOW ISTHE DISTRIBUTION OF USER INTERACTIONS?
29. 3 - HOW ISTHE DISTRIBUTION OF USER INTERACTIONS?
30. 3 - HOW ISTHE DISTRIBUTION OF USER INTERACTIONS?
31. 4 - RELATIONSHIP BETWEEN PUBLISHINGTIME AND
NUMBER OF INTERACTIONS?
32. 4 - RELATIONSHIP BETWEEN PUBLISHINGTIME AND
NUMBER OF INTERACTIONS?
33. 4 - RELATIONSHIP BETWEEN PUBLISHINGTIME AND
NUMBER OF INTERACTIONS?
34. 4 - RELATIONSHIP BETWEEN PUBLISHINGTIME AND
NUMBER OF INTERACTIONS?
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45. 2 - SIMILAR POSTS
Original Post
Did you ever wonder how great it would be if you could write your jmeter
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architecture validation | academia de arquitetura
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Foram disponibilizados no site Enterprise Architecture, na parte de
Knowledge Base de performance, alguns how-tos relacionados a testes de
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