Software Analytics with Jupyter, Pandas, jQAssistant, and Neo4j [Neo4j Online Meetup]

- Davide Mottin is an assistant professor in the Department of Computer Science at Aarhus University who researches graph mining. - His talk discusses unveiling knowledge in knowledge graphs through personalized summarization techniques. Knowledge graphs contain entities and relationships between them. - He describes an approach for generating personalized summaries of a knowledge graph based on a user's query history. The algorithm aims to find a subgraph that maximizes the probability of answering future queries, subject to a size limit.

I gave this presentation at DataOps 19 in Barcelona. You will find information about Neo4j and how to use it with Graph Algorithms for Machine Learning and Artificial Intelligence.

This document discusses graphs and graph databases. It provides examples of graphs and compares SQL queries to Gremlin queries on graphs. It also discusses different types of graph databases for online transaction processing (OLTP) and online analytical processing (OLAP). The document then discusses how a social and data graph could help address the problem of data going dark in life sciences research by enabling collaboration, data sharing and discovery of relevant experts and data. It proposes using bi-clustering algorithms to identify relevant groups within the social and data graph to facilitate data and expert discovery.

This document provides an introduction to graphs and Neo4j. It discusses that Neo4j is a native graph database that allows organizations to leverage connections in data in real-time to create value. It then provides information on Neo4j as a company and as a product, including that it is the world's leading graph database. The document goes on to define what graphs are from a data structure perspective and provides examples of famous graphs like social networks. It discusses why graph databases are useful compared to relational databases for representing complex, connected data and provides examples of use cases for Neo4j like recommendations, fraud detection, and network analysis.

Noel Yuhanna, VP, Principal Analyst, Forrester Mary Barton, Consultant, Forrester Blaise James, Analyst Relations, Neo4j

Data is both our most valuable asset and our biggest ongoing challenge. As data grows in volume, variety and complexity, across applications, clouds and siloed systems, traditional ways of working with data no longer work. Unlike traditional databases, which arrange data in rows, columns and tables, Neo4j has a flexible structure defined by stored relationships between data records. We'll discuss the primary use cases for graph databases Explore the properties of Neo4j that make those use cases possible Look into the visualisation of graphs Introduce how to write queries. Webinar, 23 July 2020

This document provides an overview of the Neo4j Graph Platform vision, including existing and upcoming products. It discusses Neo4j's long-term vision of being a graph platform beyond just a database, including tools for development and administration, analytics, and integrations. It also highlights some key existing products like the Neo4j browser and algorithms library, as well as upcoming capabilities like analytics integrations and better visibility of partner software.

The document discusses graph data science and Neo4j's Graph Data Science (GDS) framework. GDS allows running graph algorithms and machine learning models at scale on large graph datasets. It discusses key aspects of GDS including architecture, data import, algorithm selection, and case studies of customers using GDS on graphs with billions of nodes and relationships. GDS runs on dedicated instances and supports features like enterprise graph compression, unlimited parallelization, and named graphs to optimize performance on large datasets.

Neo4j GraphTour Europe 2019: Neo4j Graph Use Cases, Bruno Ungermann, Sales Director Germany & Switzerland, Neo4j

The document discusses how graph data science can accelerate AI and machine learning by leveraging relationships between data, which traditional approaches often ignore. It describes Neo4j's graph database and graph data science platform that allows users to perform queries, machine learning, and visualization on graph data to gain insights. Neo4j's graph data science library provides algorithms, embeddings, and in-graph machine learning models to make predictions that incorporate a graph's structural relationships.

The document discusses graph databases, Neo4j graph database software, and graph data science algorithms. It provides an overview of graph databases and their components like nodes, edges, and properties. It then describes Neo4j's features including querying, visualization, hosting options, and the Graph Data Science library. Finally, it explains different types of graph data science algorithms in Neo4j like centrality, similarity, and pathfinding algorithms and provides an example of each.

The document discusses two use cases for graph technologies and analytics solutions: (1) bill of material and data quality control, and (2) online shopping assistant. For the first use case, a graph database is used to model bill of materials data and rules to detect inconsistencies and prioritize data cleansing. For the second use case, a conversational shopping assistant provides real-time product recommendations using embedded expert knowledge and customer feedback. Both use cases leverage the connections in data through graph technologies to provide faster insights, improved data management and more relevant recommendations.

Mark Jensen, Director, Data Management and Interoperability, Frederick National Laboratory for Cancer Research Todd Pihl, Director, Technical Project Manager, Frederick National Laboratory for Cancer Research Ming Ying, Senior Software Engineer, Frederick National Laboratory for Cancer Research

The document discusses how Neo4j can be used to combat money laundering and financial fraud. It introduces the presenters and provides an agenda for the seminar. Additionally, it outlines Neo4j's capabilities for connecting disparate data sources and exposing related information to support enhanced decision making, fraud prevention, and compliance. Neo4j allows users to explore network and transactional data across multiple "anchor points" to discover relationships and patterns that may indicate money laundering or fraud.

These webinar slides are an introduction to Neo4j and Graph Databases. They discuss the primary use cases for Graph Databases and the properties of Neo4j which make those use cases possible. They also cover the high-level steps of modeling, importing, and querying your data using Cypher and touch on RDBMS to Graph.

Neo4j, the leading enterprise graph platform, is now globally available on Amazon Web Services (AWS) as a fully managed, always-on database service. Neo4j Aura Enterprise on AWS empowers organizations to rapidly build mission-critical, intelligent cloud-based applications backed by the performance, scale, security, and reliability that only the most deployed and most trusted graph technology can provide. Customers like Levi Strauss & Co., Sainsbury’s, Siemens, The Orchard and Tourism Media are already using Aura Enterprise on AWS for fraud detection, regulatory compliance, recommendation engines, supply chain analysis, and much more. Join us for this exclusive digital event to learn more about Neo4j Aura Enterprise on AWS: - Understand the state of the data and analytics market and how investing in Neo4j and AWS fits in the big picture - Get insights into how Siemens and Tourism Media are unlocking the power of graph databases on AWS during a panel discussion - Discover how to build modern graph applications with Neo4j on AWS through a step-by-step presentation and demo

This document discusses using graph data science and graph algorithms to detect fraud. It explains that graph data science uses relationships in data to power predictions. It provides examples of how graph algorithms like Louvain clustering, PageRank, connected components, and Jaccard similarity can be used to identify communities that frequently interact, measure influence, identify accounts sharing identifiers, and measure account similarity to detect fraud in applications like banking and financial services. The document also discusses using graph embeddings and feature engineering with graph networks to improve machine learning models for fraud detection by basing predictions on influential entities and their relationships.

A changing market landscape and open source innovations are having a dramatic impact on the consumability and ease of use of data science tools. Join this session to learn about the impact these trends and changes will have on the future of data science. If you are a data scientist, or if your organization relies on cutting edge analytics, you won't want to miss this!

This document summarizes Kelli-Jean Chun's presentation on using Python and R for data science. It discusses data science roles, provides an overview of Python and R, compares them for different use cases, and outlines a plan to predict whether NYC dogs are spayed/neutered using both languages. R will be used for exploratory data analysis and visualization, while Python with Scikit-learn, Pandas and NumPy will be used to build and evaluate a predictive model. The languages will be connected using rpy2 to load data from R into Python and reticulate to run Python code in RMarkdown.

Priyanka Dighe received her M.S. in Computer Science and Engineering from UC San Diego in 2017 and B.E. in Computer Science from BITS Pilani in 2013. She has work experience as a Software Engineer at Microsoft and Bloomreach, developing applications for Word and implementing alerting services. She completed internships at HP Labs and Bloomreach focusing on predictive analytics using social media and implementing alerting pipelines. Her skills include programming in Java, C, Python, and technologies like Spark, Hadoop, and Play Framework.

How data scientists can master advanced Python skills. It is a great way to boost and solidify your career.

This document provides an overview of getting started with data science using Python. It discusses what data science is, why it is in high demand, and the typical skills and backgrounds of data scientists. It then covers popular Python libraries for data science like NumPy, Pandas, Scikit-Learn, TensorFlow, and Keras. Common data science steps are outlined including data gathering, preparation, exploration, model building, validation, and deployment. Example applications and case studies are discussed along with resources for learning including podcasts, websites, communities, books, and TV shows.

Presentation of the Semantic Knowledge Graph research paper at the 2016 IEEE 3rd International Conference on Data Science and Advanced Analytics (Montreal, Canada - October 18th, 2016) Abstract—This paper describes a new kind of knowledge representation and mining system which we are calling the Semantic Knowledge Graph. At its heart, the Semantic Knowledge Graph leverages an inverted index, along with a complementary uninverted index, to represent nodes (terms) and edges (the documents within intersecting postings lists for multiple terms/nodes). This provides a layer of indirection between each pair of nodes and their corresponding edge, enabling edges to materialize dynamically from underlying corpus statistics. As a result, any combination of nodes can have edges to any other nodes materialize and be scored to reveal latent relationships between the nodes. This provides numerous benefits: the knowledge graph can be built automatically from a real-world corpus of data, new nodes - along with their combined edges - can be instantly materialized from any arbitrary combination of preexisting nodes (using set operations), and a full model of the semantic relationships between all entities within a domain can be represented and dynamically traversed using a highly compact representation of the graph. Such a system has widespread applications in areas as diverse as knowledge modeling and reasoning, natural language processing, anomaly detection, data cleansing, semantic search, analytics, data classification, root cause analysis, and recommendations systems. The main contribution of this paper is the introduction of a novel system - the Semantic Knowledge Graph - which is able to dynamically discover and score interesting relationships between any arbitrary combination of entities (words, phrases, or extracted concepts) through dynamically materializing nodes and edges from a compact graphical representation built automatically from a corpus of data representative of a knowledge domain.

Python is the choice llanguage for data analysis, The aim of this slide is to provide a comprehensive learning path to people new to python for data analysis. This path provides a comprehensive overview of the steps you need to learn to use Python for data analysis.

Pandas is an open source Python library that provides high-performance data structures and data analysis tools. It allows users to work with structured and unstructured data, clean and manipulate data sets, and perform complex analyses. The presentation will provide an overview of Pandas functionality, demonstrate how to download and install it, and showcase examples of using Pandas to clean and analyze financial data sets. There will be time for Q&A at the end.

Developer workflow analysis and ownership management present comprehension challenges for software ecosystems and global software engineering. Dark matter exists because tools are not fully integrated, logging is not designed for analysis, and developer workflow is unstructured. Probabilistic models using machine learning and heuristics can help associate activities with work items to address this. Ownership management challenges include ownership decay, asset subclassing, team-level ownership, and providing explainable recommendations.

Eliminate the unavoidable complexity of object-oriented designs. Using the persistent data structures built into most modern programming languages, Data-oriented programming cleanly separates code and data, which simplifies state management and eases concurrency. Data-Oriented Programming teaches you to design applications using the data-oriented paradigm. These powerful new ideas are presented through conversations, code snippets, diagrams, and even songs to help you quickly grok what’s great about DOP. You’ll learn to write DOP code that can be implemented in languages like JavaScript, Ruby, Python, Clojure and also in traditional OO languages like Java or C#. Learn more about the book here: http://mng.bz/XdKl

Agile Data Science is a lean methodology that is adopted from Agile Software Development. At the core it centers around people, interactions, and building minimally viable products to ship fast and often to solicit customer feedback. In this presentation, I describe how this work was done in the past with examples. Get started today with our help by visiting http://www.alpinenow.com

Neuron is a server-less Deep Learning and AI experiment platform for analytics where you can build, deploy and visualise the data models. Practical lab on cloud access from anywhere.

Slides from my talk at Big Data Spain 2014 in Madrid. In this talk, we will discuss our approach to bring large scale deep analytics to the masses. R is an extremely popular numerical computer environment, but scientific data processing frequently hits its memory limits. On the other hand, system to execute data intensive tasks like Hadoop or Stratosphere are not popular among R users because writing programs using these paradigms is cumbersome. We present an innovative approach to overcome these limitations using the Stratosphere/Apache Flink big data platform by means of a R package and ready-to-use distributed algorithm. This solution allows the user, with small modifications in the R code, to easily execute distributed scenarios using popular machine learning techniques. We will cover the implementation details of the proposed solution including the architecture of the system, the functionality implemented and working examples. In addition, we will cover what are the differences between our approach and other solutions that integrate R with Hadoop or other large-scale analytics systems. Finally, the results of the performance tests show that this solution is competitive with the already existing R implementations for small amounts of data and able to scale-up to gigabyte level.

Wes McKinney gave a presentation on the past, present, and future of Python for data analysis. He discussed the origins and development of pandas over the past 12 years from the first open source release in 2009 to the current state. Key points included pandas receiving its first formal funding in 2019, its large community of contributors, and factors driving Python's growth for data science like its package ecosystem and education. McKinney also addressed early concerns about Python and looked to the future, highlighting projects like Apache Arrow that aim to improve performance and interoperability.

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Learn Data Science with Python course for B.TECH, BCA, MCA, BSC, MSC, B.COM, and statistical students. Data Science with python online training course with certified industry experts. Get a 100 % pre-placement guarantee.

Vanya Sehgal is seeking a full-time position as a software developer. She has a Master's degree in Computer Science from Rochester Institute of Technology with a 3.75 GPA and relevant coursework including Java, C++, data management, and Android development. She has work experience as a software engineer intern at Intuit and Amazon where she worked on web and mobile applications, gained AWS experience, and performed testing. Her technical skills include Java, C++, SQL, Linux, and Android development and she has completed projects involving distributed systems, security evaluation, and mobile applications.

Watch here: https://bit.ly/3cZGCxr For their machine learning and data science projects to be successful, data scientists need access to all of the enterprise data delivered through their myriad of data models. However, gaining access to all data, integrated into a central repository has been a challenge. Often 80% of the project time is spent on these tasks. But, a virtual layer can help the data scientist speed up some of the most tedious tasks, like data exploration and analysis. At the same time, it also integrates well with the data science ecosystem. There is no need to change tools and learn new languages. The data virtualization platform helps data scientists offload these data integration tasks, allowing them to focus on advanced analytics. In this session, you will learn how data virtualization: - Provides all of the enterprise data, in real-time, and without replication - Enables data scientists to create and share multiple logical models using simple drag and drop - Provides a catalog of all business definitions, lineage, and relationships

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.