The slides give an overview of how Spark can be used to tackle Machine learning tasks, such as classification, regression, clustering, etc., at a Big Data scale.
Pyspark Tutorial | Introduction to Apache Spark with Python | PySpark Trainin...Edureka!
** PySpark Certification Training: https://www.edureka.co/pyspark-certification-training**
This Edureka tutorial on PySpark Tutorial will provide you with a detailed and comprehensive knowledge of Pyspark, how it works, the reason why python works best with Apache Spark. You will also learn about RDDs, data frames and mllib.
Pyspark Tutorial | Introduction to Apache Spark with Python | PySpark Trainin...Edureka!
** PySpark Certification Training: https://www.edureka.co/pyspark-certification-training**
This Edureka tutorial on PySpark Tutorial will provide you with a detailed and comprehensive knowledge of Pyspark, how it works, the reason why python works best with Apache Spark. You will also learn about RDDs, data frames and mllib.
Relational databases vs Non-relational databasesJames Serra
There is a lot of confusion about the place and purpose of the many recent non-relational database solutions ("NoSQL databases") compared to the relational database solutions that have been around for so many years. In this presentation I will first clarify what exactly these database solutions are, compare them, and discuss the best use cases for each. I'll discuss topics involving OLTP, scaling, data warehousing, polyglot persistence, and the CAP theorem. We will even touch on a new type of database solution called NewSQL. If you are building a new solution it is important to understand all your options so you take the right path to success.
DevOps for Applications in Azure Databricks: Creating Continuous Integration ...Databricks
Working with our customers, developers and partners around the world, it's clear DevOps has become increasingly critical to a team's success. Continuous integration (CI) and continuous delivery (CD) which is part of DevOps, embody a culture, set of operating principles, and collection of practices that enable application development teams to deliver code changes more frequently and reliably. In this session, we will cover how you can automate your entire process from code commit to production using CI/CD pipelines in Azure DevOps for Azure Databricks applications. Using CI/CD practices, you can simplify, speed and improve your cloud development to deliver features to your customers as soon as they're ready.
Apache Spark is a In Memory Data Processing Solution that can work with existing data source like HDFS and can make use of your existing computation infrastructure like YARN/Mesos etc. This talk will cover a basic introduction of Apache Spark with its various components like MLib, Shark, GrpahX and with few examples.
Learn to Use Databricks for Data ScienceDatabricks
Data scientists face numerous challenges throughout the data science workflow that hinder productivity. As organizations continue to become more data-driven, a collaborative environment is more critical than ever — one that provides easier access and visibility into the data, reports and dashboards built against the data, reproducibility, and insights uncovered within the data.. Join us to hear how Databricks’ open and collaborative platform simplifies data science by enabling you to run all types of analytics workloads, from data preparation to exploratory analysis and predictive analytics, at scale — all on one unified platform.
Apache Spark Tutorial | Spark Tutorial for Beginners | Apache Spark Training ...Edureka!
This Edureka Spark Tutorial will help you to understand all the basics of Apache Spark. This Spark tutorial is ideal for both beginners as well as professionals who want to learn or brush up Apache Spark concepts. Below are the topics covered in this tutorial:
1) Big Data Introduction
2) Batch vs Real Time Analytics
3) Why Apache Spark?
4) What is Apache Spark?
5) Using Spark with Hadoop
6) Apache Spark Features
7) Apache Spark Ecosystem
8) Demo: Earthquake Detection Using Apache Spark
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.
A french introduction to Cassandra database.
You can watch the video from here (in french)
http://www.jaxio.com/2012/01/06/introduction-a-cassandra-nosql-video.html
Organizations are struggling to make sense of their data within antiquated data platforms. Snowflake, the data warehouse built for the cloud, can help.
Neo4j is a powerful and expressive tool for storing, querying and manipulating data. However modeling data as graphs is quite different from modeling data under a relational database. In this talk, Michael Hunger will cover modeling business domains using graphs and show how they can be persisted and queried in Neo4j. We'll contrast this approach with the relational model, and discuss the impact on complexity, flexibility and performance.
Making Structured Streaming Ready for ProductionDatabricks
In mid-2016, we introduced Structured Steaming, a new stream processing engine built on Spark SQL that revolutionized how developers can write stream processing application without having to reason about having to reason about streaming. It allows the user to express their streaming computations the same way you would express a batch computation on static data. The Spark SQL engine takes care of running it incrementally and continuously updating the final result as streaming data continues to arrive. It truly unifies batch, streaming and interactive processing in the same Datasets/DataFrames API and the same optimized Spark SQL processing engine.
The initial alpha release of Structured Streaming in Apache Spark 2.0 introduced the basic aggregation APIs and files as streaming source and sink. Since then, we have put in a lot of work to make it ready for production use. In this talk, Tathagata Das will cover in more detail about the major features we have added, the recipes for using them in production, and the exciting new features we have plans for in future releases. Some of these features are as follows:
- Design and use of the Kafka Source
- Support for watermarks and event-time processing
- Support for more operations and output modes
Speaker: Tathagata Das
This talk was originally presented at Spark Summit East 2017.
The Modern Data Team for the Modern Data Stack: dbt and the Role of the Analy...Databricks
A traditional data team has roles including data engineer, data scientist, and data analyst. However, many organizations are finding success by integrating a new role – the analytics engineer. The analytics engineer develops a code-based data infrastructure that can serve both analytics and data science teams. He or she develops re-usable data models using the software engineering practices of version control and unit testing, and provides the critical domain expertise that ensures that data products are relevant and insightful. In this talk we’ll talk about the role and skill set of the analytics engineer, and discuss how dbt, an open source programming environment, empowers anyone with a SQL skillset to fulfill this new role on the data team. We’ll demonstrate how to use dbt to build version-controlled data models on top of Delta Lake, test both the code and our assumptions about the underlying data, and orchestrate complete data pipelines on Apache Spark™.
"The common use cases of Spark SQL include ad hoc analysis, logical warehouse, query federation, and ETL processing. Spark SQL also powers the other Spark libraries, including structured streaming for stream processing, MLlib for machine learning, and GraphFrame for graph-parallel computation. For boosting the speed of your Spark applications, you can perform the optimization efforts on the queries prior employing to the production systems. Spark query plans and Spark UIs provide you insight on the performance of your queries. This talk discloses how to read and tune the query plans for enhanced performance. It will also cover the major related features in the recent and upcoming releases of Apache Spark.
"
Ted Willke, Senior Principal Engineer & GM, Datacenter Group, Intel at MLconf SFMLconf
Abstract: How graphs became just another big data primitive
Graph-shaped data is used in product recommendation systems, social network analysis, network threat detection, image de-noising, and many other important applications. And, a growing number of these applications will benefit from parallel distributed processing for graph featuring engineering, model training, and model serving. But today’s graph tools are riddled with limitations and shortcomings, such as a lack of language bindings, streaming support, and seamless integration with other popular data services. In this talk, we’ll argue that the key to doing more with graphs is doing less with specialized systems and more with systems already good at handling data of other shapes. We’ll examine some practical data science workflows to further motivate this argument and we’ll talk about some of the things that Intel is doing with the open source community and industry to make graphs just another big data primitive.
Relational databases vs Non-relational databasesJames Serra
There is a lot of confusion about the place and purpose of the many recent non-relational database solutions ("NoSQL databases") compared to the relational database solutions that have been around for so many years. In this presentation I will first clarify what exactly these database solutions are, compare them, and discuss the best use cases for each. I'll discuss topics involving OLTP, scaling, data warehousing, polyglot persistence, and the CAP theorem. We will even touch on a new type of database solution called NewSQL. If you are building a new solution it is important to understand all your options so you take the right path to success.
DevOps for Applications in Azure Databricks: Creating Continuous Integration ...Databricks
Working with our customers, developers and partners around the world, it's clear DevOps has become increasingly critical to a team's success. Continuous integration (CI) and continuous delivery (CD) which is part of DevOps, embody a culture, set of operating principles, and collection of practices that enable application development teams to deliver code changes more frequently and reliably. In this session, we will cover how you can automate your entire process from code commit to production using CI/CD pipelines in Azure DevOps for Azure Databricks applications. Using CI/CD practices, you can simplify, speed and improve your cloud development to deliver features to your customers as soon as they're ready.
Apache Spark is a In Memory Data Processing Solution that can work with existing data source like HDFS and can make use of your existing computation infrastructure like YARN/Mesos etc. This talk will cover a basic introduction of Apache Spark with its various components like MLib, Shark, GrpahX and with few examples.
Learn to Use Databricks for Data ScienceDatabricks
Data scientists face numerous challenges throughout the data science workflow that hinder productivity. As organizations continue to become more data-driven, a collaborative environment is more critical than ever — one that provides easier access and visibility into the data, reports and dashboards built against the data, reproducibility, and insights uncovered within the data.. Join us to hear how Databricks’ open and collaborative platform simplifies data science by enabling you to run all types of analytics workloads, from data preparation to exploratory analysis and predictive analytics, at scale — all on one unified platform.
Apache Spark Tutorial | Spark Tutorial for Beginners | Apache Spark Training ...Edureka!
This Edureka Spark Tutorial will help you to understand all the basics of Apache Spark. This Spark tutorial is ideal for both beginners as well as professionals who want to learn or brush up Apache Spark concepts. Below are the topics covered in this tutorial:
1) Big Data Introduction
2) Batch vs Real Time Analytics
3) Why Apache Spark?
4) What is Apache Spark?
5) Using Spark with Hadoop
6) Apache Spark Features
7) Apache Spark Ecosystem
8) Demo: Earthquake Detection Using Apache Spark
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.
A french introduction to Cassandra database.
You can watch the video from here (in french)
http://www.jaxio.com/2012/01/06/introduction-a-cassandra-nosql-video.html
Organizations are struggling to make sense of their data within antiquated data platforms. Snowflake, the data warehouse built for the cloud, can help.
Neo4j is a powerful and expressive tool for storing, querying and manipulating data. However modeling data as graphs is quite different from modeling data under a relational database. In this talk, Michael Hunger will cover modeling business domains using graphs and show how they can be persisted and queried in Neo4j. We'll contrast this approach with the relational model, and discuss the impact on complexity, flexibility and performance.
Making Structured Streaming Ready for ProductionDatabricks
In mid-2016, we introduced Structured Steaming, a new stream processing engine built on Spark SQL that revolutionized how developers can write stream processing application without having to reason about having to reason about streaming. It allows the user to express their streaming computations the same way you would express a batch computation on static data. The Spark SQL engine takes care of running it incrementally and continuously updating the final result as streaming data continues to arrive. It truly unifies batch, streaming and interactive processing in the same Datasets/DataFrames API and the same optimized Spark SQL processing engine.
The initial alpha release of Structured Streaming in Apache Spark 2.0 introduced the basic aggregation APIs and files as streaming source and sink. Since then, we have put in a lot of work to make it ready for production use. In this talk, Tathagata Das will cover in more detail about the major features we have added, the recipes for using them in production, and the exciting new features we have plans for in future releases. Some of these features are as follows:
- Design and use of the Kafka Source
- Support for watermarks and event-time processing
- Support for more operations and output modes
Speaker: Tathagata Das
This talk was originally presented at Spark Summit East 2017.
The Modern Data Team for the Modern Data Stack: dbt and the Role of the Analy...Databricks
A traditional data team has roles including data engineer, data scientist, and data analyst. However, many organizations are finding success by integrating a new role – the analytics engineer. The analytics engineer develops a code-based data infrastructure that can serve both analytics and data science teams. He or she develops re-usable data models using the software engineering practices of version control and unit testing, and provides the critical domain expertise that ensures that data products are relevant and insightful. In this talk we’ll talk about the role and skill set of the analytics engineer, and discuss how dbt, an open source programming environment, empowers anyone with a SQL skillset to fulfill this new role on the data team. We’ll demonstrate how to use dbt to build version-controlled data models on top of Delta Lake, test both the code and our assumptions about the underlying data, and orchestrate complete data pipelines on Apache Spark™.
"The common use cases of Spark SQL include ad hoc analysis, logical warehouse, query federation, and ETL processing. Spark SQL also powers the other Spark libraries, including structured streaming for stream processing, MLlib for machine learning, and GraphFrame for graph-parallel computation. For boosting the speed of your Spark applications, you can perform the optimization efforts on the queries prior employing to the production systems. Spark query plans and Spark UIs provide you insight on the performance of your queries. This talk discloses how to read and tune the query plans for enhanced performance. It will also cover the major related features in the recent and upcoming releases of Apache Spark.
"
Ted Willke, Senior Principal Engineer & GM, Datacenter Group, Intel at MLconf SFMLconf
Abstract: How graphs became just another big data primitive
Graph-shaped data is used in product recommendation systems, social network analysis, network threat detection, image de-noising, and many other important applications. And, a growing number of these applications will benefit from parallel distributed processing for graph featuring engineering, model training, and model serving. But today’s graph tools are riddled with limitations and shortcomings, such as a lack of language bindings, streaming support, and seamless integration with other popular data services. In this talk, we’ll argue that the key to doing more with graphs is doing less with specialized systems and more with systems already good at handling data of other shapes. We’ll examine some practical data science workflows to further motivate this argument and we’ll talk about some of the things that Intel is doing with the open source community and industry to make graphs just another big data primitive.
Microsoft R enable enterprise-wide, scalable experimental data science and operational machine learning, by providing a collection of servers and tools that extend the capabilities of open-source R In these slides, we give a quick introduction to Microsoft R Server architecture, and a comprehensive overview of ScaleR, the core libraries to Microsoft R, that enables parallel execution and use external data frames (xdfs). A tutorial-like presentation covering how to: 1) setup the environments, 2) read data, 3) process & transform, 4) analyse, summarize, visualize, 5) learn & predict, and finally 6) deploy and consume (using msrdeploy).
Etosha - Data Asset Manager : Status and road mapDr. Mirko Kämpf
Etosha is an enterprise focused collaborative graph database with facts about data sets, analysis procedures, and research methods. People from multiple organizations can be connected while every owner retains full control about its own data.
A full Machine learning pipeline in Scikit-learn vs in scala-Spark: pros and ...Jose Quesada (hiring)
The machine learning libraries in Apache Spark are an impressive piece of software engineering, and are maturing rapidly. What advantages does Spark.ml offer over scikit-learn? At Data Science Retreat we've taken a real-world dataset and worked through the stages of building a predictive model -- exploration, data cleaning, feature engineering, and model fitting; which would you use in production?
The machine learning libraries in Apache Spark are an impressive piece of software engineering, and are maturing rapidly. What advantages does Spark.ml offer over scikit-learn?
At Data Science Retreat we've taken a real-world dataset and worked through the stages of building a predictive model -- exploration, data cleaning, feature engineering, and model fitting -- in several different frameworks. We'll show what it's like to work with native Spark.ml, and compare it to scikit-learn along several dimensions: ease of use, productivity, feature set, and performance.
In some ways Spark.ml is still rather immature, but it also conveys new superpowers to those who know how to use it.
This talk will provide overview of big data software engineering and software engineering for big data as the tow fields need integrated. The interplay between the two field of research applications of Data Science and Software Engineering will enhance future perspective for a safe, secure, and sustainable approaches to data science and application of data science for 50 years of software engineering data that exists.
For a Python driven Data Science team, DASK presents a very obvious logical next step for distributed analysis. However, today the de-facto standard choice for exact same purpose is Apache Spark. DASK is a pure Python framework, which does more of same i.e. it allows one to run the same Pandas or NumPy code either locally or on a cluster. Whereas, Apache Spark brings about a learning curve involving a new API and execution model although with a Python wrapper. Given the above statement, do we even need to compare and contrast to make a choice? Shouldn't DASK be the default choice? Well, that's what this session is about. It goes in detail explaining the various viewpoints and dimensions that need to be considered to pick one over other.
Scikit-Learn is a powerful machine learning library implemented in Python with numeric and scientific computing powerhouses Numpy, Scipy, and matplotlib for extremely fast analysis of small to medium sized data sets. It is open source, commercially usable and contains many modern machine learning algorithms for classification, regression, clustering, feature extraction, and optimization. For this reason Scikit-Learn is often the first tool in a Data Scientists toolkit for machine learning of incoming data sets.
The purpose of this one day course is to serve as an introduction to Machine Learning with Scikit-Learn. We will explore several clustering, classification, and regression algorithms for a variety of machine learning tasks and learn how to implement these tasks with our data using Scikit-Learn and Python. In particular, we will structure our machine learning models as though we were producing a data product, an actionable model that can be used in larger programs or algorithms; rather than as simply a research or investigation methodology.
Operational Machine Learning: Using Microsoft Technologies for Applied Data S...Khalid Salama
Sqlbits 2017 session - Data Science concerns with the activities of processing and analysing data in a particular domain, as well as applying machine learning algorithms to automatically discover insights and interesting patterns from the data. While data scientist needs tools to explore and visualize data, along with performing machine learning experiments and evaluating candidate models, operational platforms are required to productionize and maintain the resultant models, and integrate them into the operational systems. In this session, we explore the main features and capabilities of various Microsoft technologies that enable such end-to-end data science exercise, including SQL Server and Azure PaaS Services, along with practical scenarios and demos.
1. Big Data Analytics
- Big Data
- Spark: Big Data Analytics
- Resilient Distributed Datasets (RDD)
- Spark libraries (SQL, DataFrames, MLlib for machine learning, GraphX, and Streaming)
- PFP: Parallel FP-Growth
2. Ubiquitous Computing
- Edge Computing
- Cloudlet
- Fog computing
- Internet of Things (IoT)
- Virtualization
- Virtual Conferencing
- Virtual Events (2D, 3D, and Hybrid)
This presentation has been uploaded by Public Relations Cell, IIM Rohtak to help the B-school aspirants crack their interview by gaining basic knowledge on IT.
In this video from the ISC Big Data'14 Conference, Ted Willke from Intel presents: The Analytics Frontier of the Hadoop Eco-System.
"The Hadoop MapReduce framework grew out of an effort to make it easy to express and parallelize simple computations that were routinely performed at Google. It wasn’t long before libraries, like Apache Mahout, were developed to enable matrix factorization, clustering, regression, and other more complex analyses on Hadoop. Now, many of these libraries and their workloads are migrating to Apache Spark because it supports a wider class of applications than MapReduce and is more appropriate for iterative algorithms, interactive processing, and streaming applications. What’s next beyond Spark? Where is big data analytics processing headed? How will data scientists program these systems? In this talk, we will explore the current analytics frontier, the popular debates, and discuss some potentially clever additions. We will also share the emergent data science applications and collaborative university research that inform our thinking."
Learn more:
http://www.isc-events.com/bigdata14/schedule.html
and
http://www.intel.com/content/www/us/en/software/intel-graph-solutions.html
Watch the video presentation: https://www.youtube.com/watch?v=qlfx495Ekw0
AUSOUG - NZOUG-GroundBreakers-Jun 2019 - AI and Machine LearningSandesh Rao
Autonomous Database is one of the hottest Oracle products where we have attempted to use Machine Learning for several aspects of the service. This presentation takes a view on our current state of Diagnostic methodology in the Autonomous Database Cloud services and how do we process this data to find anomalies in them to troubleshoot them at a scale of several petabytes a year and conduct AIOps. Some of the use cases we will cover are a Log Anomaly timeline which we reduce significant amounts of logs using semi-supervised machine learning techniques to reduce logs and match them in near real time. We will cover techniques to analyze database issues using Machine learning techniques like Kmeans , TFIDF, Random Forests, and z-scores to predict if a spike in the CPU is a normal or abnormal spike. We will also talk about RNN’s with LSTM/GRU as some of the applications of how to predict faults before they happen. Some of the other use cases are to use convolution filters to determine maintenance windows within the database workloads, determine best times to do database backups, security anomaly timelines and many others. This is a production service and this can be used if you have a customer SR/defect today. The service is much more extensive inside the Oracle Autonomous Database Cloud. This presentation will accompany several examples with how to apply these techniques, machine learning knowledge is preferred but not a prerequisite
Makine Öğrenmesi, Yapay Zeka ve Veri Bilimi Süreçlerinin Otomatikleştirilmesi...Ali Alkan
Makine Öğrenmesi, Yapay Zeka ve Veri Bilimi Süreçlerinin Otomatikleştirilmesi | Automating Machine Learning, Artificial Intelligence, and Data Science | Guided Analytics
Building the Data Lake with Azure Data Factory and Data Lake AnalyticsKhalid Salama
In essence, a data lake is commodity distributed file system that acts as a repository to hold raw data file extracts of all the enterprise source systems, so that it can serve the data management and analytics needs of the business. A data lake system provides means to ingest data, perform scalable big data processing, and serve information, in addition to manage, monitor and secure the it environment. In these slide, we discuss building data lakes using Azure Data Factory and Data Lake Analytics. We delve into the architecture if the data lake and explore its various components. We also describe the various data ingestion scenarios and considerations. We introduce the Azure Data Lake Store, then we discuss how to build Azure Data Factory pipeline to ingest the data lake. After that, we move into big data processing using Data Lake Analytics, and we delve into U-SQL.
Microservices, DevOps, and Continuous DeliveryKhalid Salama
Continuous Delivery is the ability to get software changes - including new features, enhancements, configuration changes, and bug fixes - into production safely and quickly, in a sustainable way. In these slides, I am giving a very high-level introduction to microservices architecture, and why it is considered as enabler to continuous delivery. We cover the key characteristics of a microservice, some common concepts, architectural patterns, and implementation guidelines. In addition, we quickly cover the main concepts and activities in DevOps, which the Application Lifecycle Management process to support continuous delivery.
A VERY high level over view of Graph Analytics concepts and techniques, including structural analytics, Connectivity Analytics, Community Analytics, Path Analytics, as well as Pattern Matching
Enterprise Cloud Data Platforms - with Microsoft AzureKhalid Salama
These slides gives an overview on MS Azure Data Architecture and Services, including Data Lake Analytics, Data Factory, Azure SQL DW, Stream Analytics, Azure Machine learning tools, and Data Catalog. This is also known as Cortana Analytical Suite
These slides gives an overview of NoSQL in the context of Big Data processing. We start by defining SQL vs NoSQL concepts, the CAP theorem, and why NoSQL technologies are needed. Then we discuss the various NoSQL technology breeds, including Key/Value stores, Document stores, Column Family (wide-column) stores, memory cache stores, and graph stores, along with related tools and examples. After that we present various solution architecture patterns, in which NoSQL data stores play viable roles. Next we delve into Microsoft Azure implementation of some of these NoSQL technologies, including Redis Cache, Azure Table Storage, HBase on HDInsight, and Azure DocumentDB. Finally, we conclude with some useful resource, before we give a sneak peek on how to use neo4j for Graph Processing.
Real-Time Event & Stream Processing on MS AzureKhalid Salama
These slides discuss the main concepts of event & stream processing, as well as the related technologies on Microsoft Azure. We start by giving and overview of what Event & Stream Processing is. Then we describe the canonical architecture of a Stream Processing solution. We will delve into Message Queuing part of the solution. After that, we Introduce Apache Storm on HDInsight, as well as Azure Stream Analytics. We compare Apache Storm to Azure Stream Analytics, and finally conclude with useful resources
Intorducing Big Data and Microsoft AzureKhalid Salama
The purpose of these slides is to give a high-level overview of Big Data concepts and techniques, as well as its related tools and technologies, focusing on Microsoft Azure. It starts by defining what Big Data is, as well as why Big Data platforms are needed. Fundamental components of a Big Data Platform are discussed, followed by a little bit of theory about Distributed Processing & CAP Theorem, and its relevance to how Big Data Solutions compare to Traditional RDBMS. Use case of how Big Data fits in Enterprise Data Platforms are shown. The Hadoop Ecosystem is briefly reviewed before Big Data on Microsoft Azure is discussed. Then some directions of How to get started with Big Data.
Recently, in the fields Business Intelligence and Data Management, everybody is talking about data science, machine learning, predictive analytics and many other “clever” terms with promises to turn your data into gold. In this slides, we present the big picture of data science and machine learning. First, we define the context for data mining from BI perspective, and try to clarify various buzzwords in this field. Then we give an overview of the machine learning paradigms. After that, we are going to discuss - at a high level - the various data mining tasks, techniques and applications. Next, we will have a quick tour through the Knowledge Discovery Process. Screenshots from demos will be shown, and finally we conclude with some takeaway points.
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...John Andrews
SlideShare Description for "Chatty Kathy - UNC Bootcamp Final Project Presentation"
Title: Chatty Kathy: Enhancing Physical Activity Among Older Adults
Description:
Discover how Chatty Kathy, an innovative project developed at the UNC Bootcamp, aims to tackle the challenge of low physical activity among older adults. Our AI-driven solution uses peer interaction to boost and sustain exercise levels, significantly improving health outcomes. This presentation covers our problem statement, the rationale behind Chatty Kathy, synthetic data and persona creation, model performance metrics, a visual demonstration of the project, and potential future developments. Join us for an insightful Q&A session to explore the potential of this groundbreaking project.
Project Team: Jay Requarth, Jana Avery, John Andrews, Dr. Dick Davis II, Nee Buntoum, Nam Yeongjin & Mat Nicholas
Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.
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Empowering the Data Analytics Ecosystem: A Laser Focus on Value
The data analytics ecosystem thrives when every component functions at its peak, unlocking the true potential of data. Here's a laser focus on key areas for an empowered ecosystem:
1. Democratize Access, Not Data:
Granular Access Controls: Provide users with self-service tools tailored to their specific needs, preventing data overload and misuse.
Data Catalogs: Implement robust data catalogs for easy discovery and understanding of available data sources.
2. Foster Collaboration with Clear Roles:
Data Mesh Architecture: Break down data silos by creating a distributed data ownership model with clear ownership and responsibilities.
Collaborative Workspaces: Utilize interactive platforms where data scientists, analysts, and domain experts can work seamlessly together.
3. Leverage Advanced Analytics Strategically:
AI-powered Automation: Automate repetitive tasks like data cleaning and feature engineering, freeing up data talent for higher-level analysis.
Right-Tool Selection: Strategically choose the most effective advanced analytics techniques (e.g., AI, ML) based on specific business problems.
4. Prioritize Data Quality with Automation:
Automated Data Validation: Implement automated data quality checks to identify and rectify errors at the source, minimizing downstream issues.
Data Lineage Tracking: Track the flow of data throughout the ecosystem, ensuring transparency and facilitating root cause analysis for errors.
5. Cultivate a Data-Driven Mindset:
Metrics-Driven Performance Management: Align KPIs and performance metrics with data-driven insights to ensure actionable decision making.
Data Storytelling Workshops: Equip stakeholders with the skills to translate complex data findings into compelling narratives that drive action.
Benefits of a Precise Ecosystem:
Sharpened Focus: Precise access and clear roles ensure everyone works with the most relevant data, maximizing efficiency.
Actionable Insights: Strategic analytics and automated quality checks lead to more reliable and actionable data insights.
Continuous Improvement: Data-driven performance management fosters a culture of learning and continuous improvement.
Sustainable Growth: Empowered by data, organizations can make informed decisions to drive sustainable growth and innovation.
By focusing on these precise actions, organizations can create an empowered data analytics ecosystem that delivers real value by driving data-driven decisions and maximizing the return on their data investment.
Explore our comprehensive data analysis project presentation on predicting product ad campaign performance. Learn how data-driven insights can optimize your marketing strategies and enhance campaign effectiveness. Perfect for professionals and students looking to understand the power of data analysis in advertising. for more details visit: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/
Opendatabay - Open Data Marketplace.pptxOpendatabay
Opendatabay.com unlocks the power of data for everyone. Open Data Marketplace fosters a collaborative hub for data enthusiasts to explore, share, and contribute to a vast collection of datasets.
First ever open hub for data enthusiasts to collaborate and innovate. A platform to explore, share, and contribute to a vast collection of datasets. Through robust quality control and innovative technologies like blockchain verification, opendatabay ensures the authenticity and reliability of datasets, empowering users to make data-driven decisions with confidence. Leverage cutting-edge AI technologies to enhance the data exploration, analysis, and discovery experience.
From intelligent search and recommendations to automated data productisation and quotation, Opendatabay AI-driven features streamline the data workflow. Finding the data you need shouldn't be a complex. Opendatabay simplifies the data acquisition process with an intuitive interface and robust search tools. Effortlessly explore, discover, and access the data you need, allowing you to focus on extracting valuable insights. Opendatabay breaks new ground with a dedicated, AI-generated, synthetic datasets.
Leverage these privacy-preserving datasets for training and testing AI models without compromising sensitive information. Opendatabay prioritizes transparency by providing detailed metadata, provenance information, and usage guidelines for each dataset, ensuring users have a comprehensive understanding of the data they're working with. By leveraging a powerful combination of distributed ledger technology and rigorous third-party audits Opendatabay ensures the authenticity and reliability of every dataset. Security is at the core of Opendatabay. Marketplace implements stringent security measures, including encryption, access controls, and regular vulnerability assessments, to safeguard your data and protect your privacy.