Version 1 of a much deeper topic that I intend to explore. The context is limited to my environment and industry. It's a topic that concerns every tech hiring manager looking for such roles right now.
How to identify the Return on Investment of Big Data / CIO (Infographic)suparupaa
The Identification of the ROI of Big Data is Pending on the Democratization of the Business Insights Coming from Advanced and Predictive Analytics of that Information
Un caso di studio sui big data - Campus Connestions Summit 2018 - #CCS18Angelo Gino Varrati
In this talk I talk about Big Data (introduction to), Microsoft Azure and its services for the Big Data. I expose and experiment of sentiment analysis on Twitter using Azure Machine Learning .
Visualize and explore your google analytics data with power biOmar Khan
Power BI allows users to easily connect to and visualize their Google Analytics data in order to gain insights about website traffic such as where viewers are coming from, which days see the most traffic, and how long people spend on the site. By connecting through the Google Analytics API, the Power BI content pack provides an out-of-box dashboard, report, and dataset that allows users to monitor and explore their Google Analytics data from the last 6 months to help improve their site and inform decisions.
Manuel Bruscas, soci fundador de BCN Analytics, presenta la ciència de dades aplicada a la presa de decisions i, concretament, el projecte BCN Analytics que consisteix en fer de Barcelona un centre europeu d'Analytics aprofitant-se de les oportunitats i les característiques que ofereix la ciutat.
Aquesta presentació ha tingut lloc a la TSIUC'14, celebrada a la Universitat Autònoma de Barcelona el passat 2 de desembre de 2014, sota el títol "Reptes en Big Data a la universitat i la Recerca".
"Agile Analytics" - Marianne Faro, Analytics Competence Lead at ItilityDataconomy Media
This document summarizes a Big Data meetup in Amsterdam on Agile Analytics. It discusses the 3 V's of big data - volume, velocity, and variety. It also discusses the 3 V's of an analytics team - different types of customers/projects, speed of starting up projects using Agile methods, and roles on the team. The document advocates taking an "elephant approach" to Agile Analytics, meaning biting off pieces of big projects bit by bit. It also describes Itility as a company that designs and manages end-to-end digital solutions and offers a managed analytics platform as a cloud-like service to customers.
Analytics, BigQuery, Looker and How I Became an Internet Meme for 48 HoursRittman Analytics
Mark Rittman presents on his experience becoming an internet meme for 48 hours after sharing a photo of his Google Analytics usage on Twitter. He currently works as an independent analyst and product manager using tools like BigQuery, Looker, and Google Cloud Platform. The presentation details a project analyzing sentiment toward a news article about Rittman and his kettle through queries of Twitter and news data in BigQuery using APIs for natural language processing and geocoding.
How the IoT market may change our digital life thanks to the Data Tsunami it ...Dataconomy Media
Laurent Marchou is the IoT & Robotics Director running the open-innovation product marketing team of Orange Innovation Department (Technocentre) thanks to products like Datavenue, Ub-y or LoRa starter kit. He loves working on disruption topics and was in charge of Broadband offers for Wanadoo (a long long time ago :) ) and Product Marketing Manager on mobile market. Laurent is graduate from ESME Engineering School (Telecom Dept with TelecomParisTech) and from HEC with an advance certificate (Business Unit Management)
This document discusses how graphs can be used to solve big data problems by enabling insights through modeling and analyzing connections in data. It provides examples of how graphs have transformed industries like consumer web, telecommunications, and databases. Specifically, it outlines how graphs can be used to model reality, store data in high fidelity, look for connections in content, customers, users and products, and build these graph-based insights into applications for recommendations, impact analysis, dependency management, and risk analysis.
How to identify the Return on Investment of Big Data / CIO (Infographic)suparupaa
The Identification of the ROI of Big Data is Pending on the Democratization of the Business Insights Coming from Advanced and Predictive Analytics of that Information
Un caso di studio sui big data - Campus Connestions Summit 2018 - #CCS18Angelo Gino Varrati
In this talk I talk about Big Data (introduction to), Microsoft Azure and its services for the Big Data. I expose and experiment of sentiment analysis on Twitter using Azure Machine Learning .
Visualize and explore your google analytics data with power biOmar Khan
Power BI allows users to easily connect to and visualize their Google Analytics data in order to gain insights about website traffic such as where viewers are coming from, which days see the most traffic, and how long people spend on the site. By connecting through the Google Analytics API, the Power BI content pack provides an out-of-box dashboard, report, and dataset that allows users to monitor and explore their Google Analytics data from the last 6 months to help improve their site and inform decisions.
Manuel Bruscas, soci fundador de BCN Analytics, presenta la ciència de dades aplicada a la presa de decisions i, concretament, el projecte BCN Analytics que consisteix en fer de Barcelona un centre europeu d'Analytics aprofitant-se de les oportunitats i les característiques que ofereix la ciutat.
Aquesta presentació ha tingut lloc a la TSIUC'14, celebrada a la Universitat Autònoma de Barcelona el passat 2 de desembre de 2014, sota el títol "Reptes en Big Data a la universitat i la Recerca".
"Agile Analytics" - Marianne Faro, Analytics Competence Lead at ItilityDataconomy Media
This document summarizes a Big Data meetup in Amsterdam on Agile Analytics. It discusses the 3 V's of big data - volume, velocity, and variety. It also discusses the 3 V's of an analytics team - different types of customers/projects, speed of starting up projects using Agile methods, and roles on the team. The document advocates taking an "elephant approach" to Agile Analytics, meaning biting off pieces of big projects bit by bit. It also describes Itility as a company that designs and manages end-to-end digital solutions and offers a managed analytics platform as a cloud-like service to customers.
Analytics, BigQuery, Looker and How I Became an Internet Meme for 48 HoursRittman Analytics
Mark Rittman presents on his experience becoming an internet meme for 48 hours after sharing a photo of his Google Analytics usage on Twitter. He currently works as an independent analyst and product manager using tools like BigQuery, Looker, and Google Cloud Platform. The presentation details a project analyzing sentiment toward a news article about Rittman and his kettle through queries of Twitter and news data in BigQuery using APIs for natural language processing and geocoding.
How the IoT market may change our digital life thanks to the Data Tsunami it ...Dataconomy Media
Laurent Marchou is the IoT & Robotics Director running the open-innovation product marketing team of Orange Innovation Department (Technocentre) thanks to products like Datavenue, Ub-y or LoRa starter kit. He loves working on disruption topics and was in charge of Broadband offers for Wanadoo (a long long time ago :) ) and Product Marketing Manager on mobile market. Laurent is graduate from ESME Engineering School (Telecom Dept with TelecomParisTech) and from HEC with an advance certificate (Business Unit Management)
This document discusses how graphs can be used to solve big data problems by enabling insights through modeling and analyzing connections in data. It provides examples of how graphs have transformed industries like consumer web, telecommunications, and databases. Specifically, it outlines how graphs can be used to model reality, store data in high fidelity, look for connections in content, customers, users and products, and build these graph-based insights into applications for recommendations, impact analysis, dependency management, and risk analysis.
Gartner and other analyst firms rank Pega higher than Salesforce in several areas important for customer service. Specifically:
- Gartner named Pega as the #1 product for customer service in their 2018 Critical Capabilities report.
- Pega outperformed or equaled Salesforce in all areas except social media according to Gartner.
- Other analysts like Forrester ranked Pega higher than Salesforce for capabilities like real-time interaction management and digital decisioning.
- Salesforce was not included in analyst reports for case management, business process management, and other important customer service areas, while Pega was included and ranked highly.
Predictive Analytics World Berlin 2016 Call for SpeakersDatentreiber
Predictive Analytics World is the leading provider of independent specialized conferences in applied predictive analytics. Users, decision makers and experts in predictive analytics will meet in Berlin in order to discover the latest findings and progress, to exchange among specialists and in person and to be inspired by the success stories.
Best Hadoop Institutes : kelly tecnologies is the best Hadoop training Institute in Bangalore.Providing hadoop courses by realtime faculty in Bangalore.
Big Data Is Not Enough - Real-Time Analytics Needs Streaming ArchtecturesDr. Tim Frey
Leveraging IoT data treasure is fate-deciding for enterprises. As more and more decisions get done out of data lake analytics by data scientists, the future are reactive real-time decisions. This presentation shows streaming architectures and visualizes how they work on a conceptual level. Thereby, we describe an example application how the German election and TV duells Twitter mentions got analyzed in real time. Finally, an outlook how to merge data streams of different enterprises concludes the presentation.
German summary:
Das Heben des eigenen (IoT) Datenschatzes ist für Unternehmen derzeit unglaublich wichtig um notwendige Wettbewerbsvorteile zu erlagen. Nachdem immer mehr Wissen aus den eigenen Unternehmensdaten gezogen wird, ist es in Zukunft erfolgsentscheidend in Echtzeit auf Datenänderungen zu reagieren und Datenanalysen zu beschleunigen. Daher zeigt dieser Vortrag sogenannte Streaming Architekturen mit welchen Datenströme und Ereignisse dynamisch und schnell analysiert werden. Um den Streaming Ansatzes mit einer Demo zu verdeutlichen wird die Architektur der Echtzeitanalyse von Twitter zur Bundestagswahl 2017 (https://www.iunera.com/analyse-der-bundestagswahl-2017/) dargestellt. Zuletzt zeigt ein Ausblick wie verschiedene Datenquellen (d.h. von Unternehmen selbst und externen Quellen) durch das Streaming Konzept kombiniert und analysiert werden können.
It’s been second time that google has improved search algorithm and released mysterious update without announcing it officially. Expert name it as "News Wave Algorithm update.,
This document provides an overview of a technology dive on location analytics and 3D mapping. The location analytics portion focuses on integrating data from multiple sources, mapping that data to understand relationships, analyzing the data spatially, enriching it with demographic information, and sharing insights. Key benefits to existing Esri customers include extending GIS capabilities within organizations and leveraging hosted maps and analytics. The session also includes a software demonstration and leaves time for questions.
The world of data analytics has opened up to include a much broader spectrum of data types than the traditional rows and columns found in relational databases. Text analytics includes whole new classes of tools for search and semantic understanding. Speech and image recognition software have become mainstream. How is data analytics changing in scope and practice in the era of Big Data?
This webinar will answer this question by looking at the following:
New tools for leveraging more data types
Differences in Big Data analytics architecture
New directions in Big Data analytics
The document summarizes several data warehousing trends presented by speaker Chris Riccomini. Some of the trends discussed include: real-time data warehousing which handles hard deletes and replay from Kafka; data mesh which advocates for decentralized data ownership and treating data as a product; and headless BI which programs business metrics for use across systems rather than being confined within BI tools. The talk also covered data catalogs, reverse ETL, analytics engineering and other trends.
Moving EA - from where we are to where we should beLeanIX GmbH
Presentation held by Dr. Stefan Zerbe, ITM at EA Connect Days 2018 in Bonn. While EA (enterprise architecture) is a well-known discipline many business organizations struggle with maturity of their corporate EA practice. But even companies that stopped EA activities in recent years now relaunch EA, in order to tackle challenges resulting from digital transformation, regulatory pressure and increasing IT complexity. This presentation builds on lessons learned from companies working with EA and refocuses the EA value proposition in order to meet business expectations. It highlights the importance to extent EA thinking to business units and explains collaboration modelsto engage both, business and IT architecture specialists as well as managers, in joint architecture activities based on a real project example. From a business-oriented perspective on EA, the presentation picks up the discussion regarding a new agile mindset for EA architects and presents new ideas for tool sets to support EA work in corporations.
Analytics, Business Intelligence, and Data Science - What's the Progression?DATAVERSITY
Data analysis can include looking back at historical data, understanding what an organization currently has, and even looking forward to predictions of the future. This presentation will talk about the differences between analytics, business intelligence, and data science, as well as the differences in architecture — and possibly even organization maturity — that make each successful.
Learn more about these topics we will explore including:
Defining analytics, business intelligence, and data science
Differences in architecture
When to use analytics, business intelligence, or data science
Whether there has been an evolution between analytics, business intelligence, and data science
The document summarizes an information session on data analytics from SMU Business Intelligence and Analytics. It defines data analytics as using analysis and visualization to provide insights and solve problems. It also discusses using machine learning to automate problem solving. The document then provides recommendations on learning tools like SQL, Python, R, and Spark through online courses. It suggests gaining skills in areas like statistics, machine learning, and communication. Finally, it shares one person's journey entering data analytics through studying psychology and business, then working on data analysis projects at IBM and Lazada.
Building an immersive Data Function in Large Scale Organizations.
Data is hard, analytics is hard. Many challenges in both fields have been mastered, but many more lie ahead. One of them is how to establish the combination of both data and analytics as a company function in a large organization. In this talk, I shared insights from the ongoing journey to build a data function at Mercedes-Benz Cars Finance and to embed it into the company’s innermost workings.
This document contains information from a presentation by Anchormen on how they activate data through various services and solutions. It discusses their big data, data platform, data science and AI, and high potential program services. It also includes examples of applications of these services for clients in areas like marketing, logistics, and financial forecasting. The document promotes Anchormen's data-driven approach and circle of data activation methodology.
This study explores the practice of data science by those who practice it. We surveyed over 600 data professionals to understand their data skills, team makeup and more.
This document provides an overview of architecting a digital enterprise. It discusses key aspects of business and technical architecture, including digital products and consumer experience, digital environments, platforms, and reference architectures. The technical architecture section describes a layered "system of systems" reference architecture with systems of engagement, integration, record, and automation, along with an API-enabled digital platform approach.
The document discusses reporting capabilities with SAP cloud solutions. It describes SAP's analytics concept and the types of reporting objects like data sources, reports, key figures, and characteristics. It outlines the integrated analytics capabilities for editing existing reports, designing new reports and data sources, and integrating reports with Microsoft Excel. It also discusses interfaces for connecting external analytics systems and retrieving analytics data using OData web services.
WSO2 Summit London 2018: Lean Digital Agility with WSO2WSO2
In this slide deck, Asanka Abeysinghe, VP of Architecture at WSO2, explores a pragmatic approach to digital transformation with WSO2 that ensures lean digital agility.
This document discusses architecting a digital enterprise through business and technical architecture. It describes how digital products and platforms can provide personalized, real-time experiences for internal and external consumers. The technical architecture uses a layered "PACE" model with systems of engagement, integration, records, and automation. A digital platform shares capabilities across projects through APIs, services, and a decentralized model. Implementing business and technical architecture can transform an organization through digital strategy, leadership, and roadmaps.
Beyond the Basics 3: Introduction to the MongoDB BI ConnectorMongoDB
Watch this presentation to learn how the MongoDB BI Connector lets you use MongoDB as a data source for your SQL-based BI and analytics platforms.
Learn how to seamlessly create the visualizations and dashboards that will help you extract the insights and hidden value in your multi-structured data.
The document discusses artificial intelligence (AI) and data science, including their applications. It defines AI as using machine learning and deep learning to perform human-like tasks by processing large amounts of data. Data science involves analyzing raw data using statistics and machine learning techniques. The document outlines several sources of data in various domains. It then discusses the data science process of acquiring, processing, exploring, modeling, and optimizing data to extract useful insights. Finally, the document lists several applications of AI and data science such as smart gaming, self-driving cars, healthcare, smart homes, and customized reports.
Gartner and other analyst firms rank Pega higher than Salesforce in several areas important for customer service. Specifically:
- Gartner named Pega as the #1 product for customer service in their 2018 Critical Capabilities report.
- Pega outperformed or equaled Salesforce in all areas except social media according to Gartner.
- Other analysts like Forrester ranked Pega higher than Salesforce for capabilities like real-time interaction management and digital decisioning.
- Salesforce was not included in analyst reports for case management, business process management, and other important customer service areas, while Pega was included and ranked highly.
Predictive Analytics World Berlin 2016 Call for SpeakersDatentreiber
Predictive Analytics World is the leading provider of independent specialized conferences in applied predictive analytics. Users, decision makers and experts in predictive analytics will meet in Berlin in order to discover the latest findings and progress, to exchange among specialists and in person and to be inspired by the success stories.
Best Hadoop Institutes : kelly tecnologies is the best Hadoop training Institute in Bangalore.Providing hadoop courses by realtime faculty in Bangalore.
Big Data Is Not Enough - Real-Time Analytics Needs Streaming ArchtecturesDr. Tim Frey
Leveraging IoT data treasure is fate-deciding for enterprises. As more and more decisions get done out of data lake analytics by data scientists, the future are reactive real-time decisions. This presentation shows streaming architectures and visualizes how they work on a conceptual level. Thereby, we describe an example application how the German election and TV duells Twitter mentions got analyzed in real time. Finally, an outlook how to merge data streams of different enterprises concludes the presentation.
German summary:
Das Heben des eigenen (IoT) Datenschatzes ist für Unternehmen derzeit unglaublich wichtig um notwendige Wettbewerbsvorteile zu erlagen. Nachdem immer mehr Wissen aus den eigenen Unternehmensdaten gezogen wird, ist es in Zukunft erfolgsentscheidend in Echtzeit auf Datenänderungen zu reagieren und Datenanalysen zu beschleunigen. Daher zeigt dieser Vortrag sogenannte Streaming Architekturen mit welchen Datenströme und Ereignisse dynamisch und schnell analysiert werden. Um den Streaming Ansatzes mit einer Demo zu verdeutlichen wird die Architektur der Echtzeitanalyse von Twitter zur Bundestagswahl 2017 (https://www.iunera.com/analyse-der-bundestagswahl-2017/) dargestellt. Zuletzt zeigt ein Ausblick wie verschiedene Datenquellen (d.h. von Unternehmen selbst und externen Quellen) durch das Streaming Konzept kombiniert und analysiert werden können.
It’s been second time that google has improved search algorithm and released mysterious update without announcing it officially. Expert name it as "News Wave Algorithm update.,
This document provides an overview of a technology dive on location analytics and 3D mapping. The location analytics portion focuses on integrating data from multiple sources, mapping that data to understand relationships, analyzing the data spatially, enriching it with demographic information, and sharing insights. Key benefits to existing Esri customers include extending GIS capabilities within organizations and leveraging hosted maps and analytics. The session also includes a software demonstration and leaves time for questions.
The world of data analytics has opened up to include a much broader spectrum of data types than the traditional rows and columns found in relational databases. Text analytics includes whole new classes of tools for search and semantic understanding. Speech and image recognition software have become mainstream. How is data analytics changing in scope and practice in the era of Big Data?
This webinar will answer this question by looking at the following:
New tools for leveraging more data types
Differences in Big Data analytics architecture
New directions in Big Data analytics
The document summarizes several data warehousing trends presented by speaker Chris Riccomini. Some of the trends discussed include: real-time data warehousing which handles hard deletes and replay from Kafka; data mesh which advocates for decentralized data ownership and treating data as a product; and headless BI which programs business metrics for use across systems rather than being confined within BI tools. The talk also covered data catalogs, reverse ETL, analytics engineering and other trends.
Moving EA - from where we are to where we should beLeanIX GmbH
Presentation held by Dr. Stefan Zerbe, ITM at EA Connect Days 2018 in Bonn. While EA (enterprise architecture) is a well-known discipline many business organizations struggle with maturity of their corporate EA practice. But even companies that stopped EA activities in recent years now relaunch EA, in order to tackle challenges resulting from digital transformation, regulatory pressure and increasing IT complexity. This presentation builds on lessons learned from companies working with EA and refocuses the EA value proposition in order to meet business expectations. It highlights the importance to extent EA thinking to business units and explains collaboration modelsto engage both, business and IT architecture specialists as well as managers, in joint architecture activities based on a real project example. From a business-oriented perspective on EA, the presentation picks up the discussion regarding a new agile mindset for EA architects and presents new ideas for tool sets to support EA work in corporations.
Analytics, Business Intelligence, and Data Science - What's the Progression?DATAVERSITY
Data analysis can include looking back at historical data, understanding what an organization currently has, and even looking forward to predictions of the future. This presentation will talk about the differences between analytics, business intelligence, and data science, as well as the differences in architecture — and possibly even organization maturity — that make each successful.
Learn more about these topics we will explore including:
Defining analytics, business intelligence, and data science
Differences in architecture
When to use analytics, business intelligence, or data science
Whether there has been an evolution between analytics, business intelligence, and data science
The document summarizes an information session on data analytics from SMU Business Intelligence and Analytics. It defines data analytics as using analysis and visualization to provide insights and solve problems. It also discusses using machine learning to automate problem solving. The document then provides recommendations on learning tools like SQL, Python, R, and Spark through online courses. It suggests gaining skills in areas like statistics, machine learning, and communication. Finally, it shares one person's journey entering data analytics through studying psychology and business, then working on data analysis projects at IBM and Lazada.
Building an immersive Data Function in Large Scale Organizations.
Data is hard, analytics is hard. Many challenges in both fields have been mastered, but many more lie ahead. One of them is how to establish the combination of both data and analytics as a company function in a large organization. In this talk, I shared insights from the ongoing journey to build a data function at Mercedes-Benz Cars Finance and to embed it into the company’s innermost workings.
This document contains information from a presentation by Anchormen on how they activate data through various services and solutions. It discusses their big data, data platform, data science and AI, and high potential program services. It also includes examples of applications of these services for clients in areas like marketing, logistics, and financial forecasting. The document promotes Anchormen's data-driven approach and circle of data activation methodology.
This study explores the practice of data science by those who practice it. We surveyed over 600 data professionals to understand their data skills, team makeup and more.
This document provides an overview of architecting a digital enterprise. It discusses key aspects of business and technical architecture, including digital products and consumer experience, digital environments, platforms, and reference architectures. The technical architecture section describes a layered "system of systems" reference architecture with systems of engagement, integration, record, and automation, along with an API-enabled digital platform approach.
The document discusses reporting capabilities with SAP cloud solutions. It describes SAP's analytics concept and the types of reporting objects like data sources, reports, key figures, and characteristics. It outlines the integrated analytics capabilities for editing existing reports, designing new reports and data sources, and integrating reports with Microsoft Excel. It also discusses interfaces for connecting external analytics systems and retrieving analytics data using OData web services.
WSO2 Summit London 2018: Lean Digital Agility with WSO2WSO2
In this slide deck, Asanka Abeysinghe, VP of Architecture at WSO2, explores a pragmatic approach to digital transformation with WSO2 that ensures lean digital agility.
This document discusses architecting a digital enterprise through business and technical architecture. It describes how digital products and platforms can provide personalized, real-time experiences for internal and external consumers. The technical architecture uses a layered "PACE" model with systems of engagement, integration, records, and automation. A digital platform shares capabilities across projects through APIs, services, and a decentralized model. Implementing business and technical architecture can transform an organization through digital strategy, leadership, and roadmaps.
Beyond the Basics 3: Introduction to the MongoDB BI ConnectorMongoDB
Watch this presentation to learn how the MongoDB BI Connector lets you use MongoDB as a data source for your SQL-based BI and analytics platforms.
Learn how to seamlessly create the visualizations and dashboards that will help you extract the insights and hidden value in your multi-structured data.
The document discusses artificial intelligence (AI) and data science, including their applications. It defines AI as using machine learning and deep learning to perform human-like tasks by processing large amounts of data. Data science involves analyzing raw data using statistics and machine learning techniques. The document outlines several sources of data in various domains. It then discusses the data science process of acquiring, processing, exploring, modeling, and optimizing data to extract useful insights. Finally, the document lists several applications of AI and data science such as smart gaming, self-driving cars, healthcare, smart homes, and customized reports.
The right architecture is key for any IT project. This is especially the case for big data projects, where there are no standard architectures which have proven their suitability over years. This session discusses the different Big Data Architectures which have evolved over time, including traditional Big Data Architecture, Streaming Analytics architecture as well as Lambda and Kappa architecture and presents the mapping of components from both Open Source as well as the Oracle stack onto these architectures.
The right architecture is key for any IT project. This is valid in the case for big data projects as well, but on the other hand there are not yet many standard architectures which have proven their suitability over years.
This session discusses different Big Data Architectures which have evolved over time, including traditional Big Data Architecture, Event Driven architecture as well as Lambda and Kappa architecture.
Each architecture is presented in a vendor- and technology-independent way using a standard architecture blueprint. In a second step, these architecture blueprints are used to show how a given architecture can support certain use cases and which popular open source technologies can help to implement a solution based on a given architecture.
Xinchao(Luke) Lu is a software engineer and data analyst with 2 years of experience in data analysis, visualization, and programming. He has skills in Python, Java, MySQL, JavaScript, Hadoop, Docker, Kubernetes, Tableau, Pyspark, Pentaho, and Pandas. As an intern, he performed statistical analysis to improve product accuracy, cleansed and visualized data, and created surveys and reports. He has a bachelor's degree in computer science from Miami University where he completed projects analyzing Boston housing data, Twitter data, New York weather incidents, and built an information system.
Xinchao(Luke) Lu is a software engineer and data analyst with 2 years of experience in data analysis, data visualization, and software engineering. He has skills in Python, Java, MySQL, JavaScript, Hadoop, Docker, Kubernetes, Tableau, Pyspark, Pentaho, Pandas, and Microservices. He has worked as a data analyst intern at GDS-services and HAIWAN Entertainment, where he performed statistical analysis, data cleansing, and created reports and visualizations. He has a Bachelor's degree in Computer Science from Miami University where he completed projects in linear regression, Twitter analysis, weather analysis, and building an information system website.
Advanced Databases and Knowledge ManagementDATAVERSITY
These days, there are other database technologies at play besides Hadoop. As more raw data is converted to action and knowledge, finding and understanding data requires other kinds of database technology. The days of the single-vendor database environment are over.
Join Kelle and John as they talk about new database management system (DBMS) technology, including some of the unique applications of graph databases, covering:
What is graph?
How is it used?
What are some other promising new database technologies?
Examples of Big Data, analytics and graphs at work
KDD 2019 IADSS Workshop - Research Updates from Usama Fayyad & Hamit HamutcuIADSS
The document discusses the need for standards in defining data science roles and skills. It notes there is currently wide variation in how data science roles are defined by different companies and academic programs. This leads to inefficiencies in recruitment and skills assessment. The Initiative for Analytics and Data Science Standards (IADSS) aims to address this by defining standard roles, skills, and career paths for data science professionals based on research involving industry experts and academics. The goal is to support the growth of the analytics industry by providing clarity around data science qualifications and expectations.
Data Visualization with Microsoft Reporting ServicesChris Price
This document discusses data visualization techniques using Microsoft Reporting Services. It begins with an introduction to data visualization as both an art and science. The document then covers principles of visual design, different types of charts like line charts and bar charts, and how to encode data visually. It provides examples of effective and ineffective data visualization. Finally, it demonstrates capabilities of Reporting Services for creating visualizations and data charts.
Learn SQL from basic queries to Advance queriesmanishkhaire30
Dive into the world of data analysis with our comprehensive guide on mastering SQL! This presentation offers a practical approach to learning SQL, focusing on real-world applications and hands-on practice. Whether you're a beginner or looking to sharpen your skills, this guide provides the tools you need to extract, analyze, and interpret data effectively.
Key Highlights:
Foundations of SQL: Understand the basics of SQL, including data retrieval, filtering, and aggregation.
Advanced Queries: Learn to craft complex queries to uncover deep insights from your data.
Data Trends and Patterns: Discover how to identify and interpret trends and patterns in your datasets.
Practical Examples: Follow step-by-step examples to apply SQL techniques in real-world scenarios.
Actionable Insights: Gain the skills to derive actionable insights that drive informed decision-making.
Join us on this journey to enhance your data analysis capabilities and unlock the full potential of SQL. Perfect for data enthusiasts, analysts, and anyone eager to harness the power of data!
#DataAnalysis #SQL #LearningSQL #DataInsights #DataScience #Analytics
Predictably Improve Your B2B Tech Company's Performance by Leveraging DataKiwi Creative
Harness the power of AI-backed reports, benchmarking and data analysis to predict trends and detect anomalies in your marketing efforts.
Peter Caputa, CEO at Databox, reveals how you can discover the strategies and tools to increase your growth rate (and margins!).
From metrics to track to data habits to pick up, enhance your reporting for powerful insights to improve your B2B tech company's marketing.
- - -
This is the webinar recording from the June 2024 HubSpot User Group (HUG) for B2B Technology USA.
Watch the video recording at https://youtu.be/5vjwGfPN9lw
Sign up for future HUG events at https://events.hubspot.com/b2b-technology-usa/
ViewShift: Hassle-free Dynamic Policy Enforcement for Every Data LakeWalaa Eldin Moustafa
Dynamic policy enforcement is becoming an increasingly important topic in today’s world where data privacy and compliance is a top priority for companies, individuals, and regulators alike. In these slides, we discuss how LinkedIn implements a powerful dynamic policy enforcement engine, called ViewShift, and integrates it within its data lake. We show the query engine architecture and how catalog implementations can automatically route table resolutions to compliance-enforcing SQL views. Such views have a set of very interesting properties: (1) They are auto-generated from declarative data annotations. (2) They respect user-level consent and preferences (3) They are context-aware, encoding a different set of transformations for different use cases (4) They are portable; while the SQL logic is only implemented in one SQL dialect, it is accessible in all engines.
#SQL #Views #Privacy #Compliance #DataLake
Open Source Contributions to Postgres: The Basics POSETTE 2024ElizabethGarrettChri
Postgres is the most advanced open-source database in the world and it's supported by a community, not a single company. So how does this work? How does code actually get into Postgres? I recently had a patch submitted and committed and I want to share what I learned in that process. I’ll give you an overview of Postgres versions and how the underlying project codebase functions. I’ll also show you the process for submitting a patch and getting that tested and committed.
4th Modern Marketing Reckoner by MMA Global India & Group M: 60+ experts on W...Social Samosa
The Modern Marketing Reckoner (MMR) is a comprehensive resource packed with POVs from 60+ industry leaders on how AI is transforming the 4 key pillars of marketing – product, place, price and promotions.
Beyond the Basics of A/B Tests: Highly Innovative Experimentation Tactics You...Aggregage
This webinar will explore cutting-edge, less familiar but powerful experimentation methodologies which address well-known limitations of standard A/B Testing. Designed for data and product leaders, this session aims to inspire the embrace of innovative approaches and provide insights into the frontiers of experimentation!
Codeless Generative AI Pipelines
(GenAI with Milvus)
https://ml.dssconf.pl/user.html#!/lecture/DSSML24-041a/rate
Discover the potential of real-time streaming in the context of GenAI as we delve into the intricacies of Apache NiFi and its capabilities. Learn how this tool can significantly simplify the data engineering workflow for GenAI applications, allowing you to focus on the creative aspects rather than the technical complexities. I will guide you through practical examples and use cases, showing the impact of automation on prompt building. From data ingestion to transformation and delivery, witness how Apache NiFi streamlines the entire pipeline, ensuring a smooth and hassle-free experience.
Timothy Spann
https://www.youtube.com/@FLaNK-Stack
https://medium.com/@tspann
https://www.datainmotion.dev/
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Global Situational Awareness of A.I. and where its headedvikram sood
You can see the future first in San Francisco.
Over the past year, the talk of the town has shifted from $10 billion compute clusters to $100 billion clusters to trillion-dollar clusters. Every six months another zero is added to the boardroom plans. Behind the scenes, there’s a fierce scramble to secure every power contract still available for the rest of the decade, every voltage transformer that can possibly be procured. American big business is gearing up to pour trillions of dollars into a long-unseen mobilization of American industrial might. By the end of the decade, American electricity production will have grown tens of percent; from the shale fields of Pennsylvania to the solar farms of Nevada, hundreds of millions of GPUs will hum.
The AGI race has begun. We are building machines that can think and reason. By 2025/26, these machines will outpace college graduates. By the end of the decade, they will be smarter than you or I; we will have superintelligence, in the true sense of the word. Along the way, national security forces not seen in half a century will be un-leashed, and before long, The Project will be on. If we’re lucky, we’ll be in an all-out race with the CCP; if we’re unlucky, an all-out war.
Everyone is now talking about AI, but few have the faintest glimmer of what is about to hit them. Nvidia analysts still think 2024 might be close to the peak. Mainstream pundits are stuck on the wilful blindness of “it’s just predicting the next word”. They see only hype and business-as-usual; at most they entertain another internet-scale technological change.
Before long, the world will wake up. But right now, there are perhaps a few hundred people, most of them in San Francisco and the AI labs, that have situational awareness. Through whatever peculiar forces of fate, I have found myself amongst them. A few years ago, these people were derided as crazy—but they trusted the trendlines, which allowed them to correctly predict the AI advances of the past few years. Whether these people are also right about the next few years remains to be seen. But these are very smart people—the smartest people I have ever met—and they are the ones building this technology. Perhaps they will be an odd footnote in history, or perhaps they will go down in history like Szilard and Oppenheimer and Teller. If they are seeing the future even close to correctly, we are in for a wild ride.
Let me tell you what we see.
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A Data scientist vs an Analyst
1. Data Scientist vs Analyst
23-11-2017
Who are they?
What do they do?
How are they different?
Where are they involved?
www.akshaysehgal.com
2. Who is a Data Scientist or an Analyst?
Programming
+ Maths
---------------------
Jr. Data Scientist
Mathematics
Programming
+ Maths
+ Communication
+ Business
---------------------
Sr. Data Scientist
Maths
+ Communication
+ Business
---------------------
Analyst
Reference: https://goo.gl/d3VfaV23-11-2017 www.akshaysehgal.com
3. Programming Mathematics Business Communication
Python Statistics Products Brainstorming
R Calculus Services Ideation
Micro-services Linear Algebra Research Presentation
Data Extraction Probability Talkative!
Applied Maths
What does it take to be a Data Scientist or Analyst?
Programming Mathematics Business Communication
R Statistics Services Brainstorming
SAS Probability Dash-boarding Ideation
SQL Applied Maths Presentation
Talkative!
23-11-2017 www.akshaysehgal.com
4. How to distinguish a Data Scientist from an Analyst profile?
have worked on topics like
Data Scientists
Analysts
Image Processing, NLP, Neural Networks, Adv. Machine Learning…
Regression, Decision trees, Hypothesis testing, Visualization…
have core experience in
Data Scientists
Analysts
Products and Research
Dash-boarding and Services
have higher education in
Data Scientists
Analysts
Computer Engg, M.A. Mathematics/Statistics
M.A Statistics, Business Analytics, MBA
23-11-2017 www.akshaysehgal.com
5. Where do Data Scientists or Analysts fit in the product lifecycle?
Concept > Research > Proof of Concept > Architecture > Implementation > Deployment > Test > Support
This is where a Data Scientist should be involved!
This is where an Analyst should be involved!
23-11-2017 www.akshaysehgal.com