This document is an excerpt from a book that discusses business intelligence, data science, artificial intelligence and their role in creating business value. It includes chapters on topics such as the difference between business intelligence and data science, the evolution to data lakehouses, data literacy, delivering insights with modern data platforms, and building competent data teams. Each chapter is authored by a different expert in the data and analytics field.
Abstract. Enterprise adoption of AI/ML services has significantly accelerated in the last few years. However, the majority of ML models are still developed with the goal of solving a single task, e.g., predictiction, classification. In this talk, Debmalya Biswas will present the emerging paradigm of Compositional AI, also known as, Compositional Learning. Compositional AI envisions seamless composition of existing AI/ML services, to provide a new (composite) AI/ML service, capable of addressing complex multi-domain use-cases. In an enterprise context, this enables reuse, agility, and efficiency in development and maintenance efforts.
Value Amplify Consulting Group, offers the opportunity to hire Chief AI Officers trained to lead your organization in the following services, roadmaps and create your AI Playbook
Maximize the Value of Your Data: Neo4j Graph Data PlatformNeo4j
In this 60-minute conversation with IDC, we will highlight the momentum and reasons why a graph data platform is a breakthrough solution for businesses in need of a flexible data model.
Please join Mohit Sagar, Group Managing Director of CIO Network, as he hosts the conversation with Dr. Christopher Lee Marshall, Associate VP at IDC, and Nik Vora, Vice President of APAC at Neo4. During this very exciting discussion, you'll discover the insights and knowledge unlocked with the graph data platform.
DATA SCIENCE IS CATALYZING BUSINESS AND INNOVATION Elvis Muyanja
Today, data science is enabling companies, governments, research centres and other organisations to turn their volumes of big data into valuable and actionable insights. It is important to uncover hidden patterns, unknown correlations, market trends, customer preferences and other useful business information. According to the McKinsey Global Institute, the U.S. alone could face a shortage of about 190,000 data scientists and 1.5 million managers and analysts who can understand and make decisions using big data by 2018. In coming years, data scientists will be vital to all sectors —from law and medicine to media and nonprofits. Has the African continent planned to train the next generation of data scientists required on the continent?
Sakha Global - Artificial Intelligence in Customer ServiceSakha Global
Delivering on increasing customer expectations is possible with the help of practical application of artificial intelligence for customer service, by combining the best of human and machine intelligence. As artificial intelligence technology matures, businesses are including it in their strategic investment roadmaps. Customer service is an area where a lot of attention is being paid to reap efficiency gains. This slideshare presents some of the ways that businesses are augmenting their customer care units with AI and how Sakha Global can help.
"Industrializing Machine Learning – How to Integrate ML in Existing Businesse...Dataconomy Media
"Industrializing Machine Learning – How to Integrate ML in Existing Businesses", Erik Schmiegelow, CEO at Hivemind Technologies AG
Watch more from Data Natives Berlin 2016 here: http://bit.ly/2fE1sEo
Visit the conference website to learn more: www.datanatives.io
Follow Data Natives:
https://www.facebook.com/DataNatives
https://twitter.com/DataNativesConf
Stay Connected to Data Natives by Email: Subscribe to our newsletter to get the news first about Data Natives 2017: http://bit.ly/1WMJAqS
About the Author:
Since 1996, Erik Schmiegelow has worked as a software architecht and consultant, building large data processing platforms for companies such as NTT DoCoMo, Royal Mail, Siemens, E-Plus, Allianz and T-Mobile; and until 2001 he was CTO at the Cologne-based digital agency denkwerk.
In 2007 he founded the telecommunications consulting agency Itellity, followed by Hivemind Technologies in 2014. Hivemind Technologies is a solutions and services company, focussed on big data analytics and stream processing technologies for web, social data and industrial applications. Erik studied computer sciences in Hamburg.
Abstract. Enterprise adoption of AI/ML services has significantly accelerated in the last few years. However, the majority of ML models are still developed with the goal of solving a single task, e.g., predictiction, classification. In this talk, Debmalya Biswas will present the emerging paradigm of Compositional AI, also known as, Compositional Learning. Compositional AI envisions seamless composition of existing AI/ML services, to provide a new (composite) AI/ML service, capable of addressing complex multi-domain use-cases. In an enterprise context, this enables reuse, agility, and efficiency in development and maintenance efforts.
Value Amplify Consulting Group, offers the opportunity to hire Chief AI Officers trained to lead your organization in the following services, roadmaps and create your AI Playbook
Maximize the Value of Your Data: Neo4j Graph Data PlatformNeo4j
In this 60-minute conversation with IDC, we will highlight the momentum and reasons why a graph data platform is a breakthrough solution for businesses in need of a flexible data model.
Please join Mohit Sagar, Group Managing Director of CIO Network, as he hosts the conversation with Dr. Christopher Lee Marshall, Associate VP at IDC, and Nik Vora, Vice President of APAC at Neo4. During this very exciting discussion, you'll discover the insights and knowledge unlocked with the graph data platform.
DATA SCIENCE IS CATALYZING BUSINESS AND INNOVATION Elvis Muyanja
Today, data science is enabling companies, governments, research centres and other organisations to turn their volumes of big data into valuable and actionable insights. It is important to uncover hidden patterns, unknown correlations, market trends, customer preferences and other useful business information. According to the McKinsey Global Institute, the U.S. alone could face a shortage of about 190,000 data scientists and 1.5 million managers and analysts who can understand and make decisions using big data by 2018. In coming years, data scientists will be vital to all sectors —from law and medicine to media and nonprofits. Has the African continent planned to train the next generation of data scientists required on the continent?
Sakha Global - Artificial Intelligence in Customer ServiceSakha Global
Delivering on increasing customer expectations is possible with the help of practical application of artificial intelligence for customer service, by combining the best of human and machine intelligence. As artificial intelligence technology matures, businesses are including it in their strategic investment roadmaps. Customer service is an area where a lot of attention is being paid to reap efficiency gains. This slideshare presents some of the ways that businesses are augmenting their customer care units with AI and how Sakha Global can help.
"Industrializing Machine Learning – How to Integrate ML in Existing Businesse...Dataconomy Media
"Industrializing Machine Learning – How to Integrate ML in Existing Businesses", Erik Schmiegelow, CEO at Hivemind Technologies AG
Watch more from Data Natives Berlin 2016 here: http://bit.ly/2fE1sEo
Visit the conference website to learn more: www.datanatives.io
Follow Data Natives:
https://www.facebook.com/DataNatives
https://twitter.com/DataNativesConf
Stay Connected to Data Natives by Email: Subscribe to our newsletter to get the news first about Data Natives 2017: http://bit.ly/1WMJAqS
About the Author:
Since 1996, Erik Schmiegelow has worked as a software architecht and consultant, building large data processing platforms for companies such as NTT DoCoMo, Royal Mail, Siemens, E-Plus, Allianz and T-Mobile; and until 2001 he was CTO at the Cologne-based digital agency denkwerk.
In 2007 he founded the telecommunications consulting agency Itellity, followed by Hivemind Technologies in 2014. Hivemind Technologies is a solutions and services company, focussed on big data analytics and stream processing technologies for web, social data and industrial applications. Erik studied computer sciences in Hamburg.
Protecting data privacy in analytics and machine learning ISACA London UKUlf Mattsson
ISACA London Chapter webinar, Feb 16th 2021
Topic: “Protecting Data Privacy in Analytics and Machine Learning”
Abstract:
In this session, we will discuss a range of new emerging technologies for privacy and confidentiality in machine learning and data analytics. We will discuss how to put these technologies to work for databases and other data sources.
When we think about developing AI responsibly, there’s many different activities that we need to think about.
This session also discusses international standards and emerging privacy-enhanced computation techniques, secure multiparty computation, zero trust, cloud and trusted execution environments. We will discuss the “why, what, and how” of techniques for privacy preserving computing.
We will review how different industries are taking opportunity of these privacy preserving techniques. A retail company used secure multi-party computation to be able to respect user privacy and specific regulations and allow the retailer to gain insights while protecting the organization’s IP. Secure data-sharing is used by a healthcare organization to protect the privacy of individuals and they also store and search on encrypted medical data in cloud.
We will also review the benefits of secure data-sharing for financial institutions including a large bank that wanted to broaden access to its data lake without compromising data privacy but preserving the data’s analytical quality for machine learning purposes.
Advanced Analytics and Data Science ExpertiseSoftServe
An overview of SoftServe's Data Science service line.
- Data Science Group
- Data Science Offerings for Business
- Machine Learning Overview
- AI & Deep Learning Case Studies
- Big Data & Analytics Case Studies
Visit our website to learn more: http://www.softserveinc.com/en-us/
Do you have a true Big Data Analytics platform? What's a true Big Data Analytics platform? How can it help capitalize big data? What's needed to build one? This short introductory presentation can help understand what's a true Big Data Analytics platform and how it really helps building Big Data Analytics applications.
The Role of the Logical Data Fabric in a Unified Platform for Modern AnalyticsDenodo
Watch full webinar here: https://bit.ly/3FHKalT
Given the growing demand for analytics and the need for organizations to advance beyond dashboards to self-service analytics and more sophisticated algorithms like machine learning (ML), enterprises are moving towards a unified environment for data and analytics. What is the best approach to accomplish this unification?
In TDWI’s recent Best Practice Report, Unified Platforms for Modern Analytics, written by Fern Halper, TDWI VP Research, Senior Research Director for Advanced Analytics, adoption, use, challenges, architectures, and best practices for unified platforms for modern analytics is explored. One of the approaches for unification outlined in the report is a data fabric approach.
Join us for a webinar with our Director of Product Marketing, Robin Tandon, where he will discuss the role of the logical data fabric in a unified platform for modern analytics, focusing on several of the key findings outlined in this report. He will share insights and use case examples that demonstrate how a properly implemented logical data fabric is the most suitable approach for Unified Data Platforms across enterprises and organizations.
Watch on-demand & Learn:
- The benefits of a unified platform and its ability to capture diverse & emerging data types and how to support high performance and scalable solutions.
- The role of an enhanced AI driven data catalog and its implications towards the findings in the best practice report.
- Implications of a logical data fabric as it relates to several of the recommendations outlined in the report.
A conference report of SemTechBiz 2013 in San Francisco, from a datamining and knowledge-management point of view. It covers several companies with their automatic algorithms to extract data from cleverly discovered crowed-curated data sources, or using UI tools to leverage existing utility to lure user help mark up the data...
Enterprise Data Governance: Leveraging Knowledge Graph & AI in support of a d...Connected Data World
As one of the largest financial institutions worldwide, JP Morgan is reliant on data to drive its day-to-day operations, against an ever evolving regulatory regime. Our global data landscape possesses particular challenges of effectively maintaining data governance and metadata management.
The Data strategy at JP Morgan intends to:
a) generate business value
b) adhere to regulatory & compliance requirements
c) reduce barriers to access
d) democratize access to data
In this talk, we show how JP Morgan leverages semantic technologies to drive the implementation of our data strategy. We demonstrate how we exploit knowledge graph capabilities to answer:
1) What Data do I need?
2) What Data do we have?
3) Where does my Data come from?
4) Where should my Data come from?
5) What Data should be shared most?
Smart Data Webinar: Machine Learning UpdateDATAVERSITY
Machine Learning (ML) approaches and their supporting technologies can generally be classified as Supervised vs Unsupervised, and within those categories as General or Deep Learning (with Reinforcement Learning as a special case within Supervised Learning). The approaches may be based on biological models or statistical models, or hybrids. As demand for machine learning functionality in consumer and enterprise applications increases, it becomes important to have a framework for comparing ML products and services.
This webinar will present an overview of the machine learning landscape, from platform providers to point solutions in each major ML category, and help participants understand their options for experimentation and deployment of ML-based applications.
A Connections-first Approach to Supply Chain OptimizationNeo4j
Supply chain optimization is an unusual balancing act that requires finesse, skill and timely data. Every supply chain’s the key questions to be answered are:
What to Buy? -- what are the factors in determining your optimal product mix and set of suppliers.
How much to Buy? -- what are the most and least popular items at any given time interval
When to Buy? -- long lags in delivery timing may tax limit your flexibility and influence your inventory management practices.
We will illustrate an API-based solution that utilizes a Graph database platform to add demonstrable value to Supply Planning.
“Artificial Intelligence” covers a wide range of technologies today, including those that enable machine vision, effective computing, deep learning, and natural language processing. As advances increase, so do expectations. We now see a rush to add “AI inside” for applications and appliances in almost every domain. The reality is that some firms will have mega-hits with AI-enabled applications, and many more will suffer setbacks based on flawed adoption strategies.
This webinar will present an assessment of key AI technologies today, and help participants identify promising applications based on matching requirements to mature-enough technologies.
Performance Acceleration: Summaries, Recommendation, MPP and moreDenodo
Watch full webinar here: https://bit.ly/3nLHayP
Performance is critical for an organization across the board. Developers can optimize execution with Summaries, MPP, Data Movement, and more. Business users rely on the Recommendation engine to guide them to the right data. Let’s discover and learn about various performance acceleration techniques in this session.
Automating Data Science over a Human Genomics Knowledge BaseVaticle
# Automating Data Science over a Human Genomics Knowledge Base
Radouane Oudrhiri, the CTO of Eagle Genomics, will talk about how Eagle Genomics is building a platform for automating data science over a human genomics knowledge base. Rad will dive into the architecture Eagle Genomics and also discuss how Grakn serves as the knowledge base foundation of the system. Rad also give a brief history of databases, semantic expressiveness and how Grakn fits in the big picture.
# Radouane Oudrhiri, CTO, Eagle Genomics
Radouane has an extensive experience in leading world-class software and data-intensive system developments in different industries from Telecom to Healthcare, Nuclear, Automotive, Financials. Radouane is Lean/Six Sigma Master Black Belt with speciality in high-tech, IT and Software engineering and he is recognised as the leader and early adaptor of Lean/Six Sigma and DFSS to IT and Software. He is a fellow of the Royal Statistical Society (RSS) and member of the ISO Technical Committee (TC69: Applications of Statistical methods) where he is co-author of the Lean & Six Sigma Standard (ISO 18404) as well as the new standard under development (Design for Six Sigma). He is also part of the newly formed international Group on Big Data (nominated by BSI as the UK representative/expert). Radouane has also been Chair of the working group on Measurement Systems for Automated Processes/Systems within the ISPE (International Society for Pharmaceutical Engineering).
Data Mining With Excel 2007 And SQL Server 2008Mark Tabladillo
Introduction to Excel 2007 Data Mining Plug-In using SQL Server 2008. The presentation starts with definitions and statistical theory (without equations). Then, the audience interactively participates in four demos showing the power and possibilities of the Microsoft Data Mining Algorithms.
Big data is a huge volume of heterogenous data often generated at high speed.Big data cannot be handles with traditional data analytic tools. Hadoop is one of the mostly used big data analytic tool.Map Reduce, hive, hbase are also the tools for analysis in big data.
IBM Governed data lake is a value-driven big data platform journey. The journey starts by ingesting wide variety of data, governing it, applying data science and machine learning on it to produce actionable insights.
Data Architecture Strategies Webinar: Emerging Trends in Data Architecture – ...DATAVERSITY
A robust data architecture is at the core what’s driving today’s innovative, data-driven organizations. From AI to machine learning to Big Data – a strong data architecture is needed in order to be successful, and core fundamentals such as data quality, metadata management, and efficient data storage are more critical than ever.
With the vast array of new technologies available to support these trends, how do you make sense of it all? Our panel of experts will offer their perspectives on how the latest trends in data architecture can support your organization’s data-driven goals.
Protecting data privacy in analytics and machine learning ISACA London UKUlf Mattsson
ISACA London Chapter webinar, Feb 16th 2021
Topic: “Protecting Data Privacy in Analytics and Machine Learning”
Abstract:
In this session, we will discuss a range of new emerging technologies for privacy and confidentiality in machine learning and data analytics. We will discuss how to put these technologies to work for databases and other data sources.
When we think about developing AI responsibly, there’s many different activities that we need to think about.
This session also discusses international standards and emerging privacy-enhanced computation techniques, secure multiparty computation, zero trust, cloud and trusted execution environments. We will discuss the “why, what, and how” of techniques for privacy preserving computing.
We will review how different industries are taking opportunity of these privacy preserving techniques. A retail company used secure multi-party computation to be able to respect user privacy and specific regulations and allow the retailer to gain insights while protecting the organization’s IP. Secure data-sharing is used by a healthcare organization to protect the privacy of individuals and they also store and search on encrypted medical data in cloud.
We will also review the benefits of secure data-sharing for financial institutions including a large bank that wanted to broaden access to its data lake without compromising data privacy but preserving the data’s analytical quality for machine learning purposes.
Advanced Analytics and Data Science ExpertiseSoftServe
An overview of SoftServe's Data Science service line.
- Data Science Group
- Data Science Offerings for Business
- Machine Learning Overview
- AI & Deep Learning Case Studies
- Big Data & Analytics Case Studies
Visit our website to learn more: http://www.softserveinc.com/en-us/
Do you have a true Big Data Analytics platform? What's a true Big Data Analytics platform? How can it help capitalize big data? What's needed to build one? This short introductory presentation can help understand what's a true Big Data Analytics platform and how it really helps building Big Data Analytics applications.
The Role of the Logical Data Fabric in a Unified Platform for Modern AnalyticsDenodo
Watch full webinar here: https://bit.ly/3FHKalT
Given the growing demand for analytics and the need for organizations to advance beyond dashboards to self-service analytics and more sophisticated algorithms like machine learning (ML), enterprises are moving towards a unified environment for data and analytics. What is the best approach to accomplish this unification?
In TDWI’s recent Best Practice Report, Unified Platforms for Modern Analytics, written by Fern Halper, TDWI VP Research, Senior Research Director for Advanced Analytics, adoption, use, challenges, architectures, and best practices for unified platforms for modern analytics is explored. One of the approaches for unification outlined in the report is a data fabric approach.
Join us for a webinar with our Director of Product Marketing, Robin Tandon, where he will discuss the role of the logical data fabric in a unified platform for modern analytics, focusing on several of the key findings outlined in this report. He will share insights and use case examples that demonstrate how a properly implemented logical data fabric is the most suitable approach for Unified Data Platforms across enterprises and organizations.
Watch on-demand & Learn:
- The benefits of a unified platform and its ability to capture diverse & emerging data types and how to support high performance and scalable solutions.
- The role of an enhanced AI driven data catalog and its implications towards the findings in the best practice report.
- Implications of a logical data fabric as it relates to several of the recommendations outlined in the report.
A conference report of SemTechBiz 2013 in San Francisco, from a datamining and knowledge-management point of view. It covers several companies with their automatic algorithms to extract data from cleverly discovered crowed-curated data sources, or using UI tools to leverage existing utility to lure user help mark up the data...
Enterprise Data Governance: Leveraging Knowledge Graph & AI in support of a d...Connected Data World
As one of the largest financial institutions worldwide, JP Morgan is reliant on data to drive its day-to-day operations, against an ever evolving regulatory regime. Our global data landscape possesses particular challenges of effectively maintaining data governance and metadata management.
The Data strategy at JP Morgan intends to:
a) generate business value
b) adhere to regulatory & compliance requirements
c) reduce barriers to access
d) democratize access to data
In this talk, we show how JP Morgan leverages semantic technologies to drive the implementation of our data strategy. We demonstrate how we exploit knowledge graph capabilities to answer:
1) What Data do I need?
2) What Data do we have?
3) Where does my Data come from?
4) Where should my Data come from?
5) What Data should be shared most?
Smart Data Webinar: Machine Learning UpdateDATAVERSITY
Machine Learning (ML) approaches and their supporting technologies can generally be classified as Supervised vs Unsupervised, and within those categories as General or Deep Learning (with Reinforcement Learning as a special case within Supervised Learning). The approaches may be based on biological models or statistical models, or hybrids. As demand for machine learning functionality in consumer and enterprise applications increases, it becomes important to have a framework for comparing ML products and services.
This webinar will present an overview of the machine learning landscape, from platform providers to point solutions in each major ML category, and help participants understand their options for experimentation and deployment of ML-based applications.
A Connections-first Approach to Supply Chain OptimizationNeo4j
Supply chain optimization is an unusual balancing act that requires finesse, skill and timely data. Every supply chain’s the key questions to be answered are:
What to Buy? -- what are the factors in determining your optimal product mix and set of suppliers.
How much to Buy? -- what are the most and least popular items at any given time interval
When to Buy? -- long lags in delivery timing may tax limit your flexibility and influence your inventory management practices.
We will illustrate an API-based solution that utilizes a Graph database platform to add demonstrable value to Supply Planning.
“Artificial Intelligence” covers a wide range of technologies today, including those that enable machine vision, effective computing, deep learning, and natural language processing. As advances increase, so do expectations. We now see a rush to add “AI inside” for applications and appliances in almost every domain. The reality is that some firms will have mega-hits with AI-enabled applications, and many more will suffer setbacks based on flawed adoption strategies.
This webinar will present an assessment of key AI technologies today, and help participants identify promising applications based on matching requirements to mature-enough technologies.
Performance Acceleration: Summaries, Recommendation, MPP and moreDenodo
Watch full webinar here: https://bit.ly/3nLHayP
Performance is critical for an organization across the board. Developers can optimize execution with Summaries, MPP, Data Movement, and more. Business users rely on the Recommendation engine to guide them to the right data. Let’s discover and learn about various performance acceleration techniques in this session.
Automating Data Science over a Human Genomics Knowledge BaseVaticle
# Automating Data Science over a Human Genomics Knowledge Base
Radouane Oudrhiri, the CTO of Eagle Genomics, will talk about how Eagle Genomics is building a platform for automating data science over a human genomics knowledge base. Rad will dive into the architecture Eagle Genomics and also discuss how Grakn serves as the knowledge base foundation of the system. Rad also give a brief history of databases, semantic expressiveness and how Grakn fits in the big picture.
# Radouane Oudrhiri, CTO, Eagle Genomics
Radouane has an extensive experience in leading world-class software and data-intensive system developments in different industries from Telecom to Healthcare, Nuclear, Automotive, Financials. Radouane is Lean/Six Sigma Master Black Belt with speciality in high-tech, IT and Software engineering and he is recognised as the leader and early adaptor of Lean/Six Sigma and DFSS to IT and Software. He is a fellow of the Royal Statistical Society (RSS) and member of the ISO Technical Committee (TC69: Applications of Statistical methods) where he is co-author of the Lean & Six Sigma Standard (ISO 18404) as well as the new standard under development (Design for Six Sigma). He is also part of the newly formed international Group on Big Data (nominated by BSI as the UK representative/expert). Radouane has also been Chair of the working group on Measurement Systems for Automated Processes/Systems within the ISPE (International Society for Pharmaceutical Engineering).
Data Mining With Excel 2007 And SQL Server 2008Mark Tabladillo
Introduction to Excel 2007 Data Mining Plug-In using SQL Server 2008. The presentation starts with definitions and statistical theory (without equations). Then, the audience interactively participates in four demos showing the power and possibilities of the Microsoft Data Mining Algorithms.
Big data is a huge volume of heterogenous data often generated at high speed.Big data cannot be handles with traditional data analytic tools. Hadoop is one of the mostly used big data analytic tool.Map Reduce, hive, hbase are also the tools for analysis in big data.
IBM Governed data lake is a value-driven big data platform journey. The journey starts by ingesting wide variety of data, governing it, applying data science and machine learning on it to produce actionable insights.
Data Architecture Strategies Webinar: Emerging Trends in Data Architecture – ...DATAVERSITY
A robust data architecture is at the core what’s driving today’s innovative, data-driven organizations. From AI to machine learning to Big Data – a strong data architecture is needed in order to be successful, and core fundamentals such as data quality, metadata management, and efficient data storage are more critical than ever.
With the vast array of new technologies available to support these trends, how do you make sense of it all? Our panel of experts will offer their perspectives on how the latest trends in data architecture can support your organization’s data-driven goals.
Building a Data Strategy – Practical Steps for Aligning with Business GoalsDATAVERSITY
Developing a Data Strategy for your organization can seem like a daunting task – but it’s worth the effort. Getting your Data Strategy right can provide significant value, as data drives many of the key initiatives in today’s marketplace, from digital transformation to marketing, customer centricity, population health, and more. This webinar will help demystify Data Strategy and its relationship to Data Architecture and will provide concrete, practical ways to get started.
Data Modeling Best Practices - Business & Technical ApproachesDATAVERSITY
Data Modeling is hotter than ever, according to a number of recent surveys. Part of the appeal of data models lies in their ability to translate complex data concepts in an intuitive, visual way to both business and technical stakeholders. This webinar provides real-world best practices in using Data Modeling for both business and technical teams.
Improving Data Literacy Around Data ArchitectureDATAVERSITY
Data Literacy is an increasing concern, as organizations look to become more data-driven. As the rise of the citizen data scientist and self-service data analytics becomes increasingly common, the need for business users to understand core Data Management fundamentals is more important than ever. At the same time, technical roles need a strong foundation in Data Architecture principles and best practices. Join this webinar to understand the key components of Data Literacy, and practical ways to implement a Data Literacy program in your organization.
Emerging Trends in Data Architecture – What’s the Next Big ThingDATAVERSITY
Digital Transformation is a top priority for many organizations, and a successful digital journey requires a strong data foundation. Creating this digital transformation requires a number of core data management capabilities such as MDM, With technological innovation and change occurring at an ever-increasing rate, it’s hard to keep track of what’s hype and what can provide practical value for your organization. Join this webinar to see the results of a recent DATAVERSITY survey on emerging trends in Data Architecture, along with practical commentary and advice from industry expert Donna Burbank.
Graph databases provide the ability to quickly discover and integrate key relationships between enterprise data sets. Business use cases such as recommendation engines, social networks, enterprise knowledge graphs, and more provide valuable ways to leverage graph databases in your organization. This webinar will provide an overview of graph database technologies, and how they can be used for practical applications to drive business value.
Building a Data Strategy – Practical Steps for Aligning with Business GoalsDATAVERSITY
Developing a Data Strategy for your organization can seem like a daunting task – but it’s worth the effort. Getting your Data Strategy right can provide significant value, as data drives many of the key initiatives in today’s marketplace, from digital transformation to marketing, customer centricity, population health, and more. This webinar will help demystify Data Strategy and its relationship to Data Architecture and will provide concrete, practical ways to get started.
In this world of digitalization, technologies are advancing rapidly. As the world’s foremost tech news contributor, it is our duty to keep everyone updated with the latest top 10 trending technologies in 2021.
20 Emerging influencers in 2020 for big dataRiver11river
You might have not heard most of these names yet, but you surely will soon. This list is designed to recognize emerging talent in the fields of data and analytics – mostly entrepreneurs and up-and-coming talent who are informing, educating and inspiring others through data. They come from different sectors and backgrounds – from data architecture to visualization. The one thing that unites them is their passion for data.
A data analyst collects, cleans, and interprets data sets to provide an answer or solve an issue. They work in a variety of industries, including business, banking, criminal justice, science, medical, and government. Data analyst jobs in US can take various shapes depending on the question you're attempting to answer. Being a data analyst might also lead to other employment opportunities. Many data analysts eventually become data scientists.
MICROSOFT WORD RESEARCH ASSIGNMENT BaqibillahSOFTWARE DDioneWang844
MICROSOFT WORD RESEARCH ASSIGNMENT Baqibillah
SOFTWARE DEVELOPING AND DATA SCIENCE QB 2
Software Developing and Data Science: Microsoft Word Assignment
QBPrince George’s Community College
INT-1010-LD29
Professor Manzoor Hossain
November 29, 2020
Abstract
Software developing and data science are both great career options in the technology field. The minimum education level required to become either a software developer or data scientist is not very low. This makes the careers prestigious and well-paying ones. There are also plenty of emerging technologies being used to improve them. However, these jobs are not for everyone because the duties involved are often challenging. There are also some ethical issues linked to them that should be considered. The pros and cons of each career can be weighed according to the education level required, salary earned, and duties performed. Overall, both data science and software developing are brilliant job options in the technology field.
Table of Contents
Software Developing and Data Science: Microsoft Word Assignment1
Abstract2
Software Developing and Data Science3
Pros and Cons of Each Career7
Pros and Cons of being a Software developer:7
Pros and Cons of Being a Data Scientist:7
References9
Software Developing and Data Science
Software developers are people who develop applications and systems on computers and other devices.[footnoteRef:1] According to the U.S. Bureau of Labor Statistics, there are two different types of software developers, applications software developers and system software developers (Bureau of Labor Statistics, 2020). Application software developers create custom and commercial software, and they also design applications for computers (Bureau of Labor Statistics, 2020). Software developers make the systems to keep computers working properly and also create the system interfaces (Bureau of Labor Statistics, 2020). They make operating systems both for specific organizations and for the general public (Bureau of Labor Statistics, 2020). The required education level to become a software developer is a minimum of a bachelor’s degree in either software engineering, computer science, or another related area of study (Bureau of Labor Statistics, 2020). [1: A computer is a device used by software developers. A 3D model of a computer is shown above.]
Software developing is a challenging job but pays well. According to the National Center for O*NET Development, software developers in Maryland earn $112,490 on average (National Center for O*NET Development, 2020)[footnoteRef:2]. Artificial intelligence is an emerging technology making the job easier for software developers. According to the article How AI Is Making Software Development Easier For Companies And Coders by Simon Chandler, “software developers can use AI to write and review code, detect bugs, test software, and even optimize development projects” (Chandler, 2020). AI is a very useful in software development, but there ar ...
Key Lessons from 15 Data Leaders & Industry ExpertsBernard Marr
A new collaborative book project from the makers of leading semantic layer platform, AtScale, offers collective advice from a host of specialists from data science and business intelligence.
Top 10 tredning technologies to learn in 2021Lokesh Agarwal
In this world of digitalization, technologies are expanding rapidly. As the world foremost tech news contributor, it is the duty of us to keep everyone updated with the newest trends of the top 10 trending technologies in 2021. Technology and programming language are so important in day to day lifestyle to make the livelihood more facile. These computer scientists and professionals are regularly making the bests out of anything. Technology has taken a face of more productiveness and give the best to the nation. In the present scenario, everything is done through the technical process, you don’t have to bother about doing work, everything will be done automatically. In this article, some important technologies which are new in the market are explained according to the career preferences. So let’s have a look into the top 10 trending technologies in2021 and its impression in the coming future.
DAS Slides: Building a Data Strategy — Practical Steps for Aligning with Busi...DATAVERSITY
Developing a Data Strategy for your organization can seem like a daunting task. The opportunity in getting it right can be significant, however, as data drives many of the key initiatives in today’s marketplace from digital transformation, to marketing, to customer centricity, population health, and more. This webinar will help de-mystify data strategy and data architecture and will provide concrete, practical ways to get started.
Business Intelligence & Data Analytics– An Architected ApproachDATAVERSITY
Business intelligence (BI) and data analytics are increasing in popularity as more organizations are looking to become more data-driven. Many tools have powerful visualization techniques that can create dynamic displays of critical information. To ensure that the data displayed on these visualizations is accurate and timely, a strong Data Architecture is needed. Join this webinar to understand how to create a robust Data Architecture for BI and data analytics that takes both business and technology needs into consideration.
Also, Effective communication is about more than just exchanging information.
It’s about understanding the emotion and intentions behind the information.
Credits: Steffen Kraft
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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.
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
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.
StarCompliance is a leading firm specializing in the recovery of stolen cryptocurrency. Our comprehensive services are designed to assist individuals and organizations in navigating the complex process of fraud reporting, investigation, and fund recovery. We combine cutting-edge technology with expert legal support to provide a robust solution for victims of crypto theft.
Our Services Include:
Reporting to Tracking Authorities:
We immediately notify all relevant centralized exchanges (CEX), decentralized exchanges (DEX), and wallet providers about the stolen cryptocurrency. This ensures that the stolen assets are flagged as scam transactions, making it impossible for the thief to use them.
Assistance with Filing Police Reports:
We guide you through the process of filing a valid police report. Our support team provides detailed instructions on which police department to contact and helps you complete the necessary paperwork within the critical 72-hour window.
Launching the Refund Process:
Our team of experienced lawyers can initiate lawsuits on your behalf and represent you in various jurisdictions around the world. They work diligently to recover your stolen funds and ensure that justice is served.
At StarCompliance, we understand the urgency and stress involved in dealing with cryptocurrency theft. Our dedicated team works quickly and efficiently to provide you with the support and expertise needed to recover your assets. Trust us to be your partner in navigating the complexities of the crypto world and safeguarding your investments.
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
