This video includes:
Purpose of Data Science, Role of Data Scientist, Skills required for Data Scientist, Job roles for Data Scientist, Applications of Data Science, Career in Data Science.
Want to pursue career in Data Science? Have knowledge of limited opportunities? Don't worry!
This e- book helps readers to know about top career opportunities one can pursue in Data Science. Further info.- https://www.henryharvin.com/business-analytics-course-with-python
Data Science is the Sexiest job in 21st century. Big Data Concept is going to rule the 21st century. Here is the presentation to give complete information and overview of data science big data.
Data science is different from Data Analytics,Data Engineering,Big Data.
Presentation about Data Science.
What is Data Science its process future and scope.
Data Science Presentation By Amit Singh.
"Sexiest job of 21st century"
This video includes:
Purpose of Data Science, Role of Data Scientist, Skills required for Data Scientist, Job roles for Data Scientist, Applications of Data Science, Career in Data Science.
Want to pursue career in Data Science? Have knowledge of limited opportunities? Don't worry!
This e- book helps readers to know about top career opportunities one can pursue in Data Science. Further info.- https://www.henryharvin.com/business-analytics-course-with-python
Data Science is the Sexiest job in 21st century. Big Data Concept is going to rule the 21st century. Here is the presentation to give complete information and overview of data science big data.
Data science is different from Data Analytics,Data Engineering,Big Data.
Presentation about Data Science.
What is Data Science its process future and scope.
Data Science Presentation By Amit Singh.
"Sexiest job of 21st century"
This was first part of the presentation on "Road Map for Careers in Big Data" in Conjunction with Hortonworks/Aengus Rooney on 17th August 2016 in London. For those contemplating moving to Big Data from often Relational Background
Demystify big data data science
An overview of the shift to Data Science Platforms
The 3 critical components of a Data Science platform
Industries that are most likely to get disrupted and shift to Data Science
Characteristics of firms that get left behind the Data Science wave
Factors that push an industry towards Data Science
A brief overview of aspects of platform architecture beyond technology
Big data, Machine learning and the AuditorBharath Rao
Check an insight as to how an Auditor can leverage Analytics, machine learning, and Technology to achieve absolute assurance and to effectively control the Fraud Risk present in the Enterprise.
Do you want to understand the emerging new data-driven jobs? This presentation discusses the emerging roles of Data Science and Data Engineering, and how they are related to Business Intelligence and Big Data. We will talk about skills and background needed for the jobs, and what education and certification is important.
A Seminar Presentation on Big Data for Students.
Big data refers to a process that is used when traditional data mining and handling techniques cannot uncover the insights and meaning of the underlying data. Data that is unstructured or time sensitive or simply very large cannot be processed by relational database engines. This type of data requires a different processing approach called big data, which uses massive parallelism on readily-available hardware.
This presentation is an Introduction to the importance of Data Analytics in Product Management. During this talk Etugo Nwokah, former Chief Product Officer for WellMatch, covered how to define Data Analytics why it should be a first class citizen in any software organization
Data Science Applications | Data Science For Beginners | Data Science Trainin...Edureka!
** Data Science Certification using R: https://www.edureka.co/data-science **
This Edureka "Data Science Applications" PPT takes you through the various domains in which data science is being deployed today, along with some potential applications of this technology. The world today runs on data and this PPT shows exactly that.
Check out our Data Science Tutorial blog series: http://bit.ly/data-science-blogs
Check out our complete Youtube playlist here: http://bit.ly/data-science-playlist
Follow us to never miss an update in the future.
Instagram: https://www.instagram.com/edureka_learning/
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka
Attend The Data Science Course in Bangalore From ExcelR. Practical Data Science Course in Bangalore Sessions With Assured Placement Support From Experienced Faculty. ExcelR Offers The Data Science Course in Bangalore.
Predictive Analytics - Big Data & Artificial IntelligenceManish Jain
Quick overview of the latest in big data and artificial intelligence. A lot of buzzwords being thrown around, hopefully this presentation will demystify many of the terms.
Relationship Between Big Data & AI
Human intelligence builds up on what we read, observe, learn, sense and experience. It's our ability to store large amount of data, accumulated over years and co-relating a few data points to answer a certain question, that makes us intelligent.
Similarly for machines to replicate human intelligence, they'll need to absorb large amount of data to make an intelligent decision............... (read more)
Data Science is a form of science that focuses on dealing with huge chunks of data by using modern data analysis tools and techniques to discover hidden patterns, meaningful insights, and make critical business decisions.
A Data Science professional has to utilize complicated machine learning algorithms to develop predictive models. There could be multiple sources present in different formats used in data analysis.
Big Data & Analytics (Conceptual and Practical Introduction)Yaman Hajja, Ph.D.
A 3-day interactive workshop for startups involve in Big Data & Analytics in Asia. Introduction to Big Data & Analytics concepts, and case studies in R Programming, Excel, Web APIs, and many more.
DOI: 10.13140/RG.2.2.10638.36162
Introduction to Data Science (Data Summit, 2017)Caserta
At DBTA's 2017 Data Summit in New York, NY, Caserta Founder & President, Joe Caserta, and Senior Architect, Bill Walrond, gave a pre-conference workshop presenting the ins and outs of data science. Data scientist has been dubbed the "sexiest" job of the 21st century, but it requires an understanding of many different elements of data analysis. This presentation dives into the fundamentals of data exploration, mining, and preparation, applying the principles of statistical modeling and data visualization in real-world applications.
This was first part of the presentation on "Road Map for Careers in Big Data" in Conjunction with Hortonworks/Aengus Rooney on 17th August 2016 in London. For those contemplating moving to Big Data from often Relational Background
Demystify big data data science
An overview of the shift to Data Science Platforms
The 3 critical components of a Data Science platform
Industries that are most likely to get disrupted and shift to Data Science
Characteristics of firms that get left behind the Data Science wave
Factors that push an industry towards Data Science
A brief overview of aspects of platform architecture beyond technology
Big data, Machine learning and the AuditorBharath Rao
Check an insight as to how an Auditor can leverage Analytics, machine learning, and Technology to achieve absolute assurance and to effectively control the Fraud Risk present in the Enterprise.
Do you want to understand the emerging new data-driven jobs? This presentation discusses the emerging roles of Data Science and Data Engineering, and how they are related to Business Intelligence and Big Data. We will talk about skills and background needed for the jobs, and what education and certification is important.
A Seminar Presentation on Big Data for Students.
Big data refers to a process that is used when traditional data mining and handling techniques cannot uncover the insights and meaning of the underlying data. Data that is unstructured or time sensitive or simply very large cannot be processed by relational database engines. This type of data requires a different processing approach called big data, which uses massive parallelism on readily-available hardware.
This presentation is an Introduction to the importance of Data Analytics in Product Management. During this talk Etugo Nwokah, former Chief Product Officer for WellMatch, covered how to define Data Analytics why it should be a first class citizen in any software organization
Data Science Applications | Data Science For Beginners | Data Science Trainin...Edureka!
** Data Science Certification using R: https://www.edureka.co/data-science **
This Edureka "Data Science Applications" PPT takes you through the various domains in which data science is being deployed today, along with some potential applications of this technology. The world today runs on data and this PPT shows exactly that.
Check out our Data Science Tutorial blog series: http://bit.ly/data-science-blogs
Check out our complete Youtube playlist here: http://bit.ly/data-science-playlist
Follow us to never miss an update in the future.
Instagram: https://www.instagram.com/edureka_learning/
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka
Attend The Data Science Course in Bangalore From ExcelR. Practical Data Science Course in Bangalore Sessions With Assured Placement Support From Experienced Faculty. ExcelR Offers The Data Science Course in Bangalore.
Predictive Analytics - Big Data & Artificial IntelligenceManish Jain
Quick overview of the latest in big data and artificial intelligence. A lot of buzzwords being thrown around, hopefully this presentation will demystify many of the terms.
Relationship Between Big Data & AI
Human intelligence builds up on what we read, observe, learn, sense and experience. It's our ability to store large amount of data, accumulated over years and co-relating a few data points to answer a certain question, that makes us intelligent.
Similarly for machines to replicate human intelligence, they'll need to absorb large amount of data to make an intelligent decision............... (read more)
Data Science is a form of science that focuses on dealing with huge chunks of data by using modern data analysis tools and techniques to discover hidden patterns, meaningful insights, and make critical business decisions.
A Data Science professional has to utilize complicated machine learning algorithms to develop predictive models. There could be multiple sources present in different formats used in data analysis.
Big Data & Analytics (Conceptual and Practical Introduction)Yaman Hajja, Ph.D.
A 3-day interactive workshop for startups involve in Big Data & Analytics in Asia. Introduction to Big Data & Analytics concepts, and case studies in R Programming, Excel, Web APIs, and many more.
DOI: 10.13140/RG.2.2.10638.36162
Introduction to Data Science (Data Summit, 2017)Caserta
At DBTA's 2017 Data Summit in New York, NY, Caserta Founder & President, Joe Caserta, and Senior Architect, Bill Walrond, gave a pre-conference workshop presenting the ins and outs of data science. Data scientist has been dubbed the "sexiest" job of the 21st century, but it requires an understanding of many different elements of data analysis. This presentation dives into the fundamentals of data exploration, mining, and preparation, applying the principles of statistical modeling and data visualization in real-world applications.
As machine learning has is permeating more and more industries and businesses, the need for audit professionals to provide assurance over machine learning is growing. Andrew's presentation will provide an audit-centric overview of machine learning and present a framework for how to begin auditing machine learning in your organization.
Overview of analytics and big data in practiceVivek Murugesan
Intended to give an overview of analytics and big data in practice. With set of industry use cases from different domains. Would be useful for someone who is trying to understand Analytics and Big Data.
Are We Generation AI? An Introduction to Applications, Benefits, and Challenges of AI for Small and Medium Sized Business. Presented at the WIN.fbg meeting in Fredericksburg, TX on April 11, 2023.
Discover Practical AI use cases in Customer Service! In this webinar, you will learn how to lower the time to first response and time to resolution to keep your SLAs intact, as well as about chatbots, ticket tagging, and urgency detection. We will also mention some technologies, such as text recognition and sentiment analysis.
My slides for my talk regarding machine learning and data science. Includes working examples with accompanying repo with reproducible code and data sets available.
Machine Learning: What Assurance Professionals Need to Know Andrew Clark
Machine learning has evolved past an esoteric technique worked on by academics and research institutes into a viable technology being deployed at many companies. Machine learning has been significantly changing the competitive landscape of business models worldwide, contributing to the demise of established business, such as Blockbuster, to creating entirely new businesses, such as algorithmic advertising. This presentation strives to address the questions of what assurance professionals need to know about this technology and how to provide assurance around machine learning implementations and its unique risks.
Presentation on developments in hiring and fintech for HKU Executive certific...Kok Tong (K.T.) Khoo
Slides for my guest speaker session at the HKU executive certificate in Internet Finance. Covering personal observations in startup markets and careers, Hong Kong vs Singapore, hiring trends and business models.
Data Con LA 2022 - Demystifying the Art of Business Intelligence and Data Ana...Data Con LA
Brandon Wong, Lead Software Engineer, Academy of Motion Picture Arts and Sciences
Business Intelligence is a technology-driven process that analyzes data and forms conclusions to help assist workers to make informed business decisions. From collecting to cleaning, to morphing, to displaying we will address the pain points, tips, and tricks on how to navigate this process of converting data from raw material to a final product.
You'll learn:
From a high level, the process of bringing data from the "back" to the "front".
Tools and best practices for cleaning and displaying data.
Understanding the foundations of business intelligence to better execute on objectives.
The various ways of displaying data depends on circumstance.
Book: Software Architecture and Decision-MakingSrinath Perera
Uncertainty is the leading cause of mistakes made by practicing software architects. The primary goal of architecture is to handle uncertainty arising from user cases as well as architectural techniques. The book discusses how to make architectural decisions and manage uncertainty. From the book, You will learn common problems while designing a system, a default solution for each, more complex alternatives, and 5Q & 7P (Five Questions and Seven Principles) that help you choose.
Book, https://amzn.to/3v1MfZX
Blog: http://tinyurl.com/swdmblog
Six min video - https://youtu.be/jtnuHvPWlYU
We have critically evaluated how AI will shape integration use cases, their feasibility, and timelines. Emerging Technology Analysis Canvas (ETAC), a framework built to analyze emerging technologies, is the methodology of our study.
We observe that AI can significantly impact integration use cases and identify 13 AI-based use case classes for integration. Points to note include:
Enabling AI in an enterprise involves collecting, cleaning up, and creating a single representation of data as well as enforcing decisions and exposing data outside, each of which leads to many integration use cases. Hence, AI indirectly creates demand for integration.
AI needs data, which in some cases lead to significant competitive advantages. The need to collect data would drive vendors to offer most AI products in the cloud through APIs.
Due to lack of expertise and data, custom AI model building will be limited to large organizations. It is hard for small and medium size organization to build and maintain custom models.
The Role of Blockchain in Future IntegrationsSrinath Perera
We have critically evaluated blockchain-based integration use cases, their feasibility, and timelines. Emerging Technology Analysis Canvas (ETAC), a framework built to analyze emerging technologies, is the methodology of our study. Based on our analysis, we observe that blockchain can significantly impact integration use cases.
In our paper, we identify 30-plus blockchain-based use cases for integration and four architecture patterns. Notably, each use case we identified can be implemented using one of the architecture patterns. Furthermore, we also discuss challenges and risks posed by blockchains that would affect these architecture patterns.
Our webinar presents a critical analysis of serverless technology and our thoughts about its future. We use Emerging Technology Analysis Canvas (ETAC), a framework built to analyze emerging technologies, as the methodology of our study. Based on our analysis, we believe that serverless can significantly impact applications and software development workflows.
We’ve also made two further observations:
Limitations, such as tail latencies and cold starts, are not deal breakers for adoption. There are significant use cases that can work with existing serverless technologies despite these limitations.
We see a significant gap in required tooling and IDE support, best practices, and architecture blueprints. With proper tooling, it is possible to train existing enterprise developers to program with serverless. If proper tools are forthcoming, we believe serverless can cross the chasm in 3-5 years.
A detailed analysis can be found here: A Survey of Serverless: Status Quo and Future Directions. Join our webinar as we discuss this study, our conclusions, and evidence in detail.
1. Blockchain potential impact is real. If successful, Blockchain technologies can transform the way we live our day to day lives.
2. We believe technology is ready for limited applications in Digital Currency, Lightweight financial systems, Ledgers (of identity, ownership, status, and authority), Provenance (e.g. supply chains and other B2B scenarios) and Disintermediation, which we believe will happen in next three years.
3. However, with other use cases, blockchain faces significant challenges such as performance, irrevocability, need for regulation and lack of census mechanisms. These are hard problems and
4. It is not clear whether blockchain can sustain the current level of effort for extended period of 5+ years. There are many startups and they run the risk of running out of money before markets are ready. Failure of startups can inhibit further funding and investments.
5. Value and need of decentralization compared to centralized and semi-centralized alternatives is not clear.
A Visual Canvas for Judging New TechnologiesSrinath Perera
In the fast-changing technology world, the technology landscape shifts faster and faster. The agents of thses changes are new emerging technologies, which sometimes even create, destroy, or transform segments. In a shifting world, prevailing advantages are fleeting. Organizations that can master change and ride technology waves owns the future.
Not all emerging technologies live up to their promise. Every year, as a part of annual planning, most organizations need to decide relevance, impact, and the probability of success of emerging technologies and pick their bets. Although it is a regular decision there is no widely accepted framework for evaluating emerging technologies.
As a solution to this problem, we present “Emerging Technology Analysis Canvas” (ETAC), a framework to assess an individual emerging technology as a solution to this problem. Inspired by the Business Model Canvas, It represents different aspects of technology visually on a single page. This approach includes a set of questions that probe the technology arranged around a logical narrative. The visual representation is concise, compact, and comprehensible in a glance.
The talk discusses how analytics can attack privacy and what we can do about it. It discusses the legal responses (e.g. GDPR) as well technical responses ( differential privacy and homomorphic encryption).
The video is in https://www.facebook.com/eduscopelive/videos/314847475765297/ from 1.18.
Blockchain is often cited as one of the most impactful technology along with AI. It has attracted many startups, venture investments, and academic research. If successful, Blockchain technologies can transform the way, we live our day to day lives.
However, blockchain faces significant challenges such as performance, irrevocability, need for regulation and lack of census mechanisms. They are hard problems, and likely it will take at least 5-10 years to find answers to those problems.
Given the risk involved as well as the significant potential returns, we recommend a cautiously optimistic approach for blockchain with the focus on concrete use cases.
Today's Technology and Emerging Technology LandscapeSrinath Perera
We have seen the rise and fall of many technologies, some disappearing without a trace while others redefining the world. Collectively they have shaped our world beyond recognition. In this talk, Srinath will start with past technologies exploring their behavior. Then he will explore current middleware landscape, its composition, and relationships between different segments. He will discuss significant developments and discuss their future. Further, he will discuss emerging technologies, forces that shape them, and the promise of each technology, and finally, speculate about their evolution. You will walk away with knowledge on the evolution of middleware, the status quo, and discussion about how, at WSO2, we think those technologies will evolve.
Some died, some get by, but some have woven themselves to today's middleware so much that we do not notice them. The point I want to make is that not all emerging technologies are fads. Some are, and some are too early, like AI. But some are lasting.
The Rise of Streaming SQL and Evolution of Streaming ApplicationsSrinath Perera
First-generation stream processors, such as Apache Storm, wanted us to write code. It was a great start. However, when building real-world apps, which are used for a long time and evolve, writing code gets us into trouble.
If we want to query a database or query data stored in storage with Hadoop, we use SQL. Why can't we query data streaming using SQL? We can. Almost all open source stream processors, including Storm, Flink, and Kafka, have switched to SQL.
In this webinar, Srinath will talk about the evolution of stream processing, streaming SQL, the status quo, and what this means to stream applications. He will also dissect the experience of building streaming applications by exploring common patterns and pitfalls.
Analytics and AI: The Good, the Bad and the UglySrinath Perera
Analytics let us question the data, which in effect questions the world around us. This let us understand, monitor, and shape the world. AI let us discover connections, predict the possible futures and automate tasks.
These twin technologies can change the world around us. On one hand, make us efficient, connected, and fulfilled. At the same time, the change of status quo can replace jobs, affect lives and build biases into our systems that can marginalize millions.
In this talk, we will discuss core ideas behind analytics and AI, their possible impact, both good and bad outcomes, and challenges.
The dawn of digital businesses is upon us, with reimagined business models that make the best use of digital technologies such as automation, analytics, integration and cloud. Digital businesses are efficient, continuously optimizing, proactive, flexible and are able to fully understand their customers. Analytics is a key technology that helps in doing so. It acts as the eyes and ears of the system and provides a holistic view on the past and present so that decision-makers can predict what will happen in the future. This webinar will explore
Why becoming a digital business is not a choice
The role of analytics in digital transformation with examples
How best to leverage state of the art analytics technology
SoC Keynote:The State of the Art in Integration TechnologySrinath Perera
This talk discusses Outline of the state of the art of Enterprise Software and how we get there, as I see it. Also second part describes Ballerina, a new programming language WSO2 has built for Enterprise Computing.
It is presented as a Keynote at 11th Symposium and Summer School On Service-Oriented Computing.
We are at the dawn of digital businesses, that are reimagined to make the best use of digital technologies such as automation, analytics, cloud, and integration. These businesses are efficient, continuously optimizing, proactive, flexible and able to understand customers in detail. A key part of a digital business is analytics: the eyes and ears of the system that tracks and provides a detailed view on what was and what is and lets decision makers predict what will be.
This session will explore how the WSO2 analytics platform
Plays a role in your digital transformation journey
Collects and analyzes data through batch, real-time, interactive and predictive processing technologies
Lets you communicate the results through dashboards
Brings together all analytics technologies into a single platform and user experience
How can we filter the truth from lies and complex shades between the two? In the time of data avalanche, this is a skill that serves both our carriers as well as lives.
In this talk, we will discuss where to find information, the importance of sources, understanding bias and conflicts of interests, and finally how to communicate our conclusions with their associated confidence.
Explore our comprehensive data analysis project presentation on predicting product ad campaign performance. Learn how data-driven insights can optimize your marketing strategies and enhance campaign effectiveness. Perfect for professionals and students looking to understand the power of data analysis in advertising. for more details visit: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/
As Europe's leading economic powerhouse and the fourth-largest hashtag#economy globally, Germany stands at the forefront of innovation and industrial might. Renowned for its precision engineering and high-tech sectors, Germany's economic structure is heavily supported by a robust service industry, accounting for approximately 68% of its GDP. This economic clout and strategic geopolitical stance position Germany as a focal point in the global cyber threat landscape.
In the face of escalating global tensions, particularly those emanating from geopolitical disputes with nations like hashtag#Russia and hashtag#China, hashtag#Germany has witnessed a significant uptick in targeted cyber operations. Our analysis indicates a marked increase in hashtag#cyberattack sophistication aimed at critical infrastructure and key industrial sectors. These attacks range from ransomware campaigns to hashtag#AdvancedPersistentThreats (hashtag#APTs), threatening national security and business integrity.
🔑 Key findings include:
🔍 Increased frequency and complexity of cyber threats.
🔍 Escalation of state-sponsored and criminally motivated cyber operations.
🔍 Active dark web exchanges of malicious tools and tactics.
Our comprehensive report delves into these challenges, using a blend of open-source and proprietary data collection techniques. By monitoring activity on critical networks and analyzing attack patterns, our team provides a detailed overview of the threats facing German entities.
This report aims to equip stakeholders across public and private sectors with the knowledge to enhance their defensive strategies, reduce exposure to cyber risks, and reinforce Germany's resilience against cyber threats.
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.
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...Subhajit Sahu
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
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.
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
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...
Data science Applications in the Enterprise
1. Data Science Use Cases in
the Enterprise
Srinath Perera
Chief Architect, WSO2, Apache Member
2. Context: Understanding
Enterprise (ROI)
● It is about Money: long-term Money.
○ If you are looking to make a million once, sometimes,
you can get away with exploitation.
○ If you are looking to make a billion every year, you
have to care about customers, brand, employees as
well as the environment you are operating in
○ E.g., Indra Nooyi and her effort to move Pepsi to
healthy food.
● It is a Strategic environment where enterprises
compete.
○ “If you know the enemy and know yourself, you need not fear
the result of a hundred battles. ”
― Sun Tzu, The Art of War.
3. Context: Highly valued
Outcomes
● Efficiency, Savings
● Improving Customer Experience
● Finding new markets,
understanding markets
● Forecasts, Prediction
● Automation and Decision Support
I skate to where the puck is
going to be, not where it has
been. ---Wayne Gretzky
4. ● Examples
○ The effort by the US to use sensor and data analysis to stop
infiltration through Ho Chi Minh Trail in 70s
○ Even Nate Silver got Trump's victory wrong
● Reasons
○ History is not always representative of the future (e.g., Trump
Elections)
○ Complex systems ( highly interconnected systems where one
or few players can significantly change the outcomes)
○ Highly competitive situations such as stock Markets
■ Predictable at stable times, but not with shock
○ Average is affected dramatically by rare events (e,g, Covid)
■ Data can determine "average" outcomes with great
accuracy
○ Not enough data or data do not capture critical aspects
Nevermind the Press, Data Science does not always work
5. Use Cases @ Enterprise
● Efficiency, Self Awareness, and Forecasts
● Optimizing the sales funnel
● Predictive Maintenance
● Improving Customer Experience
● Product Use cases from a real-world iPaaS
● Finding new markets, understanding markets, Competitor
Analysis
● https://sparktoro.com/ - Instantly discover what your
audience reads, watches, listens to, and follows.
● Automate mundane tasks and let people focus on what
they are good at
● Automation and Task Assistant Systems
● Decision support systems
Often needs Explainability too
6. Efficiency: Optimize the Sales Funnel
● Each enterprise has a funnel
like this ( names may be
different)
● KPIs support decisions
● Examples:
○ conversion rates, dropoff - to find
bottlenecks
○ cost per conversion - find
activities that work well
○ Time spend on each stage
○ Forecasts
○ A/B testing optimizes
7. Efficiency: Predictive
Maintenance
● Often breakdowns have high costs
● We do preventive maintenance to
avoid that, but it leaves significant
money on the table
● Use telemetry data to predict
breakdowns
● We need to manage risk against
false negatives (e.g., cost to give
customer 100$)
8. Efficiency: Churn Prediction
● Even small churn compounds
significantly to reduce topline, and create
negative word of mouth.
● How is the user using the product?
● Has he given up?
● Are there complaints?
● Is there anything we can do if we know
before?
Need to think through the full story -
Ask “so what” until you see $$
10. Choreo Use Cases and Challenges
● Can collect data about everything, clicks,
messages, logs etc
● The focus is using AI to improve user
experience
● The system will have 10s of thousands of users
○ We can’t run a model per user
● Some use cases have limited data
● The specific user would not have enough data
initially, so we have a cold start problem
● Some use cases require personalization
11. User Experience: Forecasting Performance
● Performance feedback while
you write code
● API, service, database calls
dominate performance
● Use historical data about each
API, service, database call and
fit Machine Learning models
● Use queuing theory to model
the throughput and latency
12. Getting a Model to Production is Complicated
● Data Collection
● Model training
● Model deployment and
integrating the model into the
user experience
○ Acting on results
● Getting user feedback
● Evaluating and improving
models
13. User Experience: Automatic Data Mapping
● Programming with APIs
need us to map data
between two API calls (
and two systems)
● Automatic data
mapping suggest
mapping between two
data types
● It can maps data types
it has never seen
14. User Experience: Anomaly and Root Cause Prediction
● Detecting Performance anomalies in
the system
● The goal is to detect and performance
problems and notify the users and
supporting them in troubleshooting
● We started with several states of the art
papers and eventually beat them
○ 90% precision and 50% recall vs. 98% vs.
81% recall
● Working on attributing anomalies to
parts of the system and providing root
cause predictions
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16. Automation: Extracting information from Images/ Video
● Vidado.ai Using OCR to digitize Data RPA
does not work well with paper
● Icetana.com - decision support for video
surveillance
● www.dataminr.com detects high impact
events from public data
○ E.g., Brand risk, disease outbreaks, potential
new stories
17. Automation: Competitive Adjustments
● Common use cases
are adjusting the price
● This leads to curious
cases when bots are
on both sides
A good rule of thumb is to remember AI vs. AI does not work well.
18. Automation: Automate Mundane Tasks
● Works on top
salesforce
● Suggest next Action
● Provides templates
for actions
● Full context, connect
all information
● Benchmark
performance
19. Parting Thoughts
● If you plan to solve organizational problems
with data science, you need to understand
how it works and speak their language.
● Make sure there is enough data
● Think through the full lifecycle, including
economics (e.g., Choreo) and explain
● Model deployment, evaluation, integration to
customer, and evolution is complex
● Harder to build per user custom models,
better if you can create value against existing
data models and integrate as SaaS
Learn to see where
Data Science works,
but learn to see where
it does not also!!