This document provides an overview of key concepts in business analytics including:
- Definitions of data science, data scientist, and analytics which involve extracting insights from data.
- A process map of data science including data collection, cleaning, modeling, and communication.
- A brief history and timeline of developments in computer technology, statistics, and analytics from the 1960s to present.
- Emerging areas like artificial intelligence, autonomous systems, and the impact of technology on jobs and society.
This document summarizes a talk on data science for software engineering. It discusses how data science involves various fields like statistics, machine learning, and data mining. It notes that while "big data" is often discussed, software engineering data is typically small and sparse. Domain knowledge is important for data mining to avoid misinterpreting data. Data science with software engineering data requires understanding organizations and their willingness to share data given privacy concerns. The document outlines sharing data, models, and methods for learning across different organizations and discusses techniques for balancing privacy and utility when sharing data.
This document provides an overview of the introductory lecture to the BS in Data Science program. It discusses key topics that were covered in the lecture, including recommended books and chapters to be covered. It provides a brief introduction to key terminologies in data science, such as different data types, scales of measurement, and basic concepts. It also discusses the current landscape of data science, including the difference between roles of data scientists in academia versus industry.
Introduction to Data Science and Large-scale Machine LearningNik Spirin
This document is a presentation about data science and artificial intelligence given by James G. Shanahan. It provides an outline that covers topics such as machine learning, data science applications, architecture, and future directions. Shanahan has over 25 years of experience in data science and currently works as an independent consultant and teaches at UC Berkeley. The presentation provides background on artificial intelligence and machine learning techniques as well as examples of their successful applications.
Demystifying Data Science with an introduction to Machine LearningJulian Bright
The document provides an introduction to the field of data science, including definitions of data science and machine learning. It discusses the growing demand for data science skills and jobs. It also summarizes several key concepts in data science including the data science pipeline, common machine learning algorithms and techniques, examples of machine learning applications, and how to get started in data science through online courses and open-source tools.
A talk I gave at the MMDS workshop June 2014 on the Myria system as well as some of Seung-Hee Bae's work on scalable graph clustering.
https://mmds-data.org/
Big Data and Data Mining - Lecture 3 in Introduction to Computational Social ...Lauri Eloranta
Third lecture of the course CSS01: Introduction to Computational Social Science at the University of Helsinki, Spring 2015.(http://blogs.helsinki.fi/computationalsocialscience/).
Lecturer: Lauri Eloranta
Questions & Comments: https://twitter.com/laurieloranta
This document summarizes a talk on data science for software engineering. It discusses how data science involves various fields like statistics, machine learning, and data mining. It notes that while "big data" is often discussed, software engineering data is typically small and sparse. Domain knowledge is important for data mining to avoid misinterpreting data. Data science with software engineering data requires understanding organizations and their willingness to share data given privacy concerns. The document outlines sharing data, models, and methods for learning across different organizations and discusses techniques for balancing privacy and utility when sharing data.
This document provides an overview of the introductory lecture to the BS in Data Science program. It discusses key topics that were covered in the lecture, including recommended books and chapters to be covered. It provides a brief introduction to key terminologies in data science, such as different data types, scales of measurement, and basic concepts. It also discusses the current landscape of data science, including the difference between roles of data scientists in academia versus industry.
Introduction to Data Science and Large-scale Machine LearningNik Spirin
This document is a presentation about data science and artificial intelligence given by James G. Shanahan. It provides an outline that covers topics such as machine learning, data science applications, architecture, and future directions. Shanahan has over 25 years of experience in data science and currently works as an independent consultant and teaches at UC Berkeley. The presentation provides background on artificial intelligence and machine learning techniques as well as examples of their successful applications.
Demystifying Data Science with an introduction to Machine LearningJulian Bright
The document provides an introduction to the field of data science, including definitions of data science and machine learning. It discusses the growing demand for data science skills and jobs. It also summarizes several key concepts in data science including the data science pipeline, common machine learning algorithms and techniques, examples of machine learning applications, and how to get started in data science through online courses and open-source tools.
A talk I gave at the MMDS workshop June 2014 on the Myria system as well as some of Seung-Hee Bae's work on scalable graph clustering.
https://mmds-data.org/
Big Data and Data Mining - Lecture 3 in Introduction to Computational Social ...Lauri Eloranta
Third lecture of the course CSS01: Introduction to Computational Social Science at the University of Helsinki, Spring 2015.(http://blogs.helsinki.fi/computationalsocialscience/).
Lecturer: Lauri Eloranta
Questions & Comments: https://twitter.com/laurieloranta
Introduction to various data science. From the very beginning of data science idea, to latest designs, changing trends, technologies what make then to the application that are already in real world use as we of now.
HackerEarth is pleased to announce its next session to help you understand what it really takes to become a data scientist.
Agenda of this session will include answers to the following questions:
- Why is it the best time to take up Data Science as a career?
- How can you take the first step in Data Science? (After all, first step is always the hardest!)
- How can you become better and progress fast?
- How is life after becoming a Data Scientist?
Speaker:
Jesse Steinweg-Woods is soon-to-be a Senior Data Scientist at tronc, working on recommender systems for articles and understanding customer behavior. Previously, he worked at Argo Group Insurance on new pricing models that took advantage of machine learning techniques. He received his PhD in Atmospheric Science from Texas A&M University, and his research focused on numerical weather and climate prediction.
A 1015 update to the 2012 "Data Big and Broad" talk - http://www.slideshare.net/jahendler/data-big-and-broad-oxford-2012 - extends coverage, brings more in context of recent "big data" work.
On Beyond OWL: challenges for ontologies on the WebJames Hendler
The need for ontologies in the real world is manifest and increasing. On the Web, ontologies are everywhere — but OWL isn’t. In this talk, I look at some of the things that are not in OWL, but which are needed for the use of OWL in many Web domains. This talk explores some of the needs for ontologies on the Web in data integration, emerging technologies, and linked data applications – and asks where the features needed for these are in OWL. The talk ends with some challenges to the OWL, and greater ontology, community needed to see more eventual use of standard ontologies on the Web.
Machine Learning in the age of Big DataDaniel Sârbe
This document provides an overview of machine learning and how it gains more importance in the age of big data. It discusses machine learning techniques like supervised learning, unsupervised learning, and reinforcement learning. It also contrasts traditional data science with machine learning approaches and explains how machine learning works better with large, big data. A key point made is that more data is better for machine learning algorithms to learn from than having the best algorithm alone.
This document provides an overview of getting started with data science using Python. It discusses what data science is, why it is in high demand, and the typical skills and backgrounds of data scientists. It then covers popular Python libraries for data science like NumPy, Pandas, Scikit-Learn, TensorFlow, and Keras. Common data science steps are outlined including data gathering, preparation, exploration, model building, validation, and deployment. Example applications and case studies are discussed along with resources for learning including podcasts, websites, communities, books, and TV shows.
This talk presents areas of investigation underway at the Rensselaer Institute for Data Exploration and Applications. First presented at Flipkart, Bangalore India, 3/2015.
NOVA Data Science Meetup 1/19/2017 - Presentation 1NOVA DATASCIENCE
The document discusses cognitive computing and its applications. It begins with an agenda that includes an overview of cognitive computing and examples of its use. It then discusses IBM Research's work leading to the development of Watson. Key points made include that most data is now unstructured, cognitive systems can reason, learn and understand like humans, and examples of cognitive computing applications in various domains.
This document provides an overview of machine learning, including definitions, common applications, and examples of companies using machine learning. It discusses how BuildFax, a company that provides building permit data and services to industries like insurance, used Amazon Machine Learning to build more accurate predictive models for roof age and job cost estimates. By leveraging Amazon ML, BuildFax was able to build models much faster and provide more precise, property-specific predictions to customers through APIs.
Introduction to Data Science - Week 4 - Tools and Technologies in Data ScienceFerdin Joe John Joseph PhD
This document discusses tools and technologies used in data science. It covers popular programming languages like Python, R, Java and C++. It also discusses databases, data analytics tools, APIs, servers, and frameworks. Specific tools mentioned include Hadoop, Spark, Tableau, IBM SPSS, SAS, and Excel. The document provides brief descriptions and examples of how these various tools are used in data science.
The Rensselaer Institute for Data Exploration and Applications is addressing new modes of data exploration and integration to enhance the work of campus researchers (and beyond). This talk outlines the "data exploration" technologies being explored
Presented to a webinar hosted by Nuance Inc, under the title "The Semantic Web: What it is and Why you should care" on 2/29/2012.
This talk presents a fast overview of the Semantic Web and recent application deployment in the space.
A presentation delivered by Mohammed Barakat on the 2nd Jordanian Continuous Improvement Open Day in Amman. The presentation is about Data Science and was delivered on 3rd October 2015.
Big Data & Machine Learning - TDC2013 Sao PauloOCTO Technology
BigData and Machine Learning: Usage and Opportunities for your IT department
Talk presented at The Developer Conference in São Paulo - 12/0713
Mathieu DESPRIEE
Anastasiia Kornilova has over 3 years of experience in data science. She has an MS in Applied Mathematics and runs two blogs. Her interests include recommendation systems, natural language processing, and scalable data solutions. The agenda of her presentation includes defining data science, who data scientists are and what they do, and how to start a career in data science. She discusses the wide availability of data, how data science makes sense of and provides feedback on data, common data science applications, and who employs data scientists. The presentation outlines the typical data science workflow and skills required, including domain knowledge, math/statistics, programming, communication/visualization, and how these skills can be obtained. It provides examples of data science
This document summarizes a seminar on machine learning using big data. It discusses the history of data storage and traditional databases. It then introduces machine learning and the types of learning, including supervised and unsupervised learning. Specific algorithms for each type are covered such as k-means clustering for unsupervised and naive Bayes for supervised. Case studies on applications like Amazon product recommendations are presented. The document concludes by discussing tools for machine learning and future applications as more connected devices generate extensive data.
Machine learning with Big Data power point presentationDavid Raj Kanthi
This is an article made form the articles of IEEE published in the year 2017
The following presentation has the slides for the Title called the
Machine Learning with Big data. that following presentation which has the challenges and approaches of machine learning with big data.
The integration of the Big Data with Machine Learning has so many challenges that Big data has and what is the approach made by the machine learning mechanism for those challenges.
The document discusses challenges and opportunities related to big data and high performance computing. It notes that computational power is increasing exponentially according to Moore's Law, but clock speeds have plateaued forcing a shift to multi-core processors. This is driving the need for parallel programming and new software approaches. Big data is also growing dramatically from various sources, such as sensors and social media. Analyzing this large, heterogeneous data requires new techniques in data mining, machine learning, and visualization. High performance computing and citizen science initiatives can help extract insights from big data to address important problems in health, environment, and other domains.
As we move into a new era of ITSM computing, new big data and machine learning tools and methodologies are being developed to support IT staff by intelligently extracting insights and making predictions from the enormous amounts of data accumulated from the organization. According to Gartner, I&O leaders must take a comprehensive approach to incorporate advanced big data and machine learning technologies into their organizations or risk becoming irrelevant. But what exactly is big data and machine learning all about? How can you introduce these concepts into your existing Service Desk?
Join USF’s distinguished Computer Science and Engineering Professor Lawrence Hall and SunView Software’s VP of Marketing and Product Strategy John Prestridge as they break down the fundamentals of big data and machine learning and provide real-world examples of the impact the technologies will have on ITSM.
Data Science - An emerging Stream of Science with its Spreading Reach & ImpactDr. Sunil Kr. Pandey
This is my presentation on the Topic "Data Science - An emerging Stream of Science with its Spreading Reach & Impact". I have compiled and collected different statistics and data from different sources. This may be useful for students and those who might be interested in this field of Study.
The document discusses the growth of data and the field of data science. It begins by noting the large amounts of data being generated daily by various sources like web/e-commerce transactions, social networks, and scientific projects. It then discusses some of the challenges of big data including volume, velocity, and variety. The document provides an overview of the multidisciplinary nature of data science and the skills required of data scientists. It also summarizes different approaches to and job roles in data science.
Introduction to various data science. From the very beginning of data science idea, to latest designs, changing trends, technologies what make then to the application that are already in real world use as we of now.
HackerEarth is pleased to announce its next session to help you understand what it really takes to become a data scientist.
Agenda of this session will include answers to the following questions:
- Why is it the best time to take up Data Science as a career?
- How can you take the first step in Data Science? (After all, first step is always the hardest!)
- How can you become better and progress fast?
- How is life after becoming a Data Scientist?
Speaker:
Jesse Steinweg-Woods is soon-to-be a Senior Data Scientist at tronc, working on recommender systems for articles and understanding customer behavior. Previously, he worked at Argo Group Insurance on new pricing models that took advantage of machine learning techniques. He received his PhD in Atmospheric Science from Texas A&M University, and his research focused on numerical weather and climate prediction.
A 1015 update to the 2012 "Data Big and Broad" talk - http://www.slideshare.net/jahendler/data-big-and-broad-oxford-2012 - extends coverage, brings more in context of recent "big data" work.
On Beyond OWL: challenges for ontologies on the WebJames Hendler
The need for ontologies in the real world is manifest and increasing. On the Web, ontologies are everywhere — but OWL isn’t. In this talk, I look at some of the things that are not in OWL, but which are needed for the use of OWL in many Web domains. This talk explores some of the needs for ontologies on the Web in data integration, emerging technologies, and linked data applications – and asks where the features needed for these are in OWL. The talk ends with some challenges to the OWL, and greater ontology, community needed to see more eventual use of standard ontologies on the Web.
Machine Learning in the age of Big DataDaniel Sârbe
This document provides an overview of machine learning and how it gains more importance in the age of big data. It discusses machine learning techniques like supervised learning, unsupervised learning, and reinforcement learning. It also contrasts traditional data science with machine learning approaches and explains how machine learning works better with large, big data. A key point made is that more data is better for machine learning algorithms to learn from than having the best algorithm alone.
This document provides an overview of getting started with data science using Python. It discusses what data science is, why it is in high demand, and the typical skills and backgrounds of data scientists. It then covers popular Python libraries for data science like NumPy, Pandas, Scikit-Learn, TensorFlow, and Keras. Common data science steps are outlined including data gathering, preparation, exploration, model building, validation, and deployment. Example applications and case studies are discussed along with resources for learning including podcasts, websites, communities, books, and TV shows.
This talk presents areas of investigation underway at the Rensselaer Institute for Data Exploration and Applications. First presented at Flipkart, Bangalore India, 3/2015.
NOVA Data Science Meetup 1/19/2017 - Presentation 1NOVA DATASCIENCE
The document discusses cognitive computing and its applications. It begins with an agenda that includes an overview of cognitive computing and examples of its use. It then discusses IBM Research's work leading to the development of Watson. Key points made include that most data is now unstructured, cognitive systems can reason, learn and understand like humans, and examples of cognitive computing applications in various domains.
This document provides an overview of machine learning, including definitions, common applications, and examples of companies using machine learning. It discusses how BuildFax, a company that provides building permit data and services to industries like insurance, used Amazon Machine Learning to build more accurate predictive models for roof age and job cost estimates. By leveraging Amazon ML, BuildFax was able to build models much faster and provide more precise, property-specific predictions to customers through APIs.
Introduction to Data Science - Week 4 - Tools and Technologies in Data ScienceFerdin Joe John Joseph PhD
This document discusses tools and technologies used in data science. It covers popular programming languages like Python, R, Java and C++. It also discusses databases, data analytics tools, APIs, servers, and frameworks. Specific tools mentioned include Hadoop, Spark, Tableau, IBM SPSS, SAS, and Excel. The document provides brief descriptions and examples of how these various tools are used in data science.
The Rensselaer Institute for Data Exploration and Applications is addressing new modes of data exploration and integration to enhance the work of campus researchers (and beyond). This talk outlines the "data exploration" technologies being explored
Presented to a webinar hosted by Nuance Inc, under the title "The Semantic Web: What it is and Why you should care" on 2/29/2012.
This talk presents a fast overview of the Semantic Web and recent application deployment in the space.
A presentation delivered by Mohammed Barakat on the 2nd Jordanian Continuous Improvement Open Day in Amman. The presentation is about Data Science and was delivered on 3rd October 2015.
Big Data & Machine Learning - TDC2013 Sao PauloOCTO Technology
BigData and Machine Learning: Usage and Opportunities for your IT department
Talk presented at The Developer Conference in São Paulo - 12/0713
Mathieu DESPRIEE
Anastasiia Kornilova has over 3 years of experience in data science. She has an MS in Applied Mathematics and runs two blogs. Her interests include recommendation systems, natural language processing, and scalable data solutions. The agenda of her presentation includes defining data science, who data scientists are and what they do, and how to start a career in data science. She discusses the wide availability of data, how data science makes sense of and provides feedback on data, common data science applications, and who employs data scientists. The presentation outlines the typical data science workflow and skills required, including domain knowledge, math/statistics, programming, communication/visualization, and how these skills can be obtained. It provides examples of data science
This document summarizes a seminar on machine learning using big data. It discusses the history of data storage and traditional databases. It then introduces machine learning and the types of learning, including supervised and unsupervised learning. Specific algorithms for each type are covered such as k-means clustering for unsupervised and naive Bayes for supervised. Case studies on applications like Amazon product recommendations are presented. The document concludes by discussing tools for machine learning and future applications as more connected devices generate extensive data.
Machine learning with Big Data power point presentationDavid Raj Kanthi
This is an article made form the articles of IEEE published in the year 2017
The following presentation has the slides for the Title called the
Machine Learning with Big data. that following presentation which has the challenges and approaches of machine learning with big data.
The integration of the Big Data with Machine Learning has so many challenges that Big data has and what is the approach made by the machine learning mechanism for those challenges.
The document discusses challenges and opportunities related to big data and high performance computing. It notes that computational power is increasing exponentially according to Moore's Law, but clock speeds have plateaued forcing a shift to multi-core processors. This is driving the need for parallel programming and new software approaches. Big data is also growing dramatically from various sources, such as sensors and social media. Analyzing this large, heterogeneous data requires new techniques in data mining, machine learning, and visualization. High performance computing and citizen science initiatives can help extract insights from big data to address important problems in health, environment, and other domains.
As we move into a new era of ITSM computing, new big data and machine learning tools and methodologies are being developed to support IT staff by intelligently extracting insights and making predictions from the enormous amounts of data accumulated from the organization. According to Gartner, I&O leaders must take a comprehensive approach to incorporate advanced big data and machine learning technologies into their organizations or risk becoming irrelevant. But what exactly is big data and machine learning all about? How can you introduce these concepts into your existing Service Desk?
Join USF’s distinguished Computer Science and Engineering Professor Lawrence Hall and SunView Software’s VP of Marketing and Product Strategy John Prestridge as they break down the fundamentals of big data and machine learning and provide real-world examples of the impact the technologies will have on ITSM.
Data Science - An emerging Stream of Science with its Spreading Reach & ImpactDr. Sunil Kr. Pandey
This is my presentation on the Topic "Data Science - An emerging Stream of Science with its Spreading Reach & Impact". I have compiled and collected different statistics and data from different sources. This may be useful for students and those who might be interested in this field of Study.
The document discusses the growth of data and the field of data science. It begins by noting the large amounts of data being generated daily by various sources like web/e-commerce transactions, social networks, and scientific projects. It then discusses some of the challenges of big data including volume, velocity, and variety. The document provides an overview of the multidisciplinary nature of data science and the skills required of data scientists. It also summarizes different approaches to and job roles in data science.
NYC Open Data Meetup-- Thoughtworks chief data scientist talkVivian S. Zhang
This document summarizes a presentation on data science consulting. It discusses:
1) The Agile Analytics group at ThoughtWorks which does data science consulting projects using probabilistic modeling, machine learning, and big data technologies.
2) Two case studies are described, including developing a machine learning model to improve matching of healthcare product data and using logistic regression for retail recommendation systems.
3) The origins and future of the field are discussed, noting that while not entirely new, data science has grown due to improvements in technology, programming languages, and libraries that have increased productivity and driven new career opportunities in the field.
Real-time applications of Data Science.pptxshalini s
This document provides an overview of data science through discussing big data challenges, defining data science, contrasting it with other fields, and presenting case studies. It explains that data science uses theories from fields like computer science, mathematics, and statistics to analyze large, complex data sets and help organizations make better decisions. Example applications discussed include using data science in healthcare to improve patient care, in elections to micro-target voters, and in cities to address urban challenges through data-driven solutions.
PAARL's 1st Marina G. Dayrit Lecture Series held at UP's Melchor Hall, 5F, Proctor & Gamble Audiovisual Hall, College of Engineering, on 3 March 2017, with Albert Anthony D. Gavino of Smart Communications Inc. as resource speaker on the topic "Using Big Data to Enhance Library Services"
The document provides an overview of data science and what it entails. It discusses the hype around big data and data science, and how data science has evolved due to improvements in technology that allow for large-scale data processing. It defines data science as a process that involves collecting, cleaning, analyzing and extracting meaningful insights from data. Data scientists come from a variety of academic backgrounds and work in both industry and academia developing solutions to real-world problems using data-driven approaches.
Data science applications can be found in many domains including business, healthcare, urban planning, and more. In business, data science is used to optimize operations and customer experiences. In healthcare, data science aims to improve efficiency, reduce readmissions, and enable earlier disease detection. For urban areas experiencing rapid growth, data science combines with urban informatics to help address challenges. Case studies show how data science is used in cancer research by leveraging large datasets and algorithms, in healthcare by Stanford and Google to advance precision medicine, in political elections through micro-targeting, and with the growing Internet of Things to analyze data from billions of connected devices.
Which institute is best for data science?DIGITALSAI1
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A comprehensive up-to-date Data Science course that includes all the essential topics of the Data Science domain, presented in a well-thought-out structure.
Taught and developed by experienced and certified data professionals, the course goes right from collecting raw digital data to presenting it visually. Suitable for those with computer backgrounds, analytic mindset, and coding knowledge.
Eduxfactor is an online data science training institution based in Hyderabad. A comprehensive up-to-date Data Science course that includes all the essential topics of the Data Science domain, presented in a well-thought-out structure.
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Exploring the EduXfactor Data Science Training program, you will learn components of the Data Science lifecycle such as Big Data, Hadoop, Machine Learning, Deep Learning & R programming. Our professional experts will teach you how to adopt a blend of mathematics, statistics, business acumen, tools, algorithms & machine learning techniques. You will learn how to handle a large amount of data information & process it according to any firm business strategy.
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A comprehensive up-to-date Data Science course that includes all the essential topics of the Data Science domain, presented in a well-thought-out structure.
Taught and developed by experienced and certified data professionals, the course goes right from collecting raw digital data to presenting it visually. Suitable for those with computer backgrounds, analytic mindset, and coding knowledge.
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Learn to collect, clean, and analyze big data with R. Understand how to employ appropriate modeling and methods of analytics to extract meaningful data for decision making.
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A comprehensive up-to-date Data Science course that includes all the essential topics of the Data Science domain, presented in a well-thought-out structure.
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EduXfactor is the top and best data science training institute in hyderabad offers data science training with 100% placement assistance with course certification.
Data science online training in hyderabadVamsiNihal
Exploring the EduXfactor Data Science Training program, you will learn components of the Data Science lifecycle such as Big Data, Hadoop, Machine Learning, Deep Learning & R programming. Our professional experts will teach you how to adopt a blend of mathematics, statistics, business acumen, tools, algorithms & machine learning techniques. You will learn how to handle a large amount of data information & process it according to any firm business strategy.
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A comprehensive up-to-date Data Science course that includes all the essential topics of the Data Science domain, presented in a well-thought-out structure.
Taught and developed by experienced and certified data professionals, the course goes right from collecting raw digital data to presenting it visually. Suitable for those with computer backgrounds, analytic mindset, and coding knowledge. Grasp the key fundamentals of data science, coding, and machine learning. Develop mastery over essential analytic tools like R, Python, SQL, and more.
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Similar to Hector Guerrero- Road to Business Analytics (20)
Recruiting in the Digital Age: A Social Media MasterclassLuanWise
In this masterclass, presented at the Global HR Summit on 5th June 2024, Luan Wise explored the essential features of social media platforms that support talent acquisition, including LinkedIn, Facebook, Instagram, X (formerly Twitter) and TikTok.
An introduction to the cryptocurrency investment platform Binance Savings.Any kyc Account
Learn how to use Binance Savings to expand your bitcoin holdings. Discover how to maximize your earnings on one of the most reliable cryptocurrency exchange platforms, as well as how to earn interest on your cryptocurrency holdings and the various savings choices available.
In the Adani-Hindenburg case, what is SEBI investigating.pptxAdani case
Adani SEBI investigation revealed that the latter had sought information from five foreign jurisdictions concerning the holdings of the firm’s foreign portfolio investors (FPIs) in relation to the alleged violations of the MPS Regulations. Nevertheless, the economic interest of the twelve FPIs based in tax haven jurisdictions still needs to be determined. The Adani Group firms classed these FPIs as public shareholders. According to Hindenburg, FPIs were used to get around regulatory standards.
The 10 Most Influential Leaders Guiding Corporate Evolution, 2024.pdfthesiliconleaders
In the recent edition, The 10 Most Influential Leaders Guiding Corporate Evolution, 2024, The Silicon Leaders magazine gladly features Dejan Štancer, President of the Global Chamber of Business Leaders (GCBL), along with other leaders.
How to Implement a Real Estate CRM SoftwareSalesTown
To implement a CRM for real estate, set clear goals, choose a CRM with key real estate features, and customize it to your needs. Migrate your data, train your team, and use automation to save time. Monitor performance, ensure data security, and use the CRM to enhance marketing. Regularly check its effectiveness to improve your business.
IMPACT Silver is a pure silver zinc producer with over $260 million in revenue since 2008 and a large 100% owned 210km Mexico land package - 2024 catalysts includes new 14% grade zinc Plomosas mine and 20,000m of fully funded exploration drilling.
Industrial Tech SW: Category Renewal and CreationChristian Dahlen
Every industrial revolution has created a new set of categories and a new set of players.
Multiple new technologies have emerged, but Samsara and C3.ai are only two companies which have gone public so far.
Manufacturing startups constitute the largest pipeline share of unicorns and IPO candidates in the SF Bay Area, and software startups dominate in Germany.
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This letter, written by Kellen Harkins, Course Director at Full Sail University, commends Anny Love's exemplary performance in the Video Sharing Platforms class. It highlights her dedication, willingness to challenge herself, and exceptional skills in production, editing, and marketing across various video platforms like YouTube, TikTok, and Instagram.
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Farman Ayaz Khattak and Ehtesham Matloob are government officials in CTW Counter terrorism wing Islamabad, in Federal Investigation Agency FIA Headquarters. CTW and FIA kidnapped crypto currency owner from Islamabad and snatched 200 Bitcoins those worth of 4 billion rupees in Pakistan currency. There is not Cryptocurrency Regulations in Pakistan & CTW is official dacoit and stealing digital assets from the innocent crypto holders and making fake cases of terrorism to keep them silent.
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4. Origins of the 3 Ingredients
• Probability/Statistics …..1700’s
• Early efforts to understand uncertainty (modern Stats 1900)
• Operations Research …..1940’s
• Attempt to bring greater efficiency to use of scare resources
• Computer Technology Science …..1940’s
• 1st Ph.D. in Computer Science awarded at Purdue in 1966
5. What is Data Science and a Data Scientist?
• “Data science, also known as data-driven science, is an interdisciplinary
field about scientific methods, processes, and systems to extract knowledge or
insights from data in various forms, either structured or unstructured, similar to
Knowledge Discovery in Databases (KDD).”
• “Data science is a "concept to unify statistics, data analysis and their related
methods" in order to "understand and analyze actual phenomena" with data. It
employs techniques and theories drawn from many fields within the broad
areas of mathematics, statistics, information science, and computer science,
in particular from the subdomains of machine learning, classification, cluster
analysis, data mining, databases, and visualization.”
Modified from… https://en.wikipedia.org/wiki/Data_science
6. According to Wikipedia--
“When Harvard Business Review called it "The Sexiest Job of the 21st
Century" the term became a buzzword, and is now often applied to
business analytics, or even arbitrary use of data, or used as a sexed-up
term for statistics. While many university programs now offer a data
science degree, there exists no consensus on a definition or curriculum
contents. Because of the current popularity of this term, there are many
"advocacy efforts" surrounding it.”
Modified from … https://en.wikipedia.org/wiki/Data_science
7. A Process Map of Data Science
https://en.wikipedia.org/wiki/Data_science
8. A simple timeline of lessons learned and observations
• 1966– off to Univ. Texas as an EE
• 1970– off to what would become Silicon Valley
• 1978/80– off to Univ. Texas-MBA/Univ. Washington-Ph.D.
• 1982– off to Tuck School at Dartmouth
• 1986– off to Notre Dame
• 1990– off to W&M
• 2017– off to retirement (?)
9. 1966– off to Univ. Texas as an EE
• No computers at my high school, or likely many high schools in that time.
During orientation all Engineering majors required to learn Fortran
programming an write a complex program in 2.5 days. Went from 4500 to 500
majors!
• Lesson-- It was hard to become an engineer at UT, and one way to cull the herd
is to terrorize students and see who survives
• Take first Operations Research classes—I’m in heaven!
• First Ph.D. in Computer Science offered at Purdue Univ.
10. 1970– off to what would become Silicon Valley
• Lockheed Missiles and Space company– 30k employees
• Play Pong by Atari at Andy Capp’s Tavern in Sunnyvale
• Realize that computers are going to be the most important tool in my professional life, and
that my training in math was equally important
• Attend Engineering Economics program at Stanford and introduced to Decision Analysis—
read about early AI concepts, Neural Networks, Rule-Based Systems, Bayesian analysis,
Logic (fuzzy), Expert Systems, etc.
• All seemed important, but a little distant due to lack of computer power– for the most part
is was conceptual. No way, or difficult, to actually use these concepts
11. 1978/80–off to Univ. Texas MBA/ Univ. Washington Ph.D.
• MBA was King/Queen of all Degrees
• I learned there were firms that would pay for abilities in operations research, but very focused (for
example--my ability to do time series forecast models)
• Ex. Later… can you build a model for efficient distribution of natural gas/purchase futures contracts?
• Learned to do modeling of many types—simulation, optimization, etc.
• Still, the capabilities of these techniques were limited by the processing capabilities of computers!
• My dissertation was typed manually—next year a student colleague used an IBM personal computer.
Ms. Lupe Lopez lost job—she had typed dissertations for 40 years (sad).
12. 1982– off to Tuck School at Dartmouth
Data General One
• one or two 3.5-inch floppy drives - the first
portable computer to incorporate the new
Sony 3.5-inch disks.
• a huge 11-inch display - the largest of any
portable computer - capable of displaying a
full 25 lines of text with 80 characters per
line.
• weighing only 10 pounds, it is significantly
lighter than competing CRT-based portable
system, like the IBM Portable
• up to eight hours of run time using the
internal rechargeable batteries.
• The MBA is still King/Queen as long as you are
Finance or Marketing Major– especially
Investment Banking. Jim Bradley was a student in
my classes and a real Geek!
• I was NOT a Dartmouth Man!
• I did begin to see a break to more high-tech jobs
and Entrepreneurship that required technology
• I was still using “baby problems”, “Little–Data” in
the classroom
13. 1986– off to Notre Dame
• I began research on Rule-Based Robotics—simple AI
• Excel comes to forefront as “the working man’s/woman’s analytic platform”
• I had Bill Jelen, Mr. Excel on the internet, in class– He convinced me!!
• Apple produces a video predicting the use of computers and smart
assistants
14. 1990– off to W&M
• Deep Blue (IBM) partially defeats Kasparov in Chess
• Watson was not far behind and more sophisticated use of AI
• Technology became omnipresent
• Could do real demos of analyses in classroom
• Students could follow and try themselves
• Statisticians debate whether they should call themselves Data Scientists
• Big Data and Analytics emerges as the way to compete
“Companies questing for killer apps generally focus all their firepower on the one area that promises to create the greatest
competitive advantage. But a new breed of company is upping the stakes. Organizations such as Amazon, Harrah’s,
Capital One, and the Boston Red Sox have dominated their fields by deploying industrial-strength analytics across a wide
variety of activities. In essence, they are transforming their organization.”
Competing on Analytics, Thomas H. Davenport, January 2006
15. 2017– off to retirement (?)
• I develop and teach an online Business Analytics class in our
OMBA– I was skeptical, but it’s a big success
• I teach Intermediate Probability and Statistics to our inaugural
MSBA class– soon to also be an online program
• I teach an online Business Analytics class to our MAcc program
• I develop an online Business Analytics for UGs
• I wonder if it was the right time to retire– then I remember IT WAS!
16. Where are we in this brave new world?
• What’s working and Hot? …..AI!!
• The future of the “customer experience”
• Replacement of humans in work
• Autonomous agents, including vehicles
• What’s the future?......AI!!
• Questions about displacement
• Questions about ethics
• Questions about the effect on human existence
18. A brief glossary of terms
http://data-informed.com/glossary-of-big-data-terms/ (modified through some omission)
19. Some important terms--
Algorithm
• A process or set of rules to be followed in calculations or other problem-solving
operations, especially by a computer.
Analytics
• The discovery, interpretation, and communication of meaningful patterns in data.
Artificial Intelligence
• The theory and development of computer systems able to perform tasks that
normally require human intelligence, such as visual perception, speech
recognition, decision-making, and translation between languages.
20. Contd.
Data management
According to the Data Management Association, data management incorporates the following practices needed to manage the full data lifecycle in
an enterprise:
data governance
data architecture, analysis, and design
database management
data security management
data quality management
reference and master data management
data warehousing and business intelligence management
document, record, and content management
metadata management
contact data management
Data mining
The process of deriving patterns or knowledge from large data sets.
Data science
A recent term that has multiple definitions, but generally accepted as a discipline that incorporates statistics, data visualization, computer
programming, data mining, machine learning, and database engineering to solve complex problems.
Data scientist
A practitioner of data science.
21. Data visualization
A visual abstraction of data designed for the purpose of deriving meaning or communicating
information more effectively.
Data warehouse
A place to store data for the purpose of reporting and analysis.
Database
A digital collection of data and the structure around which the data is organized. The data is typically
entered into and accessed via a database management system (DBMS).
Enterprise resource planning (ERP)
A software system that allows an organization to coordinate and manage all its resources, information,
and business functions.
Exploratory data analysis
An approach to data analysis focused on identifying general patterns in data, including outliers and
features of the data that are not anticipated by the experimenter’s current knowledge or
preconceptions. EDA aims to uncover underlying structure, test assumptions, detect mistakes, and
understand relationships between variables.
Contd.
22. Contd.
Internet of Things (IoT)
The network of physical objects or “things” embedded with electronics, software, sensors and connectivity to enable it to
achieve greater value and service by exchanging data with the manufacturer, operator and/or other connected devices. Each
thing is uniquely identifiable through its embedded computing system but is able to interoperate within the existing Internet
infrastructure.
Machine learning
A type of artificial intelligence (AI) that provides computers with the ability to learn without being explicitly programmed.
Machine learning focuses on the development of computer programs that can change when exposed to new data.
Metadata
Any data used to describe other data–for example, a data file’s size or date of creation.
Natural language processing
The ability of a computer program or system to understand human language. Applications of natural language processing
include enabling humans to interact with computers using speech, automated language translation, and deriving meaning
from unstructured data such as text or speech data.
NoSQL
A class of database management system that does not use the relational model. NoSQL is designed to handle large data
volumes that do not follow a fixed schema. It is ideally suited for use with very large data volumes that do not require the
relational model.
23. A more complete glossary
http://data-informed.com/glossary-of-big-data-terms/ (modified through some omissions)
24. Analytics and Big Data Glossary
Last updated: 3/16/17
Algorithm
A process or set of rules to be followed in calculations or other problem-solving operations, especially by a computer.
Analytics
The discovery, interpretation, and communication of meaningful patterns in data.
Analytics platform Application
Software that is designed to perform a specific task or suite of tasks.
Artificial Intelligence
The theory and development of computer systems able to perform tasks that normally require human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages.
Behavioral analytics
Using data about people’s behavior to understand intent and predict future actions.
Big data
This term has been defined in many ways, but along similar lines. Doug Laney, then an analyst at the META Group, first defined big data in a 2001 report called “3-D Data Management: Controlling Data Volume, Velocity and Variety.” Volume refers to the sheer size of the datasets. The McKinsey report, “Big Data: The Next Frontier
for Innovation, Competition, and Productivity,” expands on the volume aspect by saying that, “’Big data’ refers to datasets whose size is beyond the ability of typical database software tools to capture, store, manage, and analyze.”
Velocity refers to the speed at which the data is acquired and used. Not only are companies and organizations collecting more and more data at a faster rate, they want to derive meaning from that data as soon as possible, often in real time.
Variety refers to the different types of data that are available to collect and analyze in addition to the structured data found in a typical database. Barry Devlin of 9sight Consulting identifies four categories of information that constitute big data:
1. Machine-generated data. This includes RFID data, geolocation data from mobile devices, and data from monitoring devices such as utility meters.
2. Computer log data, such as clickstreams from websites.
3. Textual social media information from sources such as Twitter and Facebook.
4. Multimedia social and other information from Flickr, YouTube, and other similar sites.
Business intelligence (BI)
The general term used for the identification, extraction, and analysis of data.
Classification analysis
Data analysis for the purpose of assigning the data to a particular group or class.
Cloud
A broad term that refers to any Internet-based application or service that is hosted remotely.
Clustering analysis
Data analysis for the purpose of identifying similarities and differences among data sets so that similar data sets can be clustered together.
Computer-generated data
Any data generated by a computer rather than a human–a log file for example.
25. Contd.
Correlation analysis
• A means to determine a statistical relationship between variables, often for the purpose of identifying predictive factors among the variables.
• Correlation refers to any of a broad class of statistical relationships involving dependence. Familiar examples of dependent phenomena include the correlation between the physical statures of parents and their offspring, and the correlation
between the demand for a product and its price.
Customer relationship management (CRM)
• Software that helps businesses manage sales and customer service processes.
Dashboard
• A graphical reporting of static or real-time data on a desktop or mobile device. The data represented is typically high-level to give managers a quick report on status or performance.
Data
• A quantitative or qualitative value. Common types of data include sales figures, marketing research results, readings from monitoring equipment, user actions on a website, market growth projections, demographic information, and customer lists.
Data analytics
• The application of software to derive information or meaning from data. The end result might be a report, an indication of status, or an action taken automatically based on the information received.
Data analyst
• A person responsible for the tasks of modeling, preparing, and cleaning data for the purpose of deriving actionable information from it.
Data architecture and design
• How enterprise data is structured. The actual structure or design varies depending on the eventual end result required. Data architecture has three stages or processes: conceptual representation of business entities. the logical representation of
the relationships among those entities, and the physical construction of the system to support the functionality.
Data center
• A physical facility that houses a large number of servers and data storage devices. Data centers might belong to a single organization or sell their services to many organizations.
Data cleansing
• The act of reviewing and revising data to remove duplicate entries, correct misspellings, add missing data, and provide more consistency.
Data collection
• Any process that captures any type of data.
• The process of combining data from different sources and presenting it in a single view.
Data integrity
• The measure of trust an organization has in the accuracy, completeness, timeliness, and validity of the data.
26. Contd.Data management
• According to the Data Management Association, data management incorporates the following practices needed to manage the full data lifecycle in an enterprise:
• data governance
• data architecture, analysis, and design
• database management
• data security management
• data quality management
• reference and master data management
• data warehousing and business intelligence management
• document, record, and content management
• metadata management
• contact data management
Data marketplace
• A place where people can buy and sell data online.
Data mart
• The access layer of a data warehouse used to provide data to users.
Data migration
• The process of moving data between different storage types or formats, or between different computer systems.
Data mining
• The process of deriving patterns or knowledge from large data sets.
Data model, data modeling
• A data model defines the structure of the data for the purpose of communicating between functional and technical people to show data needed for business processes, or for communicating a plan to develop how data is stored and accessed among application
development team members.
Data science
• A recent term that has multiple definitions, but generally accepted as a discipline that incorporates statistics, data visualization, computer programming, data mining, machine learning, and database engineering to solve complex problems.
Data scientist
• A practitioner of data science.
27. Data security
• The practice of protecting data from destruction or unauthorized access.
Data structure
• A specific way of storing and organizing data.
Data visualization
• A visual abstraction of data designed for the purpose of deriving meaning or communicating information more effectively.
Data warehouse
• A place to store data for the purpose of reporting and analysis.
Database
• A digital collection of data and the structure around which the data is organized. The data is typically entered into and accessed via a database management system (DBMS).
Database administrator (DBA)
• A person, often certified, who is responsible for supporting and maintaining the integrity of the structure and content of a database.
Database management system (DBMS)
• Software that collects and provides access to data in a structured format.
Demographic data
• Data relating to the characteristics of a human population.
Distributed processing
• The execution of a process across multiple computers connected by a computer network.
Document management
• The practice of tracking and storing electronic documents and scanned images of paper documents.
Electronic health records (EHR)
• A digitized health record meant to be usable across different health care settings.
Enterprise resource planning (ERP)
• A software system that allows an organization to coordinate and manage all its resources, information, and business functions.
Exploratory data analysis
• An approach to data analysis focused on identifying general patterns in data, including outliers and features of the data that are not anticipated by the experimenter’s current knowledge or preconceptions. EDA aims to uncover underlying
structure, test assumptions, detect mistakes, and understand relationships between variables.
External data
• Data that exists outside of a system.
Contd.
28. Extract, transform, and load (ETL)
• A process used in data warehousing to prepare data for use in reporting or analytics.
Information management
• The practice of collecting, managing, and distributing information of all types–digital, paper-based, structured, unstructured.
• in-memory database
• Any database system that relies on memory for data storage.
• in-memory data grid (IMDG)
• The storage of data in memory across multiple servers for the purpose of greater scalability and faster access or analytics.
Internet of Things (IoT)
• The network of physical objects or “things” embedded with electronics, software, sensors and connectivity to enable it to achieve greater value and service by exchanging data with the manufacturer, operator and/or other connected devices. Each
thing is uniquely identifiable through its embedded computing system but is able to interoperate within the existing Internet infrastructure.
Location analytics
• Location analytics brings mapping and map-driven analytics to enterprise business systems and data warehouses. It allows you to associate geospatial information with datasets.
Location data
• Data that describes a geographic location.
Machine learning
• A type of artificial intelligence (AI) that provides computers with the ability to learn without being explicitly programmed. Machine learning focuses on the development of computer programs that can change when exposed to new data.
Metadata
• Any data used to describe other data–for example, a data file’s size or date of creation.
Multidimensional database
• A type of database that stores data as multidimensional arrays, or “cubes,” as opposed to the rows and column sotrage structure of relational databases. This enables data to be analyzed from different angles for complex queries and analytical
processing (OLAP) applications.
Natural language processing
• The ability of a computer program or system to understand human language. Applications of natural language processing include enabling humans to interact with computers using speech, automated language translation, and deriving meaning
from unstructured data such as text or speech data.
NoSQL
• A class of database management system that does not use the relational model. NoSQL is designed to handle large data volumes that do not follow a fixed schema. It is ideally suited for use with very large data volumes that do not require the
relational model.
Online analytical processing (OLAP)
• The process of analyzing multidimensional data using three operations: consolidation (the aggregation of available), drill-down (the ability for users to see the underlying details), and slice and dice (the ability for users to select subsets and view
them from different perspectives).
Contd.
29. Open source software
• Software with source code that is made available by the copyright holder free of charge to the general public. This code may be redistributed, and anyone can inspect and change it.
Pattern recognition
• The classification or labeling of an identified pattern in the machine learning process.
Petabyte
• One million gigabytes or 1,024 terabytes.
Predictive analytics
• Using statistical functions on one or more datasets to predict trends or future events.
Predictive modeling
• The process of developing a model that will most likely predict a trend or outcome.
Query analysis
• The process of analyzing a search query for the purpose of optimizing it for the best possible result.
R
• An open source software environment used for statistical computing.
Records management
• The process of managing an organization’s records throughout their entire lifecycle, from creation to disposal.
Risk analysis
• The application of statistical methods on one or more datasets to determine the likely risk of a project, action, or decision.
Root-cause analysis
• The process of determining the main cause of an event or problem.
Scalability
• The ability of a system or process to maintain acceptable performance levels as workload or scope increases.
Schema
• The structure that defines the organization of data in a database system.
Search
• The process of locating specific data or content using a search tool.
Contd.
30. Search data
• Aggregated data about search terms used over time.
Storage
• Any means of storing data persistently.
Structured data
• Data that is organized by a predetermined structure.
Structured Query Language (SQL)
• A programming language designed specifically to manage and retrieve data from a relational database system.
Terabyte
• 1,000 gigabytes.
Text analytics
• The application of statistical, linguistic, and machine learning techniques on text-based sources to derive meaning or insight.
Transactional data
• Data that changes unpredictably. Examples include accounts payable and receivable data, or data about product shipments.
Transparency
• As more data becomes openly available, the idea of proprietary data as a competitive advantage is diminished.
Unstructured data
• Data that has no identifiable structure – for example, the text of email messages.
Weather data
• Real-time weather data is now widely available for organizations to use in a variety of ways. For example, a logistics company can monitor local weather conditions to optimize the transport of goods. A utility company can adjust energy distribution
in real time.
Whole Earth Model
• An integrated data management system that allows geophysicists, engineers, and financial managers in the oil and gas industry evaluate the potential of oil and gas fields.
Contd.