Some comments on data science in general. This is a super basic introduction to the topic. I shared these slides with my team members @reBuy to help them understand how can I, as the data scientist, can help them.
Data Driven Product Vision - Dawn of the Data Age Lecture SeriesLuciano Pesci, PhD
This document summarizes a lecture on interpreting data like a professional. It outlines that the lecture will teach how data fuels product vision mapping, review the main data types and their uses in product development, and explain how to use frameworks and key performance indicators to achieve a product vision. The lecture also discusses defining and collecting structured versus unstructured data, using customer journey maps, personas, and customer lifetime value as frameworks to focus data interpretation, and selecting metrics that matter like net promoter score and customer retention rate.
Big Data Day LA 2016/ Data Science Track - Data Storytelling for Impact - Dav...Data Con LA
How can our data make the biggest impact? How do we find the stories worth sharing buried in our analytics? How important are visuals, hooks, connections, content? As data science and journalism have co-evolved, the potential for effectively communicating with data has skyrocketed. We'll look at case studies of impactful data stories and share the process for developing data stories that drive action.
This document discusses how analytics and data science can help businesses make data-driven decisions to power growth. It explains that while data science deals with identifying important questions, analytics focuses on answering those questions. The document then outlines three levels of analytics maturity - descriptive, predictive, and prescriptive analytics - and provides examples of solutions and tools used at each level to analyze past data, predict future trends, and prescribe optimal outcomes. Businesses can work with Grazitti to advance through these maturity levels and drive growth with data-driven insights.
This document summarizes a project that used R and advanced analytics to predict when ATM machines would become critically low in performance, saving the company 20 million euros. It describes the end-to-end process used, including data architecture, engineering, analytics, predictive modeling, visualization, and storytelling. Key aspects involved cleaning and validating data from multiple sources, developing predictive logic from past projects, creating metrics and visuals, and negotiating solutions with stakeholders.
This document discusses the use of data science in modern banking. It provides an overview of Raiffeisen Bank, which uses data science for applications like customer profiling, churn prediction, and fraud prevention. It then describes a datathon use case to build predictive models for new customers using external open data to supplement limited internal customer information. Finally, it outlines the daily work and benefits of being a data scientist at Raiffeisen Bank.
The New Role of Epertise: Open Science in a Web of Sensors, Senses and SemanticsJohn Blossom
The document discusses how the roles of experts and scientific publishing are changing in an era of open science, sensors, and big data. It argues that expertise is becoming more collaborative and less hierarchical as knowledge moves faster globally. Experts will need to focus on developing hypotheses, understanding signals in data, and adapting to new requirements for delivering insights. Scientific publishers will need services that support collaborative innovation and make better use of open data and signals to build insight and discovery. They are encouraged to focus on hypothesis generation and testing over static reports and consider how to adapt their business models.
Keynote lecture on 'Artificial Intelligence and Antitrust' delivered during the FSR C&M, CMPF and FCP Annual Scientific Seminar on 'Competition, Regulation and Pluralism in the Online World'
Data Driven Product Vision - Dawn of the Data Age Lecture SeriesLuciano Pesci, PhD
This document summarizes a lecture on interpreting data like a professional. It outlines that the lecture will teach how data fuels product vision mapping, review the main data types and their uses in product development, and explain how to use frameworks and key performance indicators to achieve a product vision. The lecture also discusses defining and collecting structured versus unstructured data, using customer journey maps, personas, and customer lifetime value as frameworks to focus data interpretation, and selecting metrics that matter like net promoter score and customer retention rate.
Big Data Day LA 2016/ Data Science Track - Data Storytelling for Impact - Dav...Data Con LA
How can our data make the biggest impact? How do we find the stories worth sharing buried in our analytics? How important are visuals, hooks, connections, content? As data science and journalism have co-evolved, the potential for effectively communicating with data has skyrocketed. We'll look at case studies of impactful data stories and share the process for developing data stories that drive action.
This document discusses how analytics and data science can help businesses make data-driven decisions to power growth. It explains that while data science deals with identifying important questions, analytics focuses on answering those questions. The document then outlines three levels of analytics maturity - descriptive, predictive, and prescriptive analytics - and provides examples of solutions and tools used at each level to analyze past data, predict future trends, and prescribe optimal outcomes. Businesses can work with Grazitti to advance through these maturity levels and drive growth with data-driven insights.
This document summarizes a project that used R and advanced analytics to predict when ATM machines would become critically low in performance, saving the company 20 million euros. It describes the end-to-end process used, including data architecture, engineering, analytics, predictive modeling, visualization, and storytelling. Key aspects involved cleaning and validating data from multiple sources, developing predictive logic from past projects, creating metrics and visuals, and negotiating solutions with stakeholders.
This document discusses the use of data science in modern banking. It provides an overview of Raiffeisen Bank, which uses data science for applications like customer profiling, churn prediction, and fraud prevention. It then describes a datathon use case to build predictive models for new customers using external open data to supplement limited internal customer information. Finally, it outlines the daily work and benefits of being a data scientist at Raiffeisen Bank.
The New Role of Epertise: Open Science in a Web of Sensors, Senses and SemanticsJohn Blossom
The document discusses how the roles of experts and scientific publishing are changing in an era of open science, sensors, and big data. It argues that expertise is becoming more collaborative and less hierarchical as knowledge moves faster globally. Experts will need to focus on developing hypotheses, understanding signals in data, and adapting to new requirements for delivering insights. Scientific publishers will need services that support collaborative innovation and make better use of open data and signals to build insight and discovery. They are encouraged to focus on hypothesis generation and testing over static reports and consider how to adapt their business models.
Keynote lecture on 'Artificial Intelligence and Antitrust' delivered during the FSR C&M, CMPF and FCP Annual Scientific Seminar on 'Competition, Regulation and Pluralism in the Online World'
Building a Data Culture at Your Organization - Dawn of the Data Age Lecture S...Luciano Pesci, PhD
90% of all the data in existence was generated in the last 2 years and the pace is accelerating (really fast). Yet this data seems to be drowning organizations and 80% of all data projects are currently failing. This means that organizations who successfully use their data are in possession of a major competitive advantage. But it won't last, and eventually, everyone will be expected to have broad data literacy, just like the need to know how to type or making copies.
This Lecture Will:
-TEACH YOU THE STATE OF DATA TODAY WITH EXAMPLES OF FAILURE & SUCCESS
-EXPLAIN THE 4 DIFFERENT TYPES OF DATA SCIENTISTS AND THEIR TOOLS
-OUTLINE EFFECTIVE DATA SCIENCE TEAMS, ALONG WITH THEIR COST
-SHOW YOU HOW TO BUILD A DATA CULTURE AT YOUR ORGANIZATION
You can watch this webinar here: https://youtu.be/KMMvChAYV2g
Lifetime Value - The Only Metric That Matters (DMC September 2018)Luciano Pesci, PhD
Lifetime value (LTV) is the single most impactful metric for marketers to know since 20% of customers predictably contribute 80% of the total lifetime value. To understand this "Pareto Persona" you need to map data from every touchpoint in the customer journey, break down internal data silos, and adopt powerful frameworks like LTV for organizing & explaining data.
With lifetime value, you can optimize cost of acquisition decisions based on a persona that will have the highest satisfaction, longest lifecycle, and greatest likelihood to recommend you to their network. As a bonus, your product, sales, and customer experience teams will also benefit from knowing lifetime value, making you the hero of the day for delivering unparalleled ROI with data (possibly for the first time in your organization's history).
You can watch this presentation here: https://youtu.be/6x3Z7uRtvFc
Luciano Pesci, PhD is the Chief Executive Officer of Emperitas which spoke on the subject of Lifetime Value the Only Metric that Matters and how it is beneficial to business.
For those that don’t understand much about marketing or business, the term Lifetime Value could be a little confusing. Lifetime Value has been defined by Google as a way to understand a customer’s revenue potential. Forbes has defined it as a measure of the profit you can expect to generate from a customer over the entire time they do business with you. It is one of the most impactful metrics for marketers to understand as 20% of customers predictably contribute 80% of total lifetime value.
This document discusses data science use cases and tools. It describes how data science is used for digital advertisement by analyzing user data to determine advertisement relevance and estimate click-through and conversion probabilities. It also discusses how data science is used for online classifieds moderation through automated detection of illegal items, duplicates, and other violations using machine learning models. Finally, it outlines some base skills for data scientists, such as SQL, Python, machine learning libraries, and model training and evaluation.
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.
Game analytics can provide valuable insights into user acquisition, retention, engagement, and monetization. Key metrics include the acquisition cost of new users, how many users are retained over time, how engaged users are with the game, and how often engagement leads to revenue. Retention is particularly important as it provides more opportunities for conversion and repeat purchases over time. Analytics can help optimize the game experience and identify which users are most likely to engage and be retained by focusing on specific in-game events and behaviors rather than averages. The data collected should then be used to test changes, provide personalized experiences, and continuously improve the game.
Welcome To The Data Age - Dawn of the Data Age Lecture SeriesLuciano Pesci, PhD
Over the last year, this lecture series has focused on how data & research are impacting organizations and their various departments. From defining a data culture, showing agile research methods, providing theoretical frameworks, and data-driving processes galore, we did this to paint a picture of what the data age is during its passing nascent moment. Yet the most exciting part of our data story is about what will happen next and how it will help create a future beyond anyone's imagination.
This Lecture Will:
-TEACH THE BENEFITS OF THE DATA AGE.
-SHOW HOW DATA IS TRANSFORMING THE WORLD.
-EXPLAIN WHAT'S ON THE DATA HORIZON.
You can watch this lecture here: https://youtu.be/EfFszR23bVw
How can I become a data scientist? What are the most valuable skills to learn for a data scientist now? Could I learn how to be a data scientist by going through online tutorials? What does a data scientist do?
These are only some of the questions that are being discussed online, on blogs, on forums and on knowledge-sharing platforms like Quora.
Let me share the Beginner's Guide to Data Science which will be really helpful to you.
Also Checkout: http://bit.ly/2Mub6xP
This document provides an overview of big data and discusses key concepts. It begins by defining big data and noting the increasing volume, velocity and variety of data being created. It then covers the big data landscape including storage models and technologies like Hadoop, analytics techniques like machine learning, and visualization. Finally, it discusses business uses cases and how big data is impacting industries and creating new business models through insights gained from 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.
A step towards machine learning at accionlabsChetan Khatri
This document provides an overview of machine learning including definitions of common techniques like supervised learning, unsupervised learning, and reinforcement learning. It discusses applications of machine learning across various domains like vision, natural language processing, and speech recognition. Additionally, it outlines machine learning life cycles and lists tools, technologies, and resources for learning and practicing machine learning.
In this presentation I list and try to answer some useful questions about machine learning, and large-scale machine learning in particular.
I talk about things like what we can and cannot do with ML, do I need a cluster for large-scale ML, what are common problems with ML systems and future directions.
IEEE Standards Impact in IoT and 5G, Day 2 - Architectural Requirements for S...Peter Waher
The presentation on Architectural Requirements for Smart Cities on the second day of the "IEEE Standards Impact in IoT and 5G" conference in Bangalore, India, describes the vision of a Smart City and shows that there are two paths to building a Smart City. Either Top/Down or Bottom/Up. The presentation describes Open Societies, and how to create Digital equivalents of Open Societies, or Open Smart Societies. It shows how standards, interoperability, monetization, privacy and security are key factors, and how IEEE 1451.99 can help lay a strong foundation for a Smart City.
How to bridge the gap between Statisticans and Business folks ? Flutura outli...fluturadsa
Flutura has always believed that when the world of business collides with the world of math, magic unfolds. As these 2 worlds collide, it also presents a set of unique challenges - bridging the semantic language gap between business and math. Modelling complex business outcomes using math requires an interdisciplinary team consisting of business folks, data folks and math folks. While doing so business folks are always at a loss because a language chasm exists. Math folks love their ”geek speak” ( Tanimoto coefficient, chi square, odds ratio) and business folks are focussed on impactful outcomes (Mean time between failure, Next best action etc.).
22 non statistical questions for a statistician v2Derick Jose
The document lists 22 non-statistical questions for a statistician to consider when building a predictive model, including questions about the desired business outcomes, economic and non-economic impacts of correct and incorrect predictions, representativeness of past data for future predictions, and ensuring the model accounts for multiple possible outcomes rather than just reinforcing preexisting views. It concludes by providing brief information about Flutura, a company that uses data and predictive modeling to help asset-intensive industries improve business outcomes.
2016 XUG Conference Big Data: Big Deal for Personalized Communications or Meh?Jeffrey Stewart
What is the big deal with big data? Why is everyone talking about it? What, if anything, is anyone doing with it?
This session will discuss big data, starting with a definition of the 4 Vs and diving into the current and potential uses in personalized communication.
What is different from traditional data management and business intelligence is the sheer size of the datasets and the quality of sources of relevant data.
Each source has different structures, and the frequency of updates is faster than ever before. How can all of data from all facets of human activity be related? How can they be combined and analyzed to help us understand individuals and how they want to be communicated to individually?
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.
Building a Data Culture at Your Organization - Dawn of the Data Age Lecture S...Luciano Pesci, PhD
90% of all the data in existence was generated in the last 2 years and the pace is accelerating (really fast). Yet this data seems to be drowning organizations and 80% of all data projects are currently failing. This means that organizations who successfully use their data are in possession of a major competitive advantage. But it won't last, and eventually, everyone will be expected to have broad data literacy, just like the need to know how to type or making copies.
This Lecture Will:
-TEACH YOU THE STATE OF DATA TODAY WITH EXAMPLES OF FAILURE & SUCCESS
-EXPLAIN THE 4 DIFFERENT TYPES OF DATA SCIENTISTS AND THEIR TOOLS
-OUTLINE EFFECTIVE DATA SCIENCE TEAMS, ALONG WITH THEIR COST
-SHOW YOU HOW TO BUILD A DATA CULTURE AT YOUR ORGANIZATION
You can watch this webinar here: https://youtu.be/KMMvChAYV2g
Lifetime Value - The Only Metric That Matters (DMC September 2018)Luciano Pesci, PhD
Lifetime value (LTV) is the single most impactful metric for marketers to know since 20% of customers predictably contribute 80% of the total lifetime value. To understand this "Pareto Persona" you need to map data from every touchpoint in the customer journey, break down internal data silos, and adopt powerful frameworks like LTV for organizing & explaining data.
With lifetime value, you can optimize cost of acquisition decisions based on a persona that will have the highest satisfaction, longest lifecycle, and greatest likelihood to recommend you to their network. As a bonus, your product, sales, and customer experience teams will also benefit from knowing lifetime value, making you the hero of the day for delivering unparalleled ROI with data (possibly for the first time in your organization's history).
You can watch this presentation here: https://youtu.be/6x3Z7uRtvFc
Luciano Pesci, PhD is the Chief Executive Officer of Emperitas which spoke on the subject of Lifetime Value the Only Metric that Matters and how it is beneficial to business.
For those that don’t understand much about marketing or business, the term Lifetime Value could be a little confusing. Lifetime Value has been defined by Google as a way to understand a customer’s revenue potential. Forbes has defined it as a measure of the profit you can expect to generate from a customer over the entire time they do business with you. It is one of the most impactful metrics for marketers to understand as 20% of customers predictably contribute 80% of total lifetime value.
This document discusses data science use cases and tools. It describes how data science is used for digital advertisement by analyzing user data to determine advertisement relevance and estimate click-through and conversion probabilities. It also discusses how data science is used for online classifieds moderation through automated detection of illegal items, duplicates, and other violations using machine learning models. Finally, it outlines some base skills for data scientists, such as SQL, Python, machine learning libraries, and model training and evaluation.
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.
Game analytics can provide valuable insights into user acquisition, retention, engagement, and monetization. Key metrics include the acquisition cost of new users, how many users are retained over time, how engaged users are with the game, and how often engagement leads to revenue. Retention is particularly important as it provides more opportunities for conversion and repeat purchases over time. Analytics can help optimize the game experience and identify which users are most likely to engage and be retained by focusing on specific in-game events and behaviors rather than averages. The data collected should then be used to test changes, provide personalized experiences, and continuously improve the game.
Welcome To The Data Age - Dawn of the Data Age Lecture SeriesLuciano Pesci, PhD
Over the last year, this lecture series has focused on how data & research are impacting organizations and their various departments. From defining a data culture, showing agile research methods, providing theoretical frameworks, and data-driving processes galore, we did this to paint a picture of what the data age is during its passing nascent moment. Yet the most exciting part of our data story is about what will happen next and how it will help create a future beyond anyone's imagination.
This Lecture Will:
-TEACH THE BENEFITS OF THE DATA AGE.
-SHOW HOW DATA IS TRANSFORMING THE WORLD.
-EXPLAIN WHAT'S ON THE DATA HORIZON.
You can watch this lecture here: https://youtu.be/EfFszR23bVw
How can I become a data scientist? What are the most valuable skills to learn for a data scientist now? Could I learn how to be a data scientist by going through online tutorials? What does a data scientist do?
These are only some of the questions that are being discussed online, on blogs, on forums and on knowledge-sharing platforms like Quora.
Let me share the Beginner's Guide to Data Science which will be really helpful to you.
Also Checkout: http://bit.ly/2Mub6xP
This document provides an overview of big data and discusses key concepts. It begins by defining big data and noting the increasing volume, velocity and variety of data being created. It then covers the big data landscape including storage models and technologies like Hadoop, analytics techniques like machine learning, and visualization. Finally, it discusses business uses cases and how big data is impacting industries and creating new business models through insights gained from 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.
A step towards machine learning at accionlabsChetan Khatri
This document provides an overview of machine learning including definitions of common techniques like supervised learning, unsupervised learning, and reinforcement learning. It discusses applications of machine learning across various domains like vision, natural language processing, and speech recognition. Additionally, it outlines machine learning life cycles and lists tools, technologies, and resources for learning and practicing machine learning.
In this presentation I list and try to answer some useful questions about machine learning, and large-scale machine learning in particular.
I talk about things like what we can and cannot do with ML, do I need a cluster for large-scale ML, what are common problems with ML systems and future directions.
IEEE Standards Impact in IoT and 5G, Day 2 - Architectural Requirements for S...Peter Waher
The presentation on Architectural Requirements for Smart Cities on the second day of the "IEEE Standards Impact in IoT and 5G" conference in Bangalore, India, describes the vision of a Smart City and shows that there are two paths to building a Smart City. Either Top/Down or Bottom/Up. The presentation describes Open Societies, and how to create Digital equivalents of Open Societies, or Open Smart Societies. It shows how standards, interoperability, monetization, privacy and security are key factors, and how IEEE 1451.99 can help lay a strong foundation for a Smart City.
How to bridge the gap between Statisticans and Business folks ? Flutura outli...fluturadsa
Flutura has always believed that when the world of business collides with the world of math, magic unfolds. As these 2 worlds collide, it also presents a set of unique challenges - bridging the semantic language gap between business and math. Modelling complex business outcomes using math requires an interdisciplinary team consisting of business folks, data folks and math folks. While doing so business folks are always at a loss because a language chasm exists. Math folks love their ”geek speak” ( Tanimoto coefficient, chi square, odds ratio) and business folks are focussed on impactful outcomes (Mean time between failure, Next best action etc.).
22 non statistical questions for a statistician v2Derick Jose
The document lists 22 non-statistical questions for a statistician to consider when building a predictive model, including questions about the desired business outcomes, economic and non-economic impacts of correct and incorrect predictions, representativeness of past data for future predictions, and ensuring the model accounts for multiple possible outcomes rather than just reinforcing preexisting views. It concludes by providing brief information about Flutura, a company that uses data and predictive modeling to help asset-intensive industries improve business outcomes.
2016 XUG Conference Big Data: Big Deal for Personalized Communications or Meh?Jeffrey Stewart
What is the big deal with big data? Why is everyone talking about it? What, if anything, is anyone doing with it?
This session will discuss big data, starting with a definition of the 4 Vs and diving into the current and potential uses in personalized communication.
What is different from traditional data management and business intelligence is the sheer size of the datasets and the quality of sources of relevant data.
Each source has different structures, and the frequency of updates is faster than ever before. How can all of data from all facets of human activity be related? How can they be combined and analyzed to help us understand individuals and how they want to be communicated to individually?
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.
"Financial Odyssey: Navigating Past Performance Through Diverse Analytical Lens"sameer shah
Embark on a captivating financial journey with 'Financial Odyssey,' our hackathon project. Delve deep into the past performance of two companies as we employ an array of financial statement analysis techniques. From ratio analysis to trend analysis, uncover insights crucial for informed decision-making in the dynamic world of finance."
Beyond the Basics of A/B Tests: Highly Innovative Experimentation Tactics You...Aggregage
This webinar will explore cutting-edge, less familiar but powerful experimentation methodologies which address well-known limitations of standard A/B Testing. Designed for data and product leaders, this session aims to inspire the embrace of innovative approaches and provide insights into the frontiers of experimentation!
Open Source Contributions to Postgres: The Basics POSETTE 2024ElizabethGarrettChri
Postgres is the most advanced open-source database in the world and it's supported by a community, not a single company. So how does this work? How does code actually get into Postgres? I recently had a patch submitted and committed and I want to share what I learned in that process. I’ll give you an overview of Postgres versions and how the underlying project codebase functions. I’ll also show you the process for submitting a patch and getting that tested and committed.
Build applications with generative AI on Google CloudMárton Kodok
We will explore Vertex AI - Model Garden powered experiences, we are going to learn more about the integration of these generative AI APIs. We are going to see in action what the Gemini family of generative models are for developers to build and deploy AI-driven applications. Vertex AI includes a suite of foundation models, these are referred to as the PaLM and Gemini family of generative ai models, and they come in different versions. We are going to cover how to use via API to: - execute prompts in text and chat - cover multimodal use cases with image prompts. - finetune and distill to improve knowledge domains - run function calls with foundation models to optimize them for specific tasks. At the end of the session, developers will understand how to innovate with generative AI and develop apps using the generative ai industry trends.
6. Wiki’s version
“...is an interdisciplinary field about scientific methods, processes, and
systems to extract knowledge or insights from data in various forms,
either structured or unstructured…” (link)
7. What are the fields?
● Mathematics
● Computer science
● Visualization
● Business domain knowledge
● Communication/presentation skills
8. What kind of knowledge/insight?
● Ads matching
● Stock market behavior
● Accidents avoidance
● Strawberries grading
● Energy management
● Fraud detection
● …
10. Data science tracks
● Descriptive analytics: “What happened?”
● Predictive analytics: “What will happen?”
● Prescriptive analytics: “How can we make it happen?”
16. Is this A or B (or C)? (cont’)
● Will a given user convert or not?
● Does 5€ voucher is better over 10% discount?
● Is it a dragon or a unicorn?
⇨ Classification
24. How is it organized? (cont’)
● To which group of user is this new user similar?
● Where is the user’s home/work?
⇨ Clustering
25. What should I do now?
● Should the price of Harry Potter increase or decrease?
● Should server resources be boosted, kept or reduced?
⇨ (e.g) Reinforcement