Customer case study at Databricks Unified Analytics Workshop:
Data Science challenges @ Outreach and how we leverage Databricks.
Event: https://pages.databricks.com/Unified-Analytics-Seattle-Reg.html
Do you know the two most important questions to ask yourself BEFORE you start developing Excel models? And how can you avoid wasting precious time when you develop your next Excel model?
This short presentation provides you the answers, and enables you to spend time on perfecting your next model when and if it is really required.
Based on the book "The Aspiring Advisor - Strategies and Tools for a Successful Consulting Career", the number 1 handbook for consulting career starters.
More info: www.AspiringAdvisor.com
How we built NoCRM - Piotr Karwatka, CTO of DivanteDataconomy Media
1. The document proposes a concept for a CRM that works in the background through natural language processing and machine learning of users' email communications, without requiring direct user engagement with a traditional CRM interface.
2. Key aspects of the proposed "NoCRM" system include automatically discovering and classifying leads, contacts, deals and offers from emails to build a knowledge graph and sales pipeline, and using analysis of communication patterns to predict sales outcomes and provide coaching recommendations to improve performance.
3. The system would be deployed by authorizing access to company email accounts, then monitoring all communications to discover useful patterns and insights without users having to manually log activities in a CRM.
1) The document discusses a self-study approach to learning data science through project-based learning using various online resources.
2) It recommends breaking down projects into 5 steps: defining problems/solutions, data extraction/preprocessing, exploration/engineering, model implementation, and evaluation.
3) Each step requires different skillsets from domains like statistics, programming, SQL, visualization, mathematics, and business knowledge.
This document provides an introduction to the concepts of data science. It defines data science as an interdisciplinary field drawing from computer science, statistics, and application domains. The document outlines the typical workflow of a data scientist, including obtaining data, exploring it, cleaning it, performing analysis, drawing conclusions, and reporting results. It describes the focus areas of the course as mathematics, technology, visualization, and communication skills. The document emphasizes the importance of learning new skills independently and communicating results effectively to non-technical audiences.
The document describes an evaluation of informal learning in healthcare using digital tools. It outlines three pilot programs involving practice managers and healthcare professionals testing the Learning Layers conferencing tool. Data was collected through workshops, interviews and usage logs to understand challenges, barriers and how work practices were impacted. Analysis found tool usability, time constraints, traditional work habits and lack of priority were barriers. Ensuring intuitive tools, strong leadership and addressing login fatigue are needed for successful adoption of digital learning in healthcare.
NCV 3 Business Practice Hands-On Support Slide Show - Module 6Future Managers
The document outlines the steps to plan and conduct basic marketing research and present findings. It discusses identifying a research problem, planning the project, collecting primary and secondary data through various qualitative and quantitative methods, analyzing and interpreting the findings, preparing a brief written report on the results, and presenting the research orally. Students are provided activities to practice each step of the research process using a hypothetical project researching student preferences for the college cafeteria's food options.
Delivered at Pittsburgh Tech Fest - 6/10/2017
Knowledge is power, but is it if you're not using it? What if the application you delivered to your customers was extremely intelligent? It could retrieve, analyze and use the massive amounts of data that businesses are generating at an astronomical rate.
It could analyze business deals, predict potential issues, proactively recommend business decisions and estimate profit, loss and risks.
Those things provide direct benefits to your company. Churning through that data by hand doesn't. Enter Azure Machine Learning.
In this session you will learn how to integrate Azure Machine Learning into your existing applications and workflows with REST services. You will learn how to deliver a modular, maintainable solution to your customers that allows them to analyze their data.
You will learn to:
* Numerous ways to abstract business rules, workflows, AI (Machine Learning) and more into your applications
* How to Integrate Azure Machine Learning into your existing Applications and Processes
* Create Azure Machine Learning Experiments
* Retrieve the Score from an Azure Machine Learning Experiment and integrate it into your applications and processes
* Integrate numerous Machine Learning Experiments from the Azure Machine Learning Marketplace into your existing applications and processes
* Learn various concepts for abstracting and managing services and api's.
Do you know the two most important questions to ask yourself BEFORE you start developing Excel models? And how can you avoid wasting precious time when you develop your next Excel model?
This short presentation provides you the answers, and enables you to spend time on perfecting your next model when and if it is really required.
Based on the book "The Aspiring Advisor - Strategies and Tools for a Successful Consulting Career", the number 1 handbook for consulting career starters.
More info: www.AspiringAdvisor.com
How we built NoCRM - Piotr Karwatka, CTO of DivanteDataconomy Media
1. The document proposes a concept for a CRM that works in the background through natural language processing and machine learning of users' email communications, without requiring direct user engagement with a traditional CRM interface.
2. Key aspects of the proposed "NoCRM" system include automatically discovering and classifying leads, contacts, deals and offers from emails to build a knowledge graph and sales pipeline, and using analysis of communication patterns to predict sales outcomes and provide coaching recommendations to improve performance.
3. The system would be deployed by authorizing access to company email accounts, then monitoring all communications to discover useful patterns and insights without users having to manually log activities in a CRM.
1) The document discusses a self-study approach to learning data science through project-based learning using various online resources.
2) It recommends breaking down projects into 5 steps: defining problems/solutions, data extraction/preprocessing, exploration/engineering, model implementation, and evaluation.
3) Each step requires different skillsets from domains like statistics, programming, SQL, visualization, mathematics, and business knowledge.
This document provides an introduction to the concepts of data science. It defines data science as an interdisciplinary field drawing from computer science, statistics, and application domains. The document outlines the typical workflow of a data scientist, including obtaining data, exploring it, cleaning it, performing analysis, drawing conclusions, and reporting results. It describes the focus areas of the course as mathematics, technology, visualization, and communication skills. The document emphasizes the importance of learning new skills independently and communicating results effectively to non-technical audiences.
The document describes an evaluation of informal learning in healthcare using digital tools. It outlines three pilot programs involving practice managers and healthcare professionals testing the Learning Layers conferencing tool. Data was collected through workshops, interviews and usage logs to understand challenges, barriers and how work practices were impacted. Analysis found tool usability, time constraints, traditional work habits and lack of priority were barriers. Ensuring intuitive tools, strong leadership and addressing login fatigue are needed for successful adoption of digital learning in healthcare.
NCV 3 Business Practice Hands-On Support Slide Show - Module 6Future Managers
The document outlines the steps to plan and conduct basic marketing research and present findings. It discusses identifying a research problem, planning the project, collecting primary and secondary data through various qualitative and quantitative methods, analyzing and interpreting the findings, preparing a brief written report on the results, and presenting the research orally. Students are provided activities to practice each step of the research process using a hypothetical project researching student preferences for the college cafeteria's food options.
Delivered at Pittsburgh Tech Fest - 6/10/2017
Knowledge is power, but is it if you're not using it? What if the application you delivered to your customers was extremely intelligent? It could retrieve, analyze and use the massive amounts of data that businesses are generating at an astronomical rate.
It could analyze business deals, predict potential issues, proactively recommend business decisions and estimate profit, loss and risks.
Those things provide direct benefits to your company. Churning through that data by hand doesn't. Enter Azure Machine Learning.
In this session you will learn how to integrate Azure Machine Learning into your existing applications and workflows with REST services. You will learn how to deliver a modular, maintainable solution to your customers that allows them to analyze their data.
You will learn to:
* Numerous ways to abstract business rules, workflows, AI (Machine Learning) and more into your applications
* How to Integrate Azure Machine Learning into your existing Applications and Processes
* Create Azure Machine Learning Experiments
* Retrieve the Score from an Azure Machine Learning Experiment and integrate it into your applications and processes
* Integrate numerous Machine Learning Experiments from the Azure Machine Learning Marketplace into your existing applications and processes
* Learn various concepts for abstracting and managing services and api's.
Thripp EME 2040 Slides on Microsoft Education Badges, PowerPoint Quiz, Comput...Richard Thripp
My slides as an instructor for EME 2040: Introduction to Technology for Educators at University of Central Florida, for a meeting late in the Spring 2018 semester where I discussed and demonstrated earning a micro-credential (certificate or "badge") from Microsoft Education or Google; making a quiz in PowerPoint using media and relative hyperlinks; principles of computer backup, data loss, Internet security, and digital security; and grading concerns for the course.
This document provides information on how to become a software developer. It begins with an introduction that defines a software developer and notes they design, implement, and test software. It also provides the median salary range of $84,200 and high job growth of 24.6% expected through 2020. The document then outlines the roles and responsibilities of a software developer, and the requirements which do not necessarily include a computer science degree. It provides 10 steps to become a software developer including choosing a pet project, learning from books and online courses, implementing the pet project, asking questions online, and contributing to open source projects. It concludes with a list of free resources for learning C#.
The document describes RAPIDS, a rapid authoring platform for instructional design of scenarios. It aims to allow instructors to easily and quickly create scenarios for learners to understand and apply knowledge management techniques. The platform includes a scenarios database, assessments, and e-learning tools. It was tested in a pilot where participants used the scenario building tool to create and reconstruct knowledge management scenarios. Feedback indicated the tool was easy to use but could be improved by adding features like layer controls and motion. The goal is to help instructors create more engaging learning content without extensive time and effort.
Learning Management Systems for Nonprofits – Net2van July 9 2019NetSquared Vancouver
A Learning Management System (LMS) might be just the tool your organization needs to deliver your education or program content—or maybe not. Investing in an LMS is a big decision that requires ongoing resources to successfully implement and maintain. There are some key questions to ask before acquiring an LMS and best practices to follow when moving ahead with purchasing, implementing and administering an LMS.
Get the inside scoop from non-profit colleagues and non-profit learning consultants who will share their tips for success, pitfalls to avoid, and demo how they’re using this online learning technology.
What you’ll get from attending:
• Clarity on what an LMS is (and isn’t) and what it can (and can’t) do
• An eLearning terminology cheat sheet (re: the basic nuts and bolts of online learning delivery)
• An overview of some of the most common LMS options for small to medium non-profits
• Top LMS mistakes to avoid and tips for success
• LMS use case stories from a panel of 3 small to medium non-profits (why they invested in an LMS, and how they’re using it).
• Steps your organization can take right now (or later) to determine if an LMS is right for you.
ABOUT LEAH CHANG
@mmeleahchang
linkedin.com/in/mmeleahchang/
leahchang.ca
Leah has 16+ years of combined experience in education working with non-profits, private companies and public authorities to develop online and classroom learning. She specializes in Learning Management System matching, configuration and workflow, systems learning and instructional design. She loves learning about new systems and tools that might shape the future of work or help non-profits streamline their operations. She currently serves on the board of directors of the Arts Council of New Westminster, and worked with Pain BC Society as a communications and education consultant and Education Lead.
This document provides an overview of supervised learning concepts including:
- The steps in formulating a supervised learning problem including collecting labeled data, choosing a model and evaluation metric, and an optimization method.
- The dangers of overfitting when measuring performance on training data and the solution of splitting data into training and testing sets.
- An overview of Python libraries and frameworks commonly used for data science and machine learning tasks like the Scikit-learn, NumPy, Pandas, and TensorFlow libraries.
I was invited to present a master class on elearning implmentation at the 2005 eLNet Conference. I covered Westpac\'s launch of their eAcademy system and the lessons learnt.
Data Science. Business Analytics is the statistical study of business data to gain insights. Data science is the study of data using statistics, algorithms and technology. Uses mostly structured data. Uses both structured and unstructured data.
Two simples and quick ways to Save and Share your company Knowledge Jeff ANGAMA
Presentation at Singapore SharePoint Community Meeting - December 2015 - https://www.meetup.com/mssgug/events/226965173/
Employee turnover is inevitable.
Companies needs innovation.
Knowledge Management is an asset for the Entreprise Brain, the information accessible by every employee.
People shall share their knowledge to other colleagues through solutions that can research information, such as SharePoint or Office 365.
If you already have SharePoint, following this to implement a KM Solution in less than 1hour using SharePoint
Open learner models (OLMs) and learning analytics dashboards: A systematic re...Daniel Davis
This document summarizes a systematic review comparing Open Learner Models (OLMs) and learning analytics dashboards. The review found that OLMs and learning analytics dashboards both aim to provide learners with interfaces to monitor their progress, support planning and self-reflection. However, OLMs were developed in parallel with educational platforms, while learning analytics dashboards developed independently. The review identified trends in OLM research over time, central themes like self-regulated learning and reflection, and compared data modeling and evaluations between OLMs and dashboards. It recommends unifying terminology between the fields and merging their literatures to help each learn from the other.
This document provides advice for software engineering recruiting and interviews. It discusses getting relevant experiences like projects, research, and teaching to attract recruiters. It recommends applying to 3-5 jobs daily and following up consistently. For interviews, it emphasizes practicing LeetCode daily, mastering fundamentals, and communicating thought processes clearly. The key takeaways are to gain experience systematically, apply widely and persistently, and prepare through consistent coding practice.
1) The document discusses several recommendation problems at Stitch Fix including match score, fix generation, style prediction, inventory health, and search. It outlines concerns with each problem including different loss functions, organizational barriers, and lack of joint training or validation.
2) It describes mistakes made such as type 1 errors from peeking, the "balkanization" of teams working independently, and humans being left out of the model evaluation process. Weak composition of models without joint training was also a challenge.
3) The document advocates for practices like global holdouts, published validation, random re-testing, and strengthening weak composition between models. It suggests institutionalizing internal task leaderboards and validation to improve experimental rigor.
This curriculum vitae summarizes Maxim Sviridenko's professional experience and qualifications. He currently works as a Principal Research Scientist at Yahoo! Labs, and has previously held professor and research positions at various universities and IBM. His areas of expertise include algorithms, optimization, and machine learning. He has published numerous papers in journals and conferences, supervised several students and postdocs, and received multiple awards and grants for his research work.
The document discusses machine learning considerations at Meetup. It describes how Meetup uses machine learning to improve personalization and insights through recommendations and predictions. It also discusses how Meetup's data, machine learning, and data science teams work together to build ML products. Some key challenges covered include selecting objective functions, making progress on cross-domain projects, prioritizing data needs, translating local model impacts to global effects, and determining model ownership and governance.
The document discusses the six main steps for building machine learning models: 1) data access and collection, 2) data preparation and exploration, 3) model build and train, 4) model evaluation, 5) model deployment, and 6) model monitoring. It describes each step in detail, including exploring and cleaning the data, choosing a model type, training the model, evaluating model performance on test data, deploying the trained model, and monitoring the model after deployment. The process is iterative, with steps like data preparation and model training often repeated to improve the model.
As a machine learning practitioner, you probably have met people asking the question: how can I use machine learning to solve my problem? In this talk, we'll present a few of the challenges of setting up a machine learning pipeline in the real world. We'll explain why it is fundamentally different from a typical software engineering pipeline. And we'll (try to) give a few best practices to help software engineers "think ML" and prepare their collaboration with data scientists.
Recording: https://youtu.be/TZOWthpeqUY?si=MxQfT9FhPSx7fc1X&t=481
The document summarizes key topics from a recommender systems conference, including:
1. Many major companies like Netflix, Quora, and Amazon consider recommendations to be a core part of their user experience.
2. Adaptive and interactive recommendations were discussed, including how Netflix personalizes content rows based on a user's predicted mood.
3. Text modeling algorithms like word2vec were discussed for generating recommendations from content like tweets, search queries, or product descriptions.
From DevOps to MLOps: practical steps for a smooth transitionAnne-Marie Tousch
Abstract: There has been tremendous progress in artificial intelligence recently. There's no doubt one day it will also power Datadog products and you'll have to deal with it in your pipelines. What is it going to change? In this talk, I'll explain what makes ML fundamentally different than software engineering, and present a few of the operational challenges of setting up a machine learning system in the real world. Most importantly, I’ll propose practical steps to prepare the transition, that do not require you having a machine model running yet.
This talk was given at a Ladies of Code Meetup in Paris, in May 2023.
Recording: https://www.youtube.com/watch?v=S9l8GO4wtdY
Meetup: https://www.meetup.com/fr-FR/ladies-of-code-paris/events/293711765/
This document discusses using qualitative research software like WebCT and N6 to collect and analyze online discussion data. It outlines a three stage data collection strategy including open, axial, and selective coding. Advantages of computer assisted qualitative data analysis include organization, systematic approaches, and time savings. Disadvantages include complex software, loss of context, and potential data loss. The document demonstrates exporting discussion data, open coding to develop categories and properties, transforming free nodes to a tree structure, and using text searching to support research variables in analysis.
Using Computer as a Research Assistant in Qualitative ResearchJoshuaApolonio1
This document discusses using qualitative research software to collect and analyze online discussion data. It demonstrates exporting discussion data from WebCT into N6 for coding. A three-stage data collection strategy is outlined, beginning with open coding to generate categories and properties, then axial coding to interconnect categories, and ending with selective coding to build a theoretical model. Advantages of this approach include organization of large data sets and time savings, while disadvantages include complexity of software and potential to lose sight of data contexts.
Thripp EME 2040 Slides on Microsoft Education Badges, PowerPoint Quiz, Comput...Richard Thripp
My slides as an instructor for EME 2040: Introduction to Technology for Educators at University of Central Florida, for a meeting late in the Spring 2018 semester where I discussed and demonstrated earning a micro-credential (certificate or "badge") from Microsoft Education or Google; making a quiz in PowerPoint using media and relative hyperlinks; principles of computer backup, data loss, Internet security, and digital security; and grading concerns for the course.
This document provides information on how to become a software developer. It begins with an introduction that defines a software developer and notes they design, implement, and test software. It also provides the median salary range of $84,200 and high job growth of 24.6% expected through 2020. The document then outlines the roles and responsibilities of a software developer, and the requirements which do not necessarily include a computer science degree. It provides 10 steps to become a software developer including choosing a pet project, learning from books and online courses, implementing the pet project, asking questions online, and contributing to open source projects. It concludes with a list of free resources for learning C#.
The document describes RAPIDS, a rapid authoring platform for instructional design of scenarios. It aims to allow instructors to easily and quickly create scenarios for learners to understand and apply knowledge management techniques. The platform includes a scenarios database, assessments, and e-learning tools. It was tested in a pilot where participants used the scenario building tool to create and reconstruct knowledge management scenarios. Feedback indicated the tool was easy to use but could be improved by adding features like layer controls and motion. The goal is to help instructors create more engaging learning content without extensive time and effort.
Learning Management Systems for Nonprofits – Net2van July 9 2019NetSquared Vancouver
A Learning Management System (LMS) might be just the tool your organization needs to deliver your education or program content—or maybe not. Investing in an LMS is a big decision that requires ongoing resources to successfully implement and maintain. There are some key questions to ask before acquiring an LMS and best practices to follow when moving ahead with purchasing, implementing and administering an LMS.
Get the inside scoop from non-profit colleagues and non-profit learning consultants who will share their tips for success, pitfalls to avoid, and demo how they’re using this online learning technology.
What you’ll get from attending:
• Clarity on what an LMS is (and isn’t) and what it can (and can’t) do
• An eLearning terminology cheat sheet (re: the basic nuts and bolts of online learning delivery)
• An overview of some of the most common LMS options for small to medium non-profits
• Top LMS mistakes to avoid and tips for success
• LMS use case stories from a panel of 3 small to medium non-profits (why they invested in an LMS, and how they’re using it).
• Steps your organization can take right now (or later) to determine if an LMS is right for you.
ABOUT LEAH CHANG
@mmeleahchang
linkedin.com/in/mmeleahchang/
leahchang.ca
Leah has 16+ years of combined experience in education working with non-profits, private companies and public authorities to develop online and classroom learning. She specializes in Learning Management System matching, configuration and workflow, systems learning and instructional design. She loves learning about new systems and tools that might shape the future of work or help non-profits streamline their operations. She currently serves on the board of directors of the Arts Council of New Westminster, and worked with Pain BC Society as a communications and education consultant and Education Lead.
This document provides an overview of supervised learning concepts including:
- The steps in formulating a supervised learning problem including collecting labeled data, choosing a model and evaluation metric, and an optimization method.
- The dangers of overfitting when measuring performance on training data and the solution of splitting data into training and testing sets.
- An overview of Python libraries and frameworks commonly used for data science and machine learning tasks like the Scikit-learn, NumPy, Pandas, and TensorFlow libraries.
I was invited to present a master class on elearning implmentation at the 2005 eLNet Conference. I covered Westpac\'s launch of their eAcademy system and the lessons learnt.
Data Science. Business Analytics is the statistical study of business data to gain insights. Data science is the study of data using statistics, algorithms and technology. Uses mostly structured data. Uses both structured and unstructured data.
Two simples and quick ways to Save and Share your company Knowledge Jeff ANGAMA
Presentation at Singapore SharePoint Community Meeting - December 2015 - https://www.meetup.com/mssgug/events/226965173/
Employee turnover is inevitable.
Companies needs innovation.
Knowledge Management is an asset for the Entreprise Brain, the information accessible by every employee.
People shall share their knowledge to other colleagues through solutions that can research information, such as SharePoint or Office 365.
If you already have SharePoint, following this to implement a KM Solution in less than 1hour using SharePoint
Open learner models (OLMs) and learning analytics dashboards: A systematic re...Daniel Davis
This document summarizes a systematic review comparing Open Learner Models (OLMs) and learning analytics dashboards. The review found that OLMs and learning analytics dashboards both aim to provide learners with interfaces to monitor their progress, support planning and self-reflection. However, OLMs were developed in parallel with educational platforms, while learning analytics dashboards developed independently. The review identified trends in OLM research over time, central themes like self-regulated learning and reflection, and compared data modeling and evaluations between OLMs and dashboards. It recommends unifying terminology between the fields and merging their literatures to help each learn from the other.
This document provides advice for software engineering recruiting and interviews. It discusses getting relevant experiences like projects, research, and teaching to attract recruiters. It recommends applying to 3-5 jobs daily and following up consistently. For interviews, it emphasizes practicing LeetCode daily, mastering fundamentals, and communicating thought processes clearly. The key takeaways are to gain experience systematically, apply widely and persistently, and prepare through consistent coding practice.
1) The document discusses several recommendation problems at Stitch Fix including match score, fix generation, style prediction, inventory health, and search. It outlines concerns with each problem including different loss functions, organizational barriers, and lack of joint training or validation.
2) It describes mistakes made such as type 1 errors from peeking, the "balkanization" of teams working independently, and humans being left out of the model evaluation process. Weak composition of models without joint training was also a challenge.
3) The document advocates for practices like global holdouts, published validation, random re-testing, and strengthening weak composition between models. It suggests institutionalizing internal task leaderboards and validation to improve experimental rigor.
This curriculum vitae summarizes Maxim Sviridenko's professional experience and qualifications. He currently works as a Principal Research Scientist at Yahoo! Labs, and has previously held professor and research positions at various universities and IBM. His areas of expertise include algorithms, optimization, and machine learning. He has published numerous papers in journals and conferences, supervised several students and postdocs, and received multiple awards and grants for his research work.
The document discusses machine learning considerations at Meetup. It describes how Meetup uses machine learning to improve personalization and insights through recommendations and predictions. It also discusses how Meetup's data, machine learning, and data science teams work together to build ML products. Some key challenges covered include selecting objective functions, making progress on cross-domain projects, prioritizing data needs, translating local model impacts to global effects, and determining model ownership and governance.
The document discusses the six main steps for building machine learning models: 1) data access and collection, 2) data preparation and exploration, 3) model build and train, 4) model evaluation, 5) model deployment, and 6) model monitoring. It describes each step in detail, including exploring and cleaning the data, choosing a model type, training the model, evaluating model performance on test data, deploying the trained model, and monitoring the model after deployment. The process is iterative, with steps like data preparation and model training often repeated to improve the model.
As a machine learning practitioner, you probably have met people asking the question: how can I use machine learning to solve my problem? In this talk, we'll present a few of the challenges of setting up a machine learning pipeline in the real world. We'll explain why it is fundamentally different from a typical software engineering pipeline. And we'll (try to) give a few best practices to help software engineers "think ML" and prepare their collaboration with data scientists.
Recording: https://youtu.be/TZOWthpeqUY?si=MxQfT9FhPSx7fc1X&t=481
The document summarizes key topics from a recommender systems conference, including:
1. Many major companies like Netflix, Quora, and Amazon consider recommendations to be a core part of their user experience.
2. Adaptive and interactive recommendations were discussed, including how Netflix personalizes content rows based on a user's predicted mood.
3. Text modeling algorithms like word2vec were discussed for generating recommendations from content like tweets, search queries, or product descriptions.
From DevOps to MLOps: practical steps for a smooth transitionAnne-Marie Tousch
Abstract: There has been tremendous progress in artificial intelligence recently. There's no doubt one day it will also power Datadog products and you'll have to deal with it in your pipelines. What is it going to change? In this talk, I'll explain what makes ML fundamentally different than software engineering, and present a few of the operational challenges of setting up a machine learning system in the real world. Most importantly, I’ll propose practical steps to prepare the transition, that do not require you having a machine model running yet.
This talk was given at a Ladies of Code Meetup in Paris, in May 2023.
Recording: https://www.youtube.com/watch?v=S9l8GO4wtdY
Meetup: https://www.meetup.com/fr-FR/ladies-of-code-paris/events/293711765/
This document discusses using qualitative research software like WebCT and N6 to collect and analyze online discussion data. It outlines a three stage data collection strategy including open, axial, and selective coding. Advantages of computer assisted qualitative data analysis include organization, systematic approaches, and time savings. Disadvantages include complex software, loss of context, and potential data loss. The document demonstrates exporting discussion data, open coding to develop categories and properties, transforming free nodes to a tree structure, and using text searching to support research variables in analysis.
Using Computer as a Research Assistant in Qualitative ResearchJoshuaApolonio1
This document discusses using qualitative research software to collect and analyze online discussion data. It demonstrates exporting discussion data from WebCT into N6 for coding. A three-stage data collection strategy is outlined, beginning with open coding to generate categories and properties, then axial coding to interconnect categories, and ending with selective coding to build a theoretical model. Advantages of this approach include organization of large data sets and time savings, while disadvantages include complexity of software and potential to lose sight of data contexts.
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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!
4th Modern Marketing Reckoner by MMA Global India & Group M: 60+ experts on W...Social Samosa
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Global Situational Awareness of A.I. and where its headedvikram sood
You can see the future first in San Francisco.
Over the past year, the talk of the town has shifted from $10 billion compute clusters to $100 billion clusters to trillion-dollar clusters. Every six months another zero is added to the boardroom plans. Behind the scenes, there’s a fierce scramble to secure every power contract still available for the rest of the decade, every voltage transformer that can possibly be procured. American big business is gearing up to pour trillions of dollars into a long-unseen mobilization of American industrial might. By the end of the decade, American electricity production will have grown tens of percent; from the shale fields of Pennsylvania to the solar farms of Nevada, hundreds of millions of GPUs will hum.
The AGI race has begun. We are building machines that can think and reason. By 2025/26, these machines will outpace college graduates. By the end of the decade, they will be smarter than you or I; we will have superintelligence, in the true sense of the word. Along the way, national security forces not seen in half a century will be un-leashed, and before long, The Project will be on. If we’re lucky, we’ll be in an all-out race with the CCP; if we’re unlucky, an all-out war.
Everyone is now talking about AI, but few have the faintest glimmer of what is about to hit them. Nvidia analysts still think 2024 might be close to the peak. Mainstream pundits are stuck on the wilful blindness of “it’s just predicting the next word”. They see only hype and business-as-usual; at most they entertain another internet-scale technological change.
Before long, the world will wake up. But right now, there are perhaps a few hundred people, most of them in San Francisco and the AI labs, that have situational awareness. Through whatever peculiar forces of fate, I have found myself amongst them. A few years ago, these people were derided as crazy—but they trusted the trendlines, which allowed them to correctly predict the AI advances of the past few years. Whether these people are also right about the next few years remains to be seen. But these are very smart people—the smartest people I have ever met—and they are the ones building this technology. Perhaps they will be an odd footnote in history, or perhaps they will go down in history like Szilard and Oppenheimer and Teller. If they are seeing the future even close to correctly, we are in for a wild ride.
Let me tell you what we see.
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Data and AI
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#DataAnalysis #SQL #LearningSQL #DataInsights #DataScience #Analytics
2019-04-11 Databricks Unified Analytics Workshop - Outreach Case Study
1. Databricks Workshop - April 11th 2019
Case Study: Outreach
Andrew Brooks, Li Dong, Jiwei Cao
2. Content Overview
1. Introduction to Outreach
a. #1 Sales Engagement Platform
b. Data Science + Sales Engagement
2. Outreach with Databricks
a.Case Study - Production: Out-of-office Data Extraction
b.Case Study - Research: Intent Classification
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6. It’s also a new category of software.
Add content
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Source: https://blog.topohq.com/sales-engagement-the-definitive-guide/
7. How about an Example?
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Automates execution of some sales tasks:
Emails, Linkedin Messages etc.
Schedules and reminds the rep when it is the
right time to do the manual tasks (e.g. phone
call, custom manual email)
SEP Encodes and Automates Sales
Activities into Workflows/Pipelines
8. Data Science + Sales Engagement
Outreach ML Features:
- Automation - Information Extraction
- A/B testing
- Advanced Analytics (dashboard & reporting)
Optimization:
- Intent & Topic Detection
- Content & Action Recommendation
- Prioritization & Forecasting
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Highlighted use case
Phone
Email
LinkedIn
Meetings
Data Sources:
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Something to improve
- Separate stacks to develop ML model and deploy model
- jupyter notebooks
- databricks notebooks
- Docker, K8s(production)
- Lack of ML model life-cycle management
- model training
- experiment(alpha, beta)
- production to GA
- model iterations / releases
Production Architecture
Highlighted use case: OOO Information Extraction
16. Intent Classification: Problem Solving
Steps to solve it with NLP/Machine Learning:
1. Annotate some emails
2. Setup the Experiment Environment(Spark, NLP/ML-Packages)
3. Write Code and Running Experiments
4. Analyze Experiment Results
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Goal: Classify email replies into 3 categories: positive, objection and unsubscription.
17. Doesn’t Look Complicated, But Painful
Pain Points:
● Difficult to setup and maintain a proper environment.
● Can’t run multiple Experiments at the same time
● Experiment Results are scattered in multiple files. Hard to navigate and analysis.
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18. Intent Classification: Problem Solving
Steps to explore ideas with Databricks:
1. Annotate some emails
2. Setup the Experiment Environment(Spark, NLP/ML-Packages)
3. Write Code and Running Experiments
4. Analyze Experiment Results
5. Visualization the model prediction
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Goal: Classify email replies into positive, objection and unsubscription.
20. How does Databricks help us
• Setup a Dedicated Environment with Less Effort
• Running Experiments at Scale
• Analyze Experiment Results at One Place
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