Extracting and analyzing discussion data with google sheets and google analyticsMartin Hawksey
Online discussions can be a rich source of data for researchers in the humanities and social sciences. In this workshop, participants will learn how to use Google Sheets to push online discussion board data into Google Analytics, where it can be analysed. The session will also demonstrate how to use TAGS, the widely-used script for archiving Twitter data. Participants can bring their own laptops if they wish; there will also be desktop PCs for use.
Please note: if you’re not staff or student at the University of Edinburgh, you will need to obtain a temporary login from the registration desk in advance.
Overview of Data Science at Shopify and a simple user segmentation example you can use in your organisation right now! Françoise Provencher presented this talk at a sponsor workshop at PyCon US 2018 and at MTL Python. Watch it on YouTube: https://www.youtube.com/watch?v=I_ZWWkxIRy8
The beginning of this talk is applicable to a wide audience. Only the last step is JavaScript specific.
Presentation given to UtahJS Meetup (https://utahjs.com/) on July 7, 2016. Introduction of LEAP framework and how to use AWS to create a predictive model and consume prediction with the AWS JavaScript SDK.
The beginning of this talk is applicable to a wide audience. Only the last step is JavaScript specific.
Presentation given to UtahJS Meetup (https://utahjs.com/) on July 7, 2016. Introduction of LEAP framework and how to use AWS to create a predictive model and consume prediction with the AWS JavaScript SDK.
Building a Testing Playbook by Andrew RichardsonDelphic Digital
A testing playbook combines the best practices of testing and optimization, along with communication strategies, education, and gaining buy-in from your client. Andrew Richardson, Senior Director of Analytics at Delphic Digital, provides a peek behind the curtain to reveal how Delphic prioritizes tests, recruits/trains/staffs-up for a testing practice, and moved from A/B to multivariate testing. Come with and open mind, walk away with a Testing Playbook Template you can put to use at once.
This was the presentation for the Microsoft Community Technology Update of 2016. The idea was to introduce to people the concept of Machine Learning and its easy to get started if you are keen. My objective was also to communicate how some of the algorithms work and they require no more than basic understanding of Math to get going, sometimes not even that.
The algorithms we covered were, Support Vector Machines (SVM), Decision Tree using R2D3 and Neural Networks for classification. We used the Tensorflow Playground to help understand the Neural Network and Deep Learning concepts.
I gave an analogy of how Machine Learning process is like making a smoothie where your algorithm is a recipe, your data are your ingredients, your computer is your blender and your smoothie is the model that you developed. I used the same example to convey the concept of Training Validation and Testing. Coverage of Type 1 and Type 2 errors together with the metrics of Recall and Precision was covered as well. Finally I closed the session with what are some good resources to get started with Machine Learning for all skill levels. There are references to websites, courses, kaggle competition, podcasts, cheat sheets and books.
Leveraging Social Media with Computer VisionTJ Torres
This talk discusses some of my current and past work at Stitch Fix on using Deep Learning and Computer Vision to better inform recommendations in fashion and retail.
Learn how to transform from a mild-mannered online organizer into a true data-driven mastermind! What to track, how to test, and methods for creating a data-driven culture at your nonprofit.
This was the first introductory course on getting started with Azure Machine Learning that was held by Microsoft User Group on 13th September 2017. These were the slides that were presented in the session. The excel file is available separately and can be downloaded from here (https://1drv.ms/x/s!AmjwdE_MMESksHOrlLXP400eHb3p). We covered on how decision trees are created and extended the concept to building Random Forest algorithm.
This was the first introductory course on getting started with Azure Machine Learning that was held by Microsoft User Group on 13th September 2017 and 25th October 2017. These were the slides that were presented in the session. We covered the Decision Tree and Support Vector Machine (SVM) based algorithms:
1. Two-Class /Multiclasss Decision Forest
2. Two-Class /Multiclasss Decision Jungle
3. Two-Class Boosted Decision Tree
4. Two-Class Support Vector Machine (SVM)
5. Two-Class Locally Deep Support Vector Machine (LDSVM)
We also looked at how to look at Feature Importance using "Permutation Feature Importance" module
We also looked at "Partition and Sample" module and how to use it together with "Cross Validate Model" module.
Extracting and analyzing discussion data with google sheets and google analyticsMartin Hawksey
Online discussions can be a rich source of data for researchers in the humanities and social sciences. In this workshop, participants will learn how to use Google Sheets to push online discussion board data into Google Analytics, where it can be analysed. The session will also demonstrate how to use TAGS, the widely-used script for archiving Twitter data. Participants can bring their own laptops if they wish; there will also be desktop PCs for use.
Please note: if you’re not staff or student at the University of Edinburgh, you will need to obtain a temporary login from the registration desk in advance.
Overview of Data Science at Shopify and a simple user segmentation example you can use in your organisation right now! Françoise Provencher presented this talk at a sponsor workshop at PyCon US 2018 and at MTL Python. Watch it on YouTube: https://www.youtube.com/watch?v=I_ZWWkxIRy8
The beginning of this talk is applicable to a wide audience. Only the last step is JavaScript specific.
Presentation given to UtahJS Meetup (https://utahjs.com/) on July 7, 2016. Introduction of LEAP framework and how to use AWS to create a predictive model and consume prediction with the AWS JavaScript SDK.
The beginning of this talk is applicable to a wide audience. Only the last step is JavaScript specific.
Presentation given to UtahJS Meetup (https://utahjs.com/) on July 7, 2016. Introduction of LEAP framework and how to use AWS to create a predictive model and consume prediction with the AWS JavaScript SDK.
Building a Testing Playbook by Andrew RichardsonDelphic Digital
A testing playbook combines the best practices of testing and optimization, along with communication strategies, education, and gaining buy-in from your client. Andrew Richardson, Senior Director of Analytics at Delphic Digital, provides a peek behind the curtain to reveal how Delphic prioritizes tests, recruits/trains/staffs-up for a testing practice, and moved from A/B to multivariate testing. Come with and open mind, walk away with a Testing Playbook Template you can put to use at once.
This was the presentation for the Microsoft Community Technology Update of 2016. The idea was to introduce to people the concept of Machine Learning and its easy to get started if you are keen. My objective was also to communicate how some of the algorithms work and they require no more than basic understanding of Math to get going, sometimes not even that.
The algorithms we covered were, Support Vector Machines (SVM), Decision Tree using R2D3 and Neural Networks for classification. We used the Tensorflow Playground to help understand the Neural Network and Deep Learning concepts.
I gave an analogy of how Machine Learning process is like making a smoothie where your algorithm is a recipe, your data are your ingredients, your computer is your blender and your smoothie is the model that you developed. I used the same example to convey the concept of Training Validation and Testing. Coverage of Type 1 and Type 2 errors together with the metrics of Recall and Precision was covered as well. Finally I closed the session with what are some good resources to get started with Machine Learning for all skill levels. There are references to websites, courses, kaggle competition, podcasts, cheat sheets and books.
Leveraging Social Media with Computer VisionTJ Torres
This talk discusses some of my current and past work at Stitch Fix on using Deep Learning and Computer Vision to better inform recommendations in fashion and retail.
Learn how to transform from a mild-mannered online organizer into a true data-driven mastermind! What to track, how to test, and methods for creating a data-driven culture at your nonprofit.
This was the first introductory course on getting started with Azure Machine Learning that was held by Microsoft User Group on 13th September 2017. These were the slides that were presented in the session. The excel file is available separately and can be downloaded from here (https://1drv.ms/x/s!AmjwdE_MMESksHOrlLXP400eHb3p). We covered on how decision trees are created and extended the concept to building Random Forest algorithm.
This was the first introductory course on getting started with Azure Machine Learning that was held by Microsoft User Group on 13th September 2017 and 25th October 2017. These were the slides that were presented in the session. We covered the Decision Tree and Support Vector Machine (SVM) based algorithms:
1. Two-Class /Multiclasss Decision Forest
2. Two-Class /Multiclasss Decision Jungle
3. Two-Class Boosted Decision Tree
4. Two-Class Support Vector Machine (SVM)
5. Two-Class Locally Deep Support Vector Machine (LDSVM)
We also looked at how to look at Feature Importance using "Permutation Feature Importance" module
We also looked at "Partition and Sample" module and how to use it together with "Cross Validate Model" module.
Clare Corthell: Learning Data Science Onlinesfdatascience
Clare Corthell, Data Scientist and Designer at Mattermark, and author of the Open Source Data Science Masters, shares her experience teaching herself data science with online resources. http://datasciencemasters.org/
Data science is not Software Development and how Experiment Management can ma...Jakub Czakon
Working on data science projects that are run as if they were software development can sometimes feel like trying to fit a square peg in a round hole. In this talk, I will explain why that happens and what people do to try and fix it. Lately, in the context of machine learning, the concept of experiment management, which treats ml experiments as first-class citizens, has been gaining a lot of traction. I will discuss what it is, what are the benefits of using it, and how you can apply it in your work to make run your projects more efficiently.
HPX44- The True Power of User ResearchStella Hsiao
In this slide, you'll learn how to use qualitative and quantitative UX research data to boost team work and help generating idea that can growth your target index!
Similar to 10 things statistics taught us about big data (20)
Explore our comprehensive data analysis project presentation on predicting product ad campaign performance. Learn how data-driven insights can optimize your marketing strategies and enhance campaign effectiveness. Perfect for professionals and students looking to understand the power of data analysis in advertising. for more details visit: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...John Andrews
SlideShare Description for "Chatty Kathy - UNC Bootcamp Final Project Presentation"
Title: Chatty Kathy: Enhancing Physical Activity Among Older Adults
Description:
Discover how Chatty Kathy, an innovative project developed at the UNC Bootcamp, aims to tackle the challenge of low physical activity among older adults. Our AI-driven solution uses peer interaction to boost and sustain exercise levels, significantly improving health outcomes. This presentation covers our problem statement, the rationale behind Chatty Kathy, synthetic data and persona creation, model performance metrics, a visual demonstration of the project, and potential future developments. Join us for an insightful Q&A session to explore the potential of this groundbreaking project.
Project Team: Jay Requarth, Jana Avery, John Andrews, Dr. Dick Davis II, Nee Buntoum, Nam Yeongjin & Mat Nicholas
Data Centers - Striving Within A Narrow Range - Research Report - MCG - May 2...pchutichetpong
M Capital Group (“MCG”) expects to see demand and the changing evolution of supply, facilitated through institutional investment rotation out of offices and into work from home (“WFH”), while the ever-expanding need for data storage as global internet usage expands, with experts predicting 5.3 billion users by 2023. These market factors will be underpinned by technological changes, such as progressing cloud services and edge sites, allowing the industry to see strong expected annual growth of 13% over the next 4 years.
Whilst competitive headwinds remain, represented through the recent second bankruptcy filing of Sungard, which blames “COVID-19 and other macroeconomic trends including delayed customer spending decisions, insourcing and reductions in IT spending, energy inflation and reduction in demand for certain services”, the industry has seen key adjustments, where MCG believes that engineering cost management and technological innovation will be paramount to success.
MCG reports that the more favorable market conditions expected over the next few years, helped by the winding down of pandemic restrictions and a hybrid working environment will be driving market momentum forward. The continuous injection of capital by alternative investment firms, as well as the growing infrastructural investment from cloud service providers and social media companies, whose revenues are expected to grow over 3.6x larger by value in 2026, will likely help propel center provision and innovation. These factors paint a promising picture for the industry players that offset rising input costs and adapt to new technologies.
According to M Capital Group: “Specifically, the long-term cost-saving opportunities available from the rise of remote managing will likely aid value growth for the industry. Through margin optimization and further availability of capital for reinvestment, strong players will maintain their competitive foothold, while weaker players exit the market to balance supply and demand.”
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...Subhajit Sahu
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
7. from: jtleek@gmail.com
Roger let me know you gave him a
ballpark figure for the number of
students registered for his course
"Computing for Data Analysis”. Could
you give me an idea of how many have
registered for my course "Data Analysis?”
8. from: pangwei@coursera.org
Hi Jeff,
7,000 students! It's pretty awesome.
(You'll be able to check this out yourself
next week, once the class sites are up.)
18. 10 statistics things
1. Problem first, not solution backward
2. Define a metric for success first
3. Analyze interactively
4. Plot your data first and always
5. Know your real sample size
6. Watch out for confounders
7. Correct for multiple testing
8. Average many predictors
9. Smooth over time and space
10. Have others check your work
http://goo.gl/wTAuvR
63. 10 statistics things
1. Problem first, not solution backward
2. Define a metric for success first
3. Analyze interactively
4. Plot your data first and always
5. Know your real sample size
6. Watch out for confounders
7. Correct for multiple testing
8. Average many predictors
9. Smooth over time and space
10. Have others check your work
http://goo.gl/wTAuvR
