This document outlines a project to use data analytics techniques to predict the winner of the 2016 Stanley Cup. It introduces Dagny and Cayla Evans, describes collecting and analyzing hockey stats from various sources in R, discusses the complexity of hockey stats, shows the results of analyzing 2014-2015 team performance data, and shares lessons learned from the project.
Hard-working Lipscomb University computer science student with experience as an intern at Smartvue Corporation developing backend C++ and frontend NodeJS code. Skilled in programming languages including Java, C++, C#, Python, and HTML and has worked with app development kits such as Corona SDK, Unity, and Android SDK.
Calltutors.com provides urgent assignment help and 24/7 client support for all subjects. They offer on-time delivery, plagiarism-free work from PhD-qualified writers, and secure payment options. Their services include urgent assignment help, customized solutions, and support through online chat, phone, email, WhatsApp and Facebook. They assist with assignments in various subjects such as management, business, English, psychology, nursing, computer science, statistics, and many others. To avail their services, students can visit their website Calltutors.com to make a payment and receive completed work before deadlines.
Emily Castro has over 3 years of experience in data analysis using programs like R Studio and Excel. She currently works as a Data Analyst at Karl Heiner Statistical Consulting, where she analyzes marketing data, assists with client reports, and creates statistical models. Previously, she was a Graduate Assistant at SUNY New Paltz where she analyzed data, developed class materials, and tutored students in statistics. She also interned at A.JAFFE, implementing social media strategies and increasing their online presence.
How to Use Data Analytics in Gaming by Telligent Data Co-FounderProduct School
In this talk, Lewis discussed the growing field of data analytics, and its growing importance for Product Managers. He also walked through the key concepts of analytics metrics and how they apply to product performance and roadmaps, as well as the key data tools used by Product Managers day-to-day.
Data is growing exponentially. What should business managers do to make better business decisions? I explain three key things step by step. Just start today!
These slides show, why excel is an important thing to learn in the computer world. Total of 5 slides, with easy to understand wording and pictures showing the importance of excel training.
This document provides information for HIV-positive Latinos on health, treatment, and living with HIV. It discusses how an HIV diagnosis can affect people emotionally and the importance of seeking support from community organizations. It also emphasizes the importance of self-care, continuing life goals and education, and accessing appropriate medical care and treatment options. The overall message is that while having HIV brings challenges, individuals are not alone and can take steps to maintain their health.
Hard-working Lipscomb University computer science student with experience as an intern at Smartvue Corporation developing backend C++ and frontend NodeJS code. Skilled in programming languages including Java, C++, C#, Python, and HTML and has worked with app development kits such as Corona SDK, Unity, and Android SDK.
Calltutors.com provides urgent assignment help and 24/7 client support for all subjects. They offer on-time delivery, plagiarism-free work from PhD-qualified writers, and secure payment options. Their services include urgent assignment help, customized solutions, and support through online chat, phone, email, WhatsApp and Facebook. They assist with assignments in various subjects such as management, business, English, psychology, nursing, computer science, statistics, and many others. To avail their services, students can visit their website Calltutors.com to make a payment and receive completed work before deadlines.
Emily Castro has over 3 years of experience in data analysis using programs like R Studio and Excel. She currently works as a Data Analyst at Karl Heiner Statistical Consulting, where she analyzes marketing data, assists with client reports, and creates statistical models. Previously, she was a Graduate Assistant at SUNY New Paltz where she analyzed data, developed class materials, and tutored students in statistics. She also interned at A.JAFFE, implementing social media strategies and increasing their online presence.
How to Use Data Analytics in Gaming by Telligent Data Co-FounderProduct School
In this talk, Lewis discussed the growing field of data analytics, and its growing importance for Product Managers. He also walked through the key concepts of analytics metrics and how they apply to product performance and roadmaps, as well as the key data tools used by Product Managers day-to-day.
Data is growing exponentially. What should business managers do to make better business decisions? I explain three key things step by step. Just start today!
These slides show, why excel is an important thing to learn in the computer world. Total of 5 slides, with easy to understand wording and pictures showing the importance of excel training.
This document provides information for HIV-positive Latinos on health, treatment, and living with HIV. It discusses how an HIV diagnosis can affect people emotionally and the importance of seeking support from community organizations. It also emphasizes the importance of self-care, continuing life goals and education, and accessing appropriate medical care and treatment options. The overall message is that while having HIV brings challenges, individuals are not alone and can take steps to maintain their health.
The document discusses energy conservation in India. It notes that while India has 5% of the world's population, it accounts for 26% of global energy use. Improving energy efficiency through technologies like compact fluorescent light bulbs, solar water heating, better insulation, and higher efficiency appliances can significantly reduce energy costs for Indian households while cutting carbon emissions. Energy conservation has already saved India an estimated $12 billion per year in avoided electricity costs compared to continuing higher usage trends, but further opportunities remain in buildings, transportation, and industry.
This document discusses trends in web and social media in 2016. It emphasizes that websites and social media must be intuitive for consumers and should not require visitors to think too much. It also stresses the importance of speed and getting to the point quickly given people's shortening attention spans. Key elements for websites are mentioned like blogs, analytics, social media integration, calls to action, and easy navigation within 3 clicks. The document also highlights the rise of mobile and video on social media platforms and provides tips on landing pages, calls to action, and engaging with webmasters.
Este documento describe cómo acceder y usar Google Drive. Explica que Google Drive permite almacenar y acceder a archivos desde cualquier lugar a través de la web, el disco duro o dispositivos móviles. Detalla los pasos para acceder a Google Drive y cómo los archivos se actualizan automáticamente entre dispositivos. También resume las principales características de Google Drive como el almacenamiento gratuito de 5GB, la integración con otros productos de Google, su disponibilidad en múltiples plataformas y su seguridad y capacidad de b
This document provides information on starting your own "bug squad" to help address open issues for Drupal core and contributed modules. It explains that a bug squad is an organized group of volunteers that tackle issues for an individual module. The document outlines the different issue statuses and templates for writing clear issue summaries. It also provides tips for testing patches, reviewing patches using Dreditor, working with module maintainers, and communicating with users reporting issues.
The document advertises Feldenkrais Method classes, private sessions, and retreats offered by Okanagan Feldenkrais in Vernon, BC. It promotes the Feldenkrais Method created by Dr. Moshe Feldenkrais as a way to improve movement and quality of life. Okanagan Feldenkrais offers various classes and sessions for athletes, seniors, groups, and individuals to experience greater freedom, grace, and ease through movement.
Este documento resume varias iniciativas y actividades recientes de La Fondita de Jesús y sus socios para ayudar a las personas sin hogar. Supermercados locales se han unido a La Fondita para vender bolsas reusables y recaudar fondos. Estudiantes también están ayudando a promover el uso de bolsas reusables. Adicionalmente, La Fondita ha establecido un vivero hidropónico para cultivar vegetales y generar ingresos. Varios grupos como Walmart, estudiantes universitarios y voluntarios individuales contin
Become a Better Data Analyst with Tableau - DenmarkTUGSarah Bartlett
This document provides tips for becoming a better analyst using Tableau. It recommends mastering the basics through free training resources. Regular practice with downloadable Tableau Public is important, using projects of varying sizes. Asking the right questions of data ensures understanding its limitations. Study design fundamentals from books on visualization best practices. Publish work on Tableau Public and social media to get feedback. Engage with the Tableau user community through events and online. Get certified to prove skills and boost careers. Leverage additional community resources like blogs and videos. Teaching others is another way to share knowledge and give back to the community.
The document outlines a presentation about data analytics and business intelligence given by Chris Ortega. The presentation covers:
1. Definitions of data analytics and business intelligence.
2. Why data analytics and business intelligence are important for faster decision making, establishing a learning culture, and exploring opportunities.
3. The decision cycle and how business intelligence tools can automate parts of extracting data, analyzing it, and making decisions.
4. Limitations of current data analytics approaches like manual spreadsheet updates and the difficulty combining multiple data sources.
Data-Ed Webinar: Data Modeling FundamentalsDATAVERSITY
Every organization produces and consumes data. Because data is so important to day to day operations, data trends are hitting the mainstream and businesses are adopting buzzwords such as Big Data, NoSQL, data scientist, etc., to seek solutions for their fundamental issues. Few realize that the importance of any solution, regardless of platform or technology, relies on the data model supporting it. Data modeling is not an optional task for an organization’s data effort. It is a vital activity that supports the solutions driving your business.
This webinar will address fundamental data modeling methodologies, as well as trends around the practice of data modeling itself. We will discuss abstract models and entity frameworks, as well as the general shift from data modeling being segmented to becoming more integrated with business practices.
Learning Objectives:
How are anchor modeling, data vault, etc. different and when should I apply them?
Integrating data models to business models and the value this creates
Application development (Data first, code first, object first)
Data-Ed Slides: Data Modeling Strategies - Getting Your Data Ready for the Ca...DATAVERSITY
Because every organization produces and propagates data as part of their day-to-day operations, data trends are becoming more and more important in the mainstream business world’s consciousness. For many organizations in various industries, though, comprehension of this development begins and ends with buzzwords: “Big Data”, “NoSQL”, “data scientist”, and so on. Few realize that any and all solutions to their business problems, regardless of platform or relevant technology, rely to a critical extent on the data model supporting them. As such, data modeling is not an optional task for an organization’s data effort, but rather a vital activity that facilitates the solutions driving your business.
Instead of the technical minutiae of data modeling, this webinar will focus on its value and practicality for your organization. In doing so, we will:
- Address fundamental data modeling methodologies, their differences and various practical applications, and trends around the practice of data modeling itself
- Discuss abstract models and entity frameworks, as well as some basic tenets for application development
- Examine the general shift from segmented data modeling to more business-integrated practices
Become a Better Data Analyst with Tableau - Charlotte TUGSarah Bartlett
This document provides tips for becoming a better analyst using Tableau. It outlines 9 key skills: 1) Master the basics by utilizing free training resources. 2) Practice regularly by choosing learning projects. 3) Ask the right questions of data to understand its limits. 4) Study design fundamentals. 5) Publish work to gain feedback. 6) Engage with the Tableau community. 7) Get certified to prove skills. 8) Leverage community resources like blogs. 9) Teach others to share knowledge. Regular practice, publishing work, and engaging with the community are emphasized as important ways to refine skills.
This document provides an introduction to data and analytics for startups. It discusses predictive modeling, data visualization, cohort analysis using tools like Kissmetrics and Mixpanel. It also covers customer value, tips for data including focusing on important outcomes and not torturing the data. Recommendations are given for creating a data driven culture through regular reporting, dashboards, and including data in decision making. Resources for further learning include books, conferences, and online courses.
The role of data managers is integral to improving results for students with disabilities. Data managers ensure timely and accurate special education data submission, provide data analysis to inform decision making, and help local districts understand and leverage their own data. Effective data governance, cross-department information sharing, and making data accessible are important responsibilities of data managers. Summarizing data and using it to tell the story of progress can influence policy and support students.
The Emerging Role of a Data Product ManagerData Con LA
Data Con LA 2020
Description
HopSkipDrive, a startup focused on youth mobility, wanted to invest Data Culture to ultimately improve the abilities of its entire staff to quickly make data driven business decisions that created positive change. The key to achieving an effective Data Culture was treating internal data like a product, hiring a Data Product Manager to lead this initiative and create targeted solutions for specific data utilization problems. This talk by HopSkipDrive's Data Product Manager, Cindy Lin, will cover the initial job description, the steps she took once onboard including the process of creating and executing the HopSkipDrive Data Culture model and roadmap, and the outcomes she has achieved so far.
*What led to hiring this role and why was Cindy hired?
*Once onboard, retaining executive buy in
*Establishing the data culture model and roadmap
*Executive of the data culture strategy
*Impact of the above
Speaker
Cindy Lin, HopSkipDrive, Data Product Manager
Lean Analytics is a set of rules to make data science more streamlined and productive. It touches on many aspects of what a data scientist should be and how a data science project should be defined to be successful. During this presentation Richard will present where data science projects go wrong, how you should think of data science projects, what constitutes success in data science and how you can measure progress. This session will be loaded with terms, stories and descriptions of project successes and failures. If you're wondering whether you're getting value out of data science, how to get more value out of it and even whether you need it then this talk is for you!
What you will take away from this session
Learn how to make your data science projects successful
Evaluate how to track progress and report on the efficacy of data science solutions
Understand the role of engineering and data scientists
Understand your options for processes and software
This document provides an overview of the data science process for predicting the NBA MVP winner for the upcoming season. It discusses framing the question, collecting relevant stats data from basketball-reference.com, cleaning and formatting the data, exploring it with Python libraries like Pandas and NumPy, building and evaluating decision tree and random forest models, and discussing ways to improve the model's performance, such as modifying feature selection.
Tips to Become a Better Data Analyst - Data+Women GermanySarah Bartlett
This document provides tips for becoming a better data analyst, including mastering the basics of data tools, practicing regularly with learning projects, asking the right questions of data, studying design fundamentals, publishing work, engaging with the community, getting certified, leveraging community resources, and teaching others. The tips are part of a presentation on data visualization skills by Sarah Bartlett, a data analytics consultant with over 12 years of experience working with data.
The document discusses energy conservation in India. It notes that while India has 5% of the world's population, it accounts for 26% of global energy use. Improving energy efficiency through technologies like compact fluorescent light bulbs, solar water heating, better insulation, and higher efficiency appliances can significantly reduce energy costs for Indian households while cutting carbon emissions. Energy conservation has already saved India an estimated $12 billion per year in avoided electricity costs compared to continuing higher usage trends, but further opportunities remain in buildings, transportation, and industry.
This document discusses trends in web and social media in 2016. It emphasizes that websites and social media must be intuitive for consumers and should not require visitors to think too much. It also stresses the importance of speed and getting to the point quickly given people's shortening attention spans. Key elements for websites are mentioned like blogs, analytics, social media integration, calls to action, and easy navigation within 3 clicks. The document also highlights the rise of mobile and video on social media platforms and provides tips on landing pages, calls to action, and engaging with webmasters.
Este documento describe cómo acceder y usar Google Drive. Explica que Google Drive permite almacenar y acceder a archivos desde cualquier lugar a través de la web, el disco duro o dispositivos móviles. Detalla los pasos para acceder a Google Drive y cómo los archivos se actualizan automáticamente entre dispositivos. También resume las principales características de Google Drive como el almacenamiento gratuito de 5GB, la integración con otros productos de Google, su disponibilidad en múltiples plataformas y su seguridad y capacidad de b
This document provides information on starting your own "bug squad" to help address open issues for Drupal core and contributed modules. It explains that a bug squad is an organized group of volunteers that tackle issues for an individual module. The document outlines the different issue statuses and templates for writing clear issue summaries. It also provides tips for testing patches, reviewing patches using Dreditor, working with module maintainers, and communicating with users reporting issues.
The document advertises Feldenkrais Method classes, private sessions, and retreats offered by Okanagan Feldenkrais in Vernon, BC. It promotes the Feldenkrais Method created by Dr. Moshe Feldenkrais as a way to improve movement and quality of life. Okanagan Feldenkrais offers various classes and sessions for athletes, seniors, groups, and individuals to experience greater freedom, grace, and ease through movement.
Este documento resume varias iniciativas y actividades recientes de La Fondita de Jesús y sus socios para ayudar a las personas sin hogar. Supermercados locales se han unido a La Fondita para vender bolsas reusables y recaudar fondos. Estudiantes también están ayudando a promover el uso de bolsas reusables. Adicionalmente, La Fondita ha establecido un vivero hidropónico para cultivar vegetales y generar ingresos. Varios grupos como Walmart, estudiantes universitarios y voluntarios individuales contin
Become a Better Data Analyst with Tableau - DenmarkTUGSarah Bartlett
This document provides tips for becoming a better analyst using Tableau. It recommends mastering the basics through free training resources. Regular practice with downloadable Tableau Public is important, using projects of varying sizes. Asking the right questions of data ensures understanding its limitations. Study design fundamentals from books on visualization best practices. Publish work on Tableau Public and social media to get feedback. Engage with the Tableau user community through events and online. Get certified to prove skills and boost careers. Leverage additional community resources like blogs and videos. Teaching others is another way to share knowledge and give back to the community.
The document outlines a presentation about data analytics and business intelligence given by Chris Ortega. The presentation covers:
1. Definitions of data analytics and business intelligence.
2. Why data analytics and business intelligence are important for faster decision making, establishing a learning culture, and exploring opportunities.
3. The decision cycle and how business intelligence tools can automate parts of extracting data, analyzing it, and making decisions.
4. Limitations of current data analytics approaches like manual spreadsheet updates and the difficulty combining multiple data sources.
Data-Ed Webinar: Data Modeling FundamentalsDATAVERSITY
Every organization produces and consumes data. Because data is so important to day to day operations, data trends are hitting the mainstream and businesses are adopting buzzwords such as Big Data, NoSQL, data scientist, etc., to seek solutions for their fundamental issues. Few realize that the importance of any solution, regardless of platform or technology, relies on the data model supporting it. Data modeling is not an optional task for an organization’s data effort. It is a vital activity that supports the solutions driving your business.
This webinar will address fundamental data modeling methodologies, as well as trends around the practice of data modeling itself. We will discuss abstract models and entity frameworks, as well as the general shift from data modeling being segmented to becoming more integrated with business practices.
Learning Objectives:
How are anchor modeling, data vault, etc. different and when should I apply them?
Integrating data models to business models and the value this creates
Application development (Data first, code first, object first)
Data-Ed Slides: Data Modeling Strategies - Getting Your Data Ready for the Ca...DATAVERSITY
Because every organization produces and propagates data as part of their day-to-day operations, data trends are becoming more and more important in the mainstream business world’s consciousness. For many organizations in various industries, though, comprehension of this development begins and ends with buzzwords: “Big Data”, “NoSQL”, “data scientist”, and so on. Few realize that any and all solutions to their business problems, regardless of platform or relevant technology, rely to a critical extent on the data model supporting them. As such, data modeling is not an optional task for an organization’s data effort, but rather a vital activity that facilitates the solutions driving your business.
Instead of the technical minutiae of data modeling, this webinar will focus on its value and practicality for your organization. In doing so, we will:
- Address fundamental data modeling methodologies, their differences and various practical applications, and trends around the practice of data modeling itself
- Discuss abstract models and entity frameworks, as well as some basic tenets for application development
- Examine the general shift from segmented data modeling to more business-integrated practices
Become a Better Data Analyst with Tableau - Charlotte TUGSarah Bartlett
This document provides tips for becoming a better analyst using Tableau. It outlines 9 key skills: 1) Master the basics by utilizing free training resources. 2) Practice regularly by choosing learning projects. 3) Ask the right questions of data to understand its limits. 4) Study design fundamentals. 5) Publish work to gain feedback. 6) Engage with the Tableau community. 7) Get certified to prove skills. 8) Leverage community resources like blogs. 9) Teach others to share knowledge. Regular practice, publishing work, and engaging with the community are emphasized as important ways to refine skills.
This document provides an introduction to data and analytics for startups. It discusses predictive modeling, data visualization, cohort analysis using tools like Kissmetrics and Mixpanel. It also covers customer value, tips for data including focusing on important outcomes and not torturing the data. Recommendations are given for creating a data driven culture through regular reporting, dashboards, and including data in decision making. Resources for further learning include books, conferences, and online courses.
The role of data managers is integral to improving results for students with disabilities. Data managers ensure timely and accurate special education data submission, provide data analysis to inform decision making, and help local districts understand and leverage their own data. Effective data governance, cross-department information sharing, and making data accessible are important responsibilities of data managers. Summarizing data and using it to tell the story of progress can influence policy and support students.
The Emerging Role of a Data Product ManagerData Con LA
Data Con LA 2020
Description
HopSkipDrive, a startup focused on youth mobility, wanted to invest Data Culture to ultimately improve the abilities of its entire staff to quickly make data driven business decisions that created positive change. The key to achieving an effective Data Culture was treating internal data like a product, hiring a Data Product Manager to lead this initiative and create targeted solutions for specific data utilization problems. This talk by HopSkipDrive's Data Product Manager, Cindy Lin, will cover the initial job description, the steps she took once onboard including the process of creating and executing the HopSkipDrive Data Culture model and roadmap, and the outcomes she has achieved so far.
*What led to hiring this role and why was Cindy hired?
*Once onboard, retaining executive buy in
*Establishing the data culture model and roadmap
*Executive of the data culture strategy
*Impact of the above
Speaker
Cindy Lin, HopSkipDrive, Data Product Manager
Lean Analytics is a set of rules to make data science more streamlined and productive. It touches on many aspects of what a data scientist should be and how a data science project should be defined to be successful. During this presentation Richard will present where data science projects go wrong, how you should think of data science projects, what constitutes success in data science and how you can measure progress. This session will be loaded with terms, stories and descriptions of project successes and failures. If you're wondering whether you're getting value out of data science, how to get more value out of it and even whether you need it then this talk is for you!
What you will take away from this session
Learn how to make your data science projects successful
Evaluate how to track progress and report on the efficacy of data science solutions
Understand the role of engineering and data scientists
Understand your options for processes and software
This document provides an overview of the data science process for predicting the NBA MVP winner for the upcoming season. It discusses framing the question, collecting relevant stats data from basketball-reference.com, cleaning and formatting the data, exploring it with Python libraries like Pandas and NumPy, building and evaluating decision tree and random forest models, and discussing ways to improve the model's performance, such as modifying feature selection.
Tips to Become a Better Data Analyst - Data+Women GermanySarah Bartlett
This document provides tips for becoming a better data analyst, including mastering the basics of data tools, practicing regularly with learning projects, asking the right questions of data, studying design fundamentals, publishing work, engaging with the community, getting certified, leveraging community resources, and teaching others. The tips are part of a presentation on data visualization skills by Sarah Bartlett, a data analytics consultant with over 12 years of experience working with data.
The document discusses dashboarding and provides guidance on creating effective dashboards. It notes that dashboards can organize important information for easy interpretation, make decisions easier, and increase client confidence. Common problems with dashboards include including too much data, poor visual displays, and poor or confusing math. The document recommends that dashboards focus on visual design, include the right data on a single page with the most important information, and carefully select metrics. It also provides tips on choosing appropriate data visualizations like bar graphs, line graphs, and bullet graphs and maintaining dashboards through continuous improvement.
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.
Nubank is using machine learning, analytics and data engineering to disrupt the financial industry in Brazil. With over 5 million customers, it's already the biggest fintech outside Asia.
In this presentation I go over the main learnings of the company in the data space since it was founded 5 years ago. How did the company look like on each of those years? What mistakes did we make? What lessons did we learn?
This deck was presented in the São Paulo Product.io Meetup
This document provides an overview of developing an effective measurement strategy for content by focusing on storytelling. It discusses planning measurement by understanding goals, content, and key measures. It emphasizes collecting contextual and non-analytics data in addition to analytics and organizing all data centrally. Finally, it discusses presenting data persuasively through focus and storytelling, with examples of telling the story of where an organization was, what changed, the resulting impact, and next steps. The overall message is that numbers should be turned into stories and stories should drive action through an effective measurement process.
Number Stories: Win Friends and Influence HiPPOs with an Effective Measuremen...Michael Powers
Data overload has come to content strategy. With so many things to measure and tools to measure it with, how do you find a way to use analytics without succumbing to analysis paralysis? And without spending all your time on analytics? This session will walk through the creation of a measurement strategy that supports your existing content strategy. Then we’ll look at the ways you can use those analytics to tell the kinds of stories that persuade your peers and superiors to make smarter content decisions.
In this session, you will:
Learn how to decide what to measure and why
Find out how to create an analytics routine that provides actionable insights without taking up all your time
Learn to present measurements and analytics in ways that influence and persuade others
The document discusses how data from an LMS (learning management system) can be captured and utilized. It describes the types of data an LMS collects on users, courses, and completion rates. Key performance indicators (KPIs) that are typically measured by LMS data are also outlined, such as user engagement, training effectiveness, and compliance. The document emphasizes that high quality, clean data is important for accurate reporting and recommendations capturing data through integrations, housekeeping old records, and using reporting and analytics tools to gain insights from LMS data.
Advanced Use Cases for Analytics Breakout SessionSplunk
This document discusses Splunk's analytics capabilities and how to develop analytics for business users. It introduces personas as user types in a Splunk deployment beyond core IT. Requirements should be gathered for each persona, including their business problem, relevant data sources, and how they prefer to consume results. Searches and data models can then be developed and delivered through dashboards, visualizations, or third-party tools. Advanced analytics techniques discussed include anomaly detection, data visualization, predictive analytics, and demos. The document encourages reaching out for help from Splunk technical teams to grow analytics beyond IT.
Mindmaps: Agile and Lightweight Documentation for TestingTechWell
Quality starts with requirements. In small to mid-size companies, it is not uncommon for the communication chain to be broken. Florin Ursu shares ways to avoid miscommunication through a streamlined process in which requirements are communicated to both developers and testers simultaneously; then developers write code while testers document what will be tested. Florin explores what mindmaps are; what they can be used for, both in general and applied to software development; and then dives deeper into how mindmaps can be used for testing. He describes how his teams use mindmaps to brainstorm, organize testing scenarios, prioritize work, review test scenarios, present results to stakeholders highlighting what was tested and—just as importantly—what was not tested, issues found, and risks. Using example mindmaps, Florin highlights important details captured in day to day work, including tips regarding format, communication style, and how to “sell” the idea of mindmaps to your stakeholders.
4. Who are we?
Cayla Evans
• Junior @ Bishop Ireton
HS
• National bound hockey
player
• No prior work
experience
Dagny Evans
• Entrepreneur
• Expert in process
management, project
management and data
analytics
• Degrees from AU and GW
• Advocate & supporter for
WIT and young women
pursuing STEM
5. Project Overview
In Scope
• Using big data
techniques to predict
who will win the 2016
Stanley Cup
• Leverage interest in
sports to expose
technology to Cayla
Out of Scope
• Not a hardcore statistics
project
• Not a visualization
project
• No game-by-game stat
collection or analysis
6. Tools & Sources
• R & R Studio
• Various websites
– Helpful website lynda.com
– nhl.com
– stats.hockeyanalysis.com
– the teams’ personal website
• Excel/comma separated value text files
• Book: Practical Data Science in R (Nina Zumel & John
Mount)
• Github – presentation, data files & R scripts
posted (https://github.com/dagnyevans/stanleycup)
7. Methodology
1. Find & download the data
2. Combine disparate data sources
3. Cleanse data (spelling, cases)
4. Use Excel & R to analyze data
1. Looking for data quality & correlations in stats to
winners
5. Calculate mean of historical player stats as
2015-2016 stats
6. Aggregate player stats to team stats*
7. Train & test models against data sets
8. Project Details
• Data & R script walk-through
• Data Overview
– History records: 4,352
– Seasons: 5
– Teams: 30
– Players: 1,421
9. Complexity in Hockey Stats
• History of Hockey Stats/Inherent complexity
– Shots on goal is primary stat used in hockey
– Governing bodies still trying to figure out player
stats
• Other factors
– Best team does not always win
– Humans have bad days
– Performance of team is sum of player
performance
11. How’d we do?
• Learned fundamentals of data analysis
• Learned R syntax for: loads, functions, merges,
modeling, & analysis
• Cleansed and merged data to get to clean data
set for modeling
• Used history to predict 2015-2016 player stats
• Ran models and correlations to forecast
winner
On any given day, any team can win
12. Passing the torch
• Expand data set to include playoff participants
and game by game player stats
• Try alternate models
• Share your work!
Reminder: data sets, script and powerpoint all
avaialable at: https://github.com/dagnyevans/stanleycup
13. Cayla’s Lessons Learned
• Remember to save the work you do so that
you do not have to repeat yourself
• Computers are stupid and will do exactly
what you tell them to
• The data you start out with is not always the
data you need
• Trial and error
• Map your project
• Take notes – process, progress and results
14. Dagny’s Lessons Learned
• Don’t assume your intern knows everything you
do
• Act -> Review -> Proceed -> Repeat
• Just because you have the tools, doesn’t mean
you can answer the question
• Clear, concise written reference & how-to
instruction for r (or data science) are hard to find
• If you use an interesting subject to introduce tech
ideas, you can engage (and teach) young people
about tech
Editor's Notes
Cayla
I am Cayla Evans. I am a junior at Bishop Ireton HS and am a national bound hockey player. I do not know what I want to do yet. I am planning to use the next two years to do that. This project was a way for me to see if tech is something I want to do.
Dagny
Joined husband in March to run our software & data integration consulting company
Prior to that worked in across dotcom, telecom, data analytics industries –worked at several small growing DC business on cutting edges of industry
Big believer there are many paths to tech
Cayla
We decided to do this particular project because I am starting to think about what I want to study in College. Data Science seems cool. This project allows me to learn about Data Science using a topic I’m interested in. The real goal is to see if Data Science is something I want to do when I get out of college.
Dagny
Inspiration comes from many sources – this project is product of letting my mind wander
I really wanted a project that would expose Cayla to technical opportunities, not just softer business skills (although we worked on those too)
Husband too busy, so I leverage something I was good at
Cayla
Used many different sources. My mother bought me a couple of books for understand concepts and even made me write book reportscon them.
Also used various websites when I couldn’t figure out to do something and to find my data
For majority of project used R.
Cayla
I located the player and team stats of the ‘10-’11 through ‘14-’15 seasons. I took those stats & loaded them all into R so that I could correlate any of the stats with each other.
Just a few days after the analysis, I realized that the stats I had loaded were not up to date. I was able to find and load new player/team stats. Right after the data was loaded and proved to be right I mapped out the plan for the rest of the project.
Cleansing the data isn’t finished one time through
I merged the player and goalie stats into the Rosters of all 30 teams in the NHL. Using the rosters I then calculated the averages for the player stats and the one goalie stat that would be needed to make the team stats. Once I calculated the averages I filled in the ‘blank’ 2015-2016 stats. I then aggregated or added the player and goalie statistics to make the team stats.
Dagny
My role – advisor, researcher, quality control, cardboard batman
*applied model & correlation to both data sets
Cayla
Important stats – shots, icorsi, ifenwick, Sv%
Different approaches to get to the same results
(last 50 years) Shot on goal a flawed statistic because “on goal” – if it hits the goalie, it’s considered on goal. But if it hits the pipe, it’s not a shot. Goalie stat only not a player stat. still trying to figure out
Take an example: Alex Ovechkin shoots – 1) goes in -> goal and shot; 2) 5 ft wide, but goalie grabs it -> shot; 3) 5 ft wide, but goalie doesn’t touch it -> no shot; 4) Hits the post, misses the net -> no shot
Fenwick is shots plus all shot attempts that missed the net (i.e. hit the post/crossbar, shot wide, etc.)
Corsi is Fenwick plus all shots attempts that were blocked by the defending team
I have played hockey for the past 8 years. The best team does not always win. We are human. Humans have bad days. Since one player is not responsible for the win of a game the performance of the team is critical. Bad days for the players could mean a bad day for the team.
Example of 3 core player stats at team level.
No clear outliers
President’s cup winner (best team at end of regular season) did not win stanley cup
Neither cup winners had significantly higher stats
The root is always the question I’m trying to answer – business question
Mapped project from data collection to answering the business question
Data collection; cleansing; analysis; results
One practical one: R is a bit finicky. It’s caching the work until you save it, so if you didn’t save enough or “reset the cache”, syntax that worked previously would return funky results