Use Data Mining technique (Decision Tree, Bootstrap Forest,Boosted Tree, Neural Network, Nominal Logistic Regression) to predict wining probability for each team. Accuracy in 2015 (until 3/26) is 73%.
Develop a forecasting model that predicts the chance of each NCAA Men's College Basketball team reaching each round and examines factors contributing to a team's performance, based on their performance data from 2001-2014.
Tools/Software: SAS JMP, Tableau, Excel, SPSS
Apply Data Mining and Machine Learning technique(Feature Selection, Classification Algorithm, Model Optimization, System Ensemble) to predict wining probability for each team.
To Send or Not to Send -- What Data Says About Sending Branded SwagSales Hacker
What You'll Learn:
- Whether you should include direct mail in your 2019 budget
- How to create (and measure) the effectiveness of a direct mail campaign
- If sending branded swag could help you build pipeline
Project
E-sports awareness and opportunities
Sep 2020 – Oct 2020
Project descriptionObjectives of the project were:
•Assess involvement in E-sports based on demographics
•Comparison of popularity and preference of streaming websites for viewers and players.
•Pandemic's influence on E-sports industry.
•To understand the scope of E-sports as a career.
Software used: Excel
The Data Quality Formula details the fundamental elements to data quality; Detection, Analysis and Resolution. In order for businesses to realise success, they must understand the Rule of 3.
Develop a forecasting model that predicts the chance of each NCAA Men's College Basketball team reaching each round and examines factors contributing to a team's performance, based on their performance data from 2001-2014.
Tools/Software: SAS JMP, Tableau, Excel, SPSS
Apply Data Mining and Machine Learning technique(Feature Selection, Classification Algorithm, Model Optimization, System Ensemble) to predict wining probability for each team.
To Send or Not to Send -- What Data Says About Sending Branded SwagSales Hacker
What You'll Learn:
- Whether you should include direct mail in your 2019 budget
- How to create (and measure) the effectiveness of a direct mail campaign
- If sending branded swag could help you build pipeline
Project
E-sports awareness and opportunities
Sep 2020 – Oct 2020
Project descriptionObjectives of the project were:
•Assess involvement in E-sports based on demographics
•Comparison of popularity and preference of streaming websites for viewers and players.
•Pandemic's influence on E-sports industry.
•To understand the scope of E-sports as a career.
Software used: Excel
The Data Quality Formula details the fundamental elements to data quality; Detection, Analysis and Resolution. In order for businesses to realise success, they must understand the Rule of 3.
This competition is challenging because the attendants are asked to use Microsoft Azure to analyze any open data. The judge would evaluate team's performance based on visualization and modeling. Our team analyzed the factors that cause multiple car crashes and the correlation between Vehicle crashes and repair shops.
Adopt clustering and sentiment analysis to find out which topic Jeb Bush cared the most from his email contents during 1999-2006 and compare the voters’ opinions from Twitter
Coke Zero Official Document 2013: It’s Not Your Fault You Can’t Stop Watching...Coke Zero
This presentation offers a detailed exploration into all of the reasons why man is unable to do anything but watch basketball during the NCAA tournament. Plus, it has pictures!
Download the full presentation: http://bit.ly/YLX8Ul
在這個資料科學蔚為風潮的年代,身為一個對新技術充滿好奇的攻城獅,自然會想要擴充自己的武器庫,學習嶄新的資料分析工具;而 R 語言,一個由統計學家專門為了資料探索與分析所開發的腳本語言,具有龐大的開源社群支持以及琳瑯滿目、數以萬計的各式套件,正是當今學習資料科學相關工具的首選。
然而,R 語言的設計邏輯與一般的程式語言不同,工程師們過去學習程式語言的經驗,往往造成學習 R 語言的障礙,本課程將從 R 語言的基礎開始,讓同學們從課堂講解以及互動式上機課程中,得以徹底理解 R 語言的核心概念與精要,學習如何利用 R 語言問資料問題,並且從資料分析的角度撰寫效率良好同時具有高度可讀性的 R 語言代碼。
The Location-Tagged Social Score Sheet: 2015 NCAA March Madness InfographicGeofeedia
To see how the games stacked up on the social side, we created geofences around the championship, Final Four, NCAA Sweet 16 and Elite Eight stadiums (and the teams' college campuses!) to catch the NCAA March Madness social buzz. Read more, on the blog: http://bit.ly/1IG0Who
Kevin Waida graduated from the University of Missouri-Columbia with a bachelor’s degree in communications and minors in business and financial planning. He is now a student in the Colorado State University master of business administration program. Outside of his studies, Kevin Waida maintains a strong interest in college basketball.
“From Eliza to Siri and beyond: Promise and challenges of intelligent, langua...diannepatricia
“From Eliza to Siri and beyond: Promise and challenges of intelligent, language-controlled assistants/chatbots.“ - Alexander Braun, founder of Creative Construction Heroes presented as part of the Cognitive Systems Institute Speaker Series on Nov. 3, 2016.
Figuring out the right metrics for your gameSaurav Sahu
This is a talk I gave at IGDA Conference 'Industry Speaks' on 1st April'17. I talked about how one should go about thinking the metrics to track in their games. Also, stressed on the fact that Analytics should not be an after-thought but should be squeezed in during the game production phase itself.
The slide discusses Google's HEART framework and Pirate Metrics while sharing an approach Goals/Signals/Metrics to make it easy to list down metrics once you have your goals.
The latter part of the slides talks about the generic biases one should be aware of.
Feel free to reach out incase of any query.
This competition is challenging because the attendants are asked to use Microsoft Azure to analyze any open data. The judge would evaluate team's performance based on visualization and modeling. Our team analyzed the factors that cause multiple car crashes and the correlation between Vehicle crashes and repair shops.
Adopt clustering and sentiment analysis to find out which topic Jeb Bush cared the most from his email contents during 1999-2006 and compare the voters’ opinions from Twitter
Coke Zero Official Document 2013: It’s Not Your Fault You Can’t Stop Watching...Coke Zero
This presentation offers a detailed exploration into all of the reasons why man is unable to do anything but watch basketball during the NCAA tournament. Plus, it has pictures!
Download the full presentation: http://bit.ly/YLX8Ul
在這個資料科學蔚為風潮的年代,身為一個對新技術充滿好奇的攻城獅,自然會想要擴充自己的武器庫,學習嶄新的資料分析工具;而 R 語言,一個由統計學家專門為了資料探索與分析所開發的腳本語言,具有龐大的開源社群支持以及琳瑯滿目、數以萬計的各式套件,正是當今學習資料科學相關工具的首選。
然而,R 語言的設計邏輯與一般的程式語言不同,工程師們過去學習程式語言的經驗,往往造成學習 R 語言的障礙,本課程將從 R 語言的基礎開始,讓同學們從課堂講解以及互動式上機課程中,得以徹底理解 R 語言的核心概念與精要,學習如何利用 R 語言問資料問題,並且從資料分析的角度撰寫效率良好同時具有高度可讀性的 R 語言代碼。
The Location-Tagged Social Score Sheet: 2015 NCAA March Madness InfographicGeofeedia
To see how the games stacked up on the social side, we created geofences around the championship, Final Four, NCAA Sweet 16 and Elite Eight stadiums (and the teams' college campuses!) to catch the NCAA March Madness social buzz. Read more, on the blog: http://bit.ly/1IG0Who
Kevin Waida graduated from the University of Missouri-Columbia with a bachelor’s degree in communications and minors in business and financial planning. He is now a student in the Colorado State University master of business administration program. Outside of his studies, Kevin Waida maintains a strong interest in college basketball.
“From Eliza to Siri and beyond: Promise and challenges of intelligent, langua...diannepatricia
“From Eliza to Siri and beyond: Promise and challenges of intelligent, language-controlled assistants/chatbots.“ - Alexander Braun, founder of Creative Construction Heroes presented as part of the Cognitive Systems Institute Speaker Series on Nov. 3, 2016.
Figuring out the right metrics for your gameSaurav Sahu
This is a talk I gave at IGDA Conference 'Industry Speaks' on 1st April'17. I talked about how one should go about thinking the metrics to track in their games. Also, stressed on the fact that Analytics should not be an after-thought but should be squeezed in during the game production phase itself.
The slide discusses Google's HEART framework and Pirate Metrics while sharing an approach Goals/Signals/Metrics to make it easy to list down metrics once you have your goals.
The latter part of the slides talks about the generic biases one should be aware of.
Feel free to reach out incase of any query.
Do lower-seeded teams really play with an "underdog" mentality?Kymee Noll
In my Elementary Statistics class my freshman year of college, our final project was one of our choice. I chose to compare field goal percentages between higher and lower seeded teams in the NCAA Division I Men's Basketball Tournament.
Enabling Managers To Coach with Data [Webinar Slides]Frederik Hermann
Join us as we share how to enable front line managers to coach with performance and correlation data. The team at RingCentral rolled out a number of programs to improve the quality and effectiveness of coaching in a remote environment with prescriptive sales performance data. Our guests Sarah Fricke (Director Sales Enablement) and Caitlin Lambert (Enablement Analyst) from RingCentral are the ones spearheading these initiatives and will a share a hands-on playbook and templates to boost sales performance with data-driven enablement.
Here are some key takeaways you can expect from this conversation:
1. What it means to coach in a remote environment
2. How to align to sales leader goals
3. Equipping managers with the right metrics to coach to
4. A dashboard template with the metrics and KPIs that matter most
5. And how to bring this all together to implement with systems and processes
Watch the recording at https://saleshood.com/enabling-managers-to-coach-with-data/
--
Brought to you by Saleshood - https://saleshood.com
Saleshood is the leading all-in-one sales enablement platform used by hyper-growth companies to boost sales performance. Saleshood is proven to reduce time to ramp, lift quota attainment and accelerate sales velocity. Successful hyper-growth companies like Drift, Demandbase, Bombora, Domo, Omada Health, Sage, Seagate, RingCentral, Tanium, Tealium, Trinet, and Yext use Saleshood to realize fast revenue outcomes with 100% virtual training, coaching and selling – at scale.
Employee Engagement: What is it? How Do You Improve it? 10 Best Practices fro...Qualtrics
Engaged employees are more productive, contribute more to the bottom line, generate higher customer ratings, and help you attract new talent. On the flip side, actively disengaged employees cost the US approximately half a trillion dollars per year.
Join us to learn the best practices from Mike Schroeder, CEO of TNS Employee Insights, on how to design employee engagement surveys, measure engagement and, most importantly, improve employee engagement in your organization.
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
Show drafts
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Empowering the Data Analytics Ecosystem: A Laser Focus on Value
The data analytics ecosystem thrives when every component functions at its peak, unlocking the true potential of data. Here's a laser focus on key areas for an empowered ecosystem:
1. Democratize Access, Not Data:
Granular Access Controls: Provide users with self-service tools tailored to their specific needs, preventing data overload and misuse.
Data Catalogs: Implement robust data catalogs for easy discovery and understanding of available data sources.
2. Foster Collaboration with Clear Roles:
Data Mesh Architecture: Break down data silos by creating a distributed data ownership model with clear ownership and responsibilities.
Collaborative Workspaces: Utilize interactive platforms where data scientists, analysts, and domain experts can work seamlessly together.
3. Leverage Advanced Analytics Strategically:
AI-powered Automation: Automate repetitive tasks like data cleaning and feature engineering, freeing up data talent for higher-level analysis.
Right-Tool Selection: Strategically choose the most effective advanced analytics techniques (e.g., AI, ML) based on specific business problems.
4. Prioritize Data Quality with Automation:
Automated Data Validation: Implement automated data quality checks to identify and rectify errors at the source, minimizing downstream issues.
Data Lineage Tracking: Track the flow of data throughout the ecosystem, ensuring transparency and facilitating root cause analysis for errors.
5. Cultivate a Data-Driven Mindset:
Metrics-Driven Performance Management: Align KPIs and performance metrics with data-driven insights to ensure actionable decision making.
Data Storytelling Workshops: Equip stakeholders with the skills to translate complex data findings into compelling narratives that drive action.
Benefits of a Precise Ecosystem:
Sharpened Focus: Precise access and clear roles ensure everyone works with the most relevant data, maximizing efficiency.
Actionable Insights: Strategic analytics and automated quality checks lead to more reliable and actionable data insights.
Continuous Improvement: Data-driven performance management fosters a culture of learning and continuous improvement.
Sustainable Growth: Empowered by data, organizations can make informed decisions to drive sustainable growth and innovation.
By focusing on these precise actions, organizations can create an empowered data analytics ecosystem that delivers real value by driving data-driven decisions and maximizing the return on their data investment.
2. Introduction
❖ Background: NCAA Men’s Basketball Tournament is a single-elimination tournament,
currently featuring 68 college teams.
❖ Objective: Create an effective model that examines factors contributing to a team’s
performance, based on data from 2001-2014.
❖ Result: As can be analyzed from the model, box score has a large effect on a team’s
result in 2015, which is helpful to predict:
➢ Win/Lose
➢ Winning Probability
➢ Sweet Sixteen
2
3. 3
Independent & Dependent Variables
Independent
Variables
SeedLocation
Box
Score
Assist, Steal, Block Shot,
% 2/3 Point Field Goals,
% Free Throws, Tempo
Seed#,
If this team is Top 5,
If this team is 15/16
Latitude, Longitude,
Distance Difference
Dependent Variable:
Win/Lose
5. ● Distribution Review: Most variables are normal distributed
5
Distribution and Correlation
● Scatter Matrix: Few variables has linear correlation
6. 5 Models Performance
Validation
Nominal Logistic Regression Accuracy: 72%
ROC Curve for Validation
Nominal Logistic
Regression has the
best performance
Performance Validation
6
Training
7. Result Lose Win
Lose 6 6
Win 5 24
Total 11 30
● 2015 Forecast Top 16 team● 2015 Forecast Result: 73% accuracy
Prediction
7
8. Model Explanation
Defensive efficiency, offensive efficiency, opponent’s
blocked shots and assists are most important attributes
based on individual p-value
According to our analysis results, good offensive efficiency
contributes more than defensive efficiency in leading a
team’s success
The closer
the distance
to stadium,
the better
result a team
performs
8
9. Interesting Analysis
● Average score difference is narrowing down
● The score pattern for Top 5 Seeds is less volatile
than the one for bottom 2 seeds
● 9 out of 16 is predicted correctly
● Only Georgetown shows a declining pattern
of winning probability
9
10. Result and Conclusion
❖ Whether a team wins or loses is positively related to four
primary factors:
➢offensive efficiency
➢defensive efficiency
➢block shots
➢assists
❖ Accuracy: Our model is 72.19% accurate in predicting a
team’s result for 2015.
10