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Data Analytics

Transcript: Data Analytics Project Data Analytics Group 65: Aliya Sheikh A20384623 Falgun Shah A20376282 Machine learning from Disaster (Titanic Dataset) Machine learning from Disaster (Titan... Under the guidance of: Prof. Yong Zheng Content Content Contents Objective Dataset Understanding Data Cleansing Data Modeling Data Evaluation Statistical Analysis Contents Objective Objective Objective Objective Introduction Introduction On April 15, 1912, during her maiden voyage, the Titanic sank after colliding with an iceberg. One of the reasons that the shipwreck led to such loss of life was that there were not enough lifeboats for the passengers and crew. Primary Goal Primary Goal Predict survival on the Titanic Comparing which category of people survived the most. Survived v/s Class Survived v/s Sex Survived v/s Port of Entry Dataset Dataset Dataset Understanding Dataset Understanding Survival Yes =1 | No = 0 pClass 1st= Upper | 2nd= Middle | 3rd= Lower sex Male | Female age(in yrs) sibsp # of siblings / spouses aboard the Titanic parch # of parents / children aboard the Titanic ticket fare cabin embarked C = Cherbourg | Q = Queenstown | S = Southampton (*port of embarkment) Data Cleansing Data Cleansing Missing Values Missing Values Cabin variable : Ignoring this variable, as it has a lot of missing values and does not have much impact on the model. Handling Missing Values Age: Replacing the missing values by taking the mean of the age. Embarked: Removing the missing values, since there are only two missing values. Multi-Collinearity Multi-Collinearity Value of collinearity > 5 between the two variables --> the variables are strongly correlated. From the graph, we can state that there is no collinearity. Influential Points Cook's Distance: Any point over 4/n will be considered as Influential Points. where, n = number of observations Using influence.measures(forward_model), we found out that, none of the points are greater than 4/n. Thus, there are no influential points. Influential Points Data Modeling Data Modelling Logistic Regression Logistic Regression When the target variable is Qualitative When response variable is Binary Yes / No True / False Approved / Rejected Full Model Insignificant Variables: Parch Fare Embarked AIC: 802.19 Forward Selection Backward Selection Stepwise Selection Model Selection Model Selection Forward Selection Model Forward Selection Model AIC: 662.08 Adj-R2: 0.315 Backward Elimination Model Backward Elimination Model AIC: 662.08 Adj-R2: 0.315 Stepwise Selection Model Stepwise Selection Model AIC: 674.03 Adj-R2: 0.307 Model Interpretation Selecting the Best Model: On comparing the models, based on AIC and Adj-R2, we can state that: Forward/Backward regression model is the best model. AIC: 622.08 Adj-R2: 0.315 Model Interpretation f(x) = 5.18 - 1.17 * Pclass - 2.73 * Sex - 0.04* Age - 0.36 * SibSp Model Interpretation Odds of Success (Sex): exp(-2.73) - 1 = 0.93 --> 93% Percentage change in odds of success for every unit increase in X. Model Evaluation Model Evaluation Accuracy Accuracy Accuracy : 0.82 Misclassification Error: The decision will be based on the boundary 0.5. • P(y=1|X) > 0.5 then y = 1 • P(y=1|X) < 0.5 then y = 0 Statistical Analysis Survival on the Titanic Count of survivors: 469 Survived v/s Sex Based on the plot, we can conclude that: Female Survivors: 359 Male Survivors: 110 Survived v/s Embarked Based on the above plot, we can conclude that: • Survivals from Port of entry C: 131  Male: 30  Female: 101 • Survivals from Port of entry Q: 50  Male: 3  Female: 47 • Survivals from Port of entry S: 288  Male: 77  Female: 211 Survived v/s Class Based on the above plot, we can conclude that: • Survivals from Upper Class: 185  Male: 46  Female: 139 • Survivals from Middle Class: 116  Male: 17  Female: 99 • Survivals from Lower Class: 168  Male: 47  Female: 121 Survivor's Names Questions ? Questions ?

Which is best: Keynote, SlideRocket, Prezi, or PowerPoint?

Transcript: Cloud-based Professional Templates PowerPoint Compatible Import media Available for Mac and Windows Different options Online and Offline access Import PowerPoint or Google presentations Real time data Online Security Measure effectiveness We will cover: PowerPoint Characteristics Prezi Characteristics Keynote Characteristics SlideRocket Characteristics Clearside Conclusion Recently acquired SlideRocket Company that helps sales leaders achieve higher productivity by proving real time visibility and analytics to improve customer interaction Keynote Free- Prezis are public and limited storage $4- Private prezis and 500 MB of storage $14- Private prezis and 2GB of storage Available for Mac Apple designed Themes iCloud based Creative tools PowerPoint compatible Clearside Valeria Oropeza Available in iTunes for $9.99 Varied Themes Presenter View Share over the web Improved video and audio supports PowerPoint on touch devices Conclusion Which is Best: PowerPoint, Keynote, Prezi or SlideRocket PowerPoint 2013 Prezi SlideRocket

Data Analytics

Transcript: GET OUT OF YOUR SILOS Clearly define what success means to your Project Business Unit Organization What takes 10 milliseconds? Update internal status for every encounter as soon as there's a more recent status available Automated queuing of reviews for new claims which have targeting or associated HEDIS measures Create an automation process Try to take a step back and make sure your foundation is solid good morning! BIG DOCUMENT DOCUMENT Spread what you've learned across the organization Plan to include lots of dates: Project start and end dates Data load dates AUTOMATION Audit everything Apply any model to any data set and any date of service span Let analytics drive your strategies This is how we've always done it My gut tells me that this is right We don't have time to wait Systems that need work-arounds --------------------------------------------------------------------------------- Learn from the results of your measurements so that you can make new, better measurements DATA Automation DOCUMENT Carefully document every process thoroughly hello world what's new? RA chart review Analytics Every product that supports your programs What other questions could this analysis/project answer? If collecting data, reach out to other business units Existing data Data needs Create measurements for your goals --------------------------------------------------------------------------------- Everyone is trying to simultaneously build & fly the airplane -------------------------------------------------------------------- Project/Initiative names and descriptions DOCUMENT Make new measurements DOCUMENT Establish collection rules Simple NLP project targeting words/phrases with high probability of a positive HCC hit: amputation, stoma stock market transactions Planning well for data Reports that you can distribute to business units to act on immediately Care management reports on new conditions on the MOR Diagnoses/encounters with EDPS/RAPS errors New encounters with conditions that require monitoring and evaluation or will be part of HEDIS measures Data transmission logs to catch submission gaps/process errors Measurements for current project goals Dates CULTURE SHOCK Reports just for the sake of producing reports Keep original files Topics: Real-time data Time to obtain attestations Work with other business units to: Change your organizational culture to support an analytic culture, not a status report culture EDPS/RAPS Vendors Canned reports Audits/Reconciliation Regular actionable reports Outliers and alerts Analyze and clean your data prior to loading it Central repository catalog and review tool for all chart images, coding reviews, and auditing for RA and HEDIS Maximize resources ACTION! Time to fix records before EDPS/RAPS submission Time for interventions Align timeframes Data sources with results that you can't replicate Bump new claims against any EMR access you have as they are received Megan Lent, MIS FOUNDATIONS WHAT'S NEW? Re-evaluate your definition of success often Keep model tables and logic separate and flexible Questionable data sources, or Collaborating with other business units Anything that you do repeatedly can and should be automated Use a qualified encounter table Plan Ahead Repeat Measurements Make new goals using the lessons learned from your measurements Current projects: Questions? Actionable Reports Free Stuff! cool story bro Making time to automate will: Help set a good foundation Allow for scalable growth Reduce errors Free analysts and other resources to do bigger better things GROWTH BEST PRACTICES Let's get real-time Tools & Data Storage aka "Advice from an analyst" DATA SOURCES Start with a single source of data for analytics Don't make it more difficult by having to reconcile multiple sources of data ex: Multiple reports that are meant to answer the same question, but have different results if you can cool stuff

PowerPoint Game Templates

Transcript: Example of a Jeopardy Template By: Laken Feeser and Rachel Chapman When creating without a template... http://www.edtechnetwork.com/powerpoint.html https://www.thebalance.com/free-family-feud-powerpoint-templates-1358184 Example of a Deal or No Deal Template PowerPoint Game Templates There are free templates for games such as jeopardy, wheel of fortune, and cash cab that can be downloaded online. However, some templates may cost more money depending on the complexity of the game. Classroom Games that Make Test Review and Memorization Fun! (n.d.). Retrieved February 17, 2017, from http://people.uncw.edu/ertzbergerj/msgames.htm Fisher, S. (n.d.). Customize a PowerPoint Game for Your Class with These Free Templates. Retrieved February 17, 2017, from https://www.thebalance.com/free-powerpoint-games-for-teachers-1358169 1. Users will begin with a lot of slides all with the same basic graphic design. 2. The, decide and create a series of questions that are to be asked during the game. 3. By hyper linking certain answers to different slides, the game jumps from slide to slide while playing the game. 4. This kind of setup is normally seen as a simple quiz show game. Example of a Wheel of Fortune Template https://www.teacherspayteachers.com/Product/Wheel-of-Riches-PowerPoint-Template-Plays-Just-Like-Wheel-of-Fortune-383606 Games can be made in order to make a fun and easy way to learn. Popular game templates include: Family Feud Millionaire Jeopardy and other quiz shows. http://www.free-power-point-templates.com/deal-powerpoint-template/ Quick video on template "Millionaire" PowerPoint Games Some games are easier to make compared to others If users are unsure whether or not downloading certain templates is safe, you can actually make your own game by just simply using PowerPoint. add logo here References Example of a Family Feud Template PowerPoint Games are a great way to introduce new concepts and ideas You can create a fun, competitive atmosphere with the use of different templates You can change and rearrange information to correlate with the topic or idea being discussed. Great with students, workers, family, etc. For example: With games like Jeopardy and Family Feud, players can pick practically any answers. The person who is running the game will have to have all of the answers in order to determine if players are correct or not. However, with a game like Who Wants to be a Millionaire, the players only have a choice between answers, A, B, C, or D. Therefore, when the player decides their answer, the person running the game clicks it, and the game will tell them whether they are right or wrong.

Data Analytics

Transcript: VERIFICATION AGAINST OSM DATA SAMPLE CHECK - NEVADA OSM name value Faulty location data Completeness Timeleness Validity Accuracy Postcode OUTPUT Name Maximum density areas: Las Vegas Arizona Montreal Edinburgh Apt/suite number Matching Yelp and OSM data Data visualization COMPLETENESS RESULTS APT/SUITE NUMBER AND STATION INFORMATION + MUNICIPALITY/PROVINCE + POSTAL CODE Categories Technologies PostgreSQL Python QGIS WHAT Some names contain city OSM APT/SUITE NUMBER + STREET + CITY + ZIP CODE Highway = hwy Lane = ln Trail = tl Parkway = pkwy Content Data Analytics courtyard phoenix airport, courtyard phoenix mesa, courtyard phoenix north, courtyard by marriot - madison east starbucks coffee company uk, starbucks, starbucks atrium, starbucks coffee Data Quality Assessment Name, Full address, City, State, Longitude, Latitude, Open, Type, Review count, Stars Yelp is the best way to find great local businesses. Lines of data: 61,184 WHAT ? WHERE ? WHY ? WHEN ? CONTACT INFO Information source: Mentor Google Presented by: Dautbegović Amra COORDINATES Introduction Yelp Academic data inspection Matching between Yelp and OSM Personal experience House number Street / Road City / Town Heidelberg /usr/lib /zowie/bowie discount tire store - henderson, nv North = N South = S East = E West = W Project definition OSM places 2340 w bell rd Categories Road NAMESAKE CITIES IN DIFFERENT STATES PERSONAL EXPERIENCE Neighborhoods Working hours Number of entries Verona Some names contain category Relevant attributes (SUB)CATEGORIES Nightlife(nightlife) Bars(bars) Beer Bar (beerbar) Pubs (pubs) Gay Bars (gaybars) Full address City THANK YOU! CONS Marked countries: Canada Germany UK USA OSM and Yelp visualization Middleton STREET + APT/SUITE NUMBER + ZIP CODE + CITY Unstandardized data Non-categorized categories Lack of contact information Tied hands for future plans State middle schools & high schools religious schools swimming lessons/schools vocational & technical school specialty schools cooking schools preschools language schools driving schools dance schools massage schools cosmetology schools art schools elementary schools No past experience Very interesting Time consuming OSM servers' limited number of requests over a period of time OSM - 15,732 Yelp - 25,230 Categories High businesses area density Useful information about businesses High completeness Accurate addresses Mentor: Džubur Samra ADDRESS 2340 west bell road MATCHING ALGORITHM YELP ADDRESS STANDARDIZATION Yelp places YELP Yelp Low completeness Format: HH:MM Depends on categories Creating our own website like www.navigator.ba or www.foursquare.com Inspect POI data - Yelp data Visual presentation Match data against POI OSM data Enrich OMS data with addresses Latitude Longitude PROS Road = rd Building = bldg Avenue = av Suite = ste WHERE Working hours Name Addresses ABBREVIATIONS Categories Matched Partially matched Not matched Less words in the name 1.1 Latitude & longitude 1.2 Address comparison 1.3 Combination NAME CATEGORIES LOCATION INPUT Allowed distance depends on category e.g. Golf court and Pharmacy 2.4 % 1.9% Matched Partially matched Not matched 95.7 % Synonyms SAME COORDINATES FOR DIFFERENT FULL ADDRESSES APT/SUITE NUMBER + E/W/N/S + STREET + CITY + STATE + ZIP CODE SAME COORDINATES FOR DIFFERENT STATES SAME COORDINATES FOR DIFFERENT CITIES

Data Analytics

Transcript: Look at other companies to see how they gather data Specific Software What is working Is the data already being collected useful? If not what data would be more useful Look at the types of warranties that are underperforming Common Problems? Insights Problem Out of control warranty costs Biggest Concerns apply new techniques on existing and new data to generate insights Create a test-and-learn environment for continuously harnessing the insights Explore data sets to understand how they would help in accepting or refuting the hypotheses Use qualitative and quantitative analysis techniques to use data to validate the hypotheses Convert outputs into user-friendly formats and visualizations that will help different stakeholders understand the analysis Ton's of data with no way to organize or make use of it Cannot share data across company Cannot collect data from onboard computers Final Project Case Study #3 Client’s challenge: Global automotive manufacturer A leading global automotive manufacturer realizes that its warranty costs are out of control because several of the business functions across the organization, including finance, sales and marketing, quality, and engineering, are unable to share data and make timely tactical and strategic business decisions related to emerging warranty and quality issues. The main operational obstacle is that they lacked real-time consolidated data and the reporting and analytics tools needed to drive insights, reduce costs and improve quality. Manual business processes exist within multiple siloed business areas. Analysts are spending the majority of their time gathering data and generating reports rather than analyzing information. There is no place where all of the granular, vehicle-level information came together to create a master set of data and a “single version of the truth.” While vehicles are constantly collecting potentially valuable real-time data about their performance and faults via their on-board computers, like many of their competitors, the company has not devised a way to collect it. When sample data is collected, the company still cannot combine it with existing warranty data or mine it to determine root causes of problems. Addressing potential problems pre-emptively could save significant amounts of money and bolster the brand’s reputation for quality at the same time. Other companies in the industry were beginning to see the strategic value of data analytics and it is time to take action. Planning

Data Analytics

Transcript: Presenter: Anand Ulle Data Analytic Life Cycle Model Building Why Do we care? Determine if the team succeeded or failed in its objectives  Assess if the results are statistically significant and valid  If so, identify aspects of the results that present salient findings  Identify surprising results and those in line with the hypotheses  Communicate and document the key findings and major insights derived from the analysis  This is the most visible portion of the process to the outside stakeholders and sponsors Classify Data Big Data Ex data preparation Common Tools Real Steel: Courtesy ImDb Operationalize 1. Learning the Business Domain 2. Resources 3. Framing the Problem 4. Identifying Key Stakeholders 5. Interviewing the Analytics Sponsor 6. Developing Initial Hypotheses 7. Identifying Potential Data Sources Big Data conti... Need for Data Analytics Data Analytics Structured Data Understand patterns, correlations amongst the wide, highly voluminous data to arrive at a decision. In this last phase, the team communicates the benefits of the project more broadly and sets up a pilot project to deploy the work in a controlled way  Risk is managed effectively by undertaking small scope, pilot deployment before a wide-scale rollout  During the pilot project, the team may need to execute the algorithm more efficiently in the database rather than with inmemory tools like R, especially with larger datasets  To test the model in a live setting, consider running the model in a production environment for a discrete set of products or a single line of business  Monitor model accuracy and retrain the model if necessary Source: All logos are taken from the internet What is Data? model planning Data exploration and selection model selection common tools discovery Communicate Results In the near future when people become uninterested in boxing and similar sports, a new sport is created - Robot boxing wherein robots battle each other while being controlled by someone. Charlie Kenton, a former boxer who's trying to make it in the new sport, not only doesn't do well, he is very deeply in the red. When he learns that his ex, mother of his son Max, dies, he goes to figure out what to do with him. His ex's sister wants to take him in but Charlie has first say in the matter. Charlie asks her husband for money so he can buy a new Robot in exchange for turning Max over to them. He takes Max for the summer. And Max improves his control of his robot. But when the robot is destroyed, they go to a scrap yard to get parts. Max finds an old generation robot named Atom and restores him. Max wants Atom to fight but Charlie tells him he won't last a round. However, Atom wins. And it isn't long before Atom is getting major bouts. Max gets Charlie to teach Atom how to fight, and the father and son bond strengthens  explore data Analyze the data ETL - Extract transform Load Learning from the data Data conditioning- cleaning, Normalizing Visualize Unstructured Data

Data Analytics

Transcript: SWOT INDUSTRY PERSONNEL TRAININGS Pre-Configured Industry Generic Solutions CORE COMPETENCY SUCCESS GUARANTEED MONEY BACK POLICY Sales Forecast VISION includes Staffing companies INDUSTRY FOCUS - FINANCIAL - HEALTH - MANUFACTURING - RETAIL - INSURANCE STRIVE EXCEL RESEARCH + WHITE PAPERS STRATEGY Visionary LIMITED NETWORK SMALL PLAYER PROJECT FAILURES The short term objective is to start this company quickly and inexpensively, The long term objective is to grow the company into a stable and profitable entity DELIVERY PERIOD EFFICIENCY PERFORMANCE Flexibility Venkata Pulapa Technology INTERNET MARKETING PRICING Data Abundance QUALITY PERFORMANCE Responsibility Organizational Chart Quantitative Analysis Potential Market Proven Methodology INNOVATION Pattern Symposiums Knowledge INFORMED decisions DECISION SUPPORT SYSTEMS Creativity SUCCESS Dynamic Data Driven PRODUCTS/SERVICES MISSION Implementation of Analytical Solutions Products/ Services Cost Period Type Knowledge/Information Equity Discovery 2weeks Fixed Pre-Configured Industry Generic Solutions$90,000 Module Fixed Implementation of Analytical Solutions Project Cost/Week Variable QUESTIONS Business Intelligence Solutions Competition Competitive REVENUES LIMITED FUNDS LIMITED RESOURCES NO TRACK RECORD The business value of Decision Making Systems comes from and through people. Data Analytics LLC., with its high quality service wants to retain the customer pool along with capturing a quarter shares in the market in the next 5 years. Results Knowledge/Information Equity Discovery PERSISTENCE MARKET ANALYSIS MARKETING Resource Allocation & Availability Professionalism Entrepreneurship Experience NETWORK DATA MINING & DISCOVERY Huge Data Storage

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