Transcript: McArthur High Science TOGETHER WE SUCCEED... Teacher Comparative Data Francis 59% (3classes) Victor 57% (1 classes) Pernell 47% (3 classes) Santos 38% (4 classes) Spradley 33% (2 classes) Lowest 25% Proficient BIO EOC Housekeeping/Accountability Revamp IFC (standard based) Student centered (reading/comprehension, higher level questions) WEBS Depth of Knowledge PLCs are crucial Eliminate barriers Continue with what works with fidelity!! What worked? T Honors Classes What didn't? 10th Graders tested Test Date 2012-2013 ---55% Test Date 2013-2014 ---46% Johnson 95% (2 classes) Pernell 93% (3 classes) Francis 90% (2 classes) Santos 95% (1 classes) Why Data ? What's the plan? TOGETHER WE SUCCEED....... Department Analysis! Regular Classes Student Comparative Data Gives us a purpose Make adjustments Will use assessment data to drive instructions/ time Hone in on Regular Classes PLC Detailed IFCs Common Assessments Constant Data Analysis USA TestPrep Labs/Hands on Activities/Games Relationship building/Incentives Having Regular classes Period 1 Teacher centered lectures Did not increase rigor Doing certain labs after EOC THANK YOU
Transcript: Data Science Project Purpose Imagine you are asked to settle in a different city and you just don't have enough information about the city. What will you do, how will you choose the area that you want to live in? Description The problem statement revolves around a boy named Siddhartha. Siddhartha is a national level badminton player. Siddhartha got placed in Toronto, Canada but he's worried about his badminton career. He's not aware of the city and wants know the best area in the city so that he can also focus on his badminton as well. Let's see how our project helps him out! Objectives In order to solve the problem, we came up with our project, "The Battle of Neighbourhoods". The project divides the city in clusters on the basis of their longitude & latitude and ultimately helps you to find your ideal residential area! Collect and clean the data Analyze the data Visualize the data Collect/Clean Web Scraping using BeautifulSoup and getting coordinates using geopy Foursquare API to explore neighbourhoods Creating map and filtering data Data Collection & Cleaning The first step is to collect data and clean it. We use a technique called Web Scraping to get our data. After which we create the map of the city and filter the data. At the end, Foursquare API is used to explore neighbourhoods. KMeans Clustering KMeans clustering divides the data into K non-overlapping subsets or clusters without any cluster internal structure. It is an unsupervised algorithm. Distance of samples from each other is used to shape the clusters. There are two approaches to choose the centroids. Assign each customer to the closest center. Form a distance matrix. Iterative algorithm Not necessarily the best possible outcome. Visualize Vizualization Examining Clusters At the end, we need to visualize our findings in the best possible way. With the help of folium library, we create the map and then specify each cluster with a colour Finally, we examine each and every cluster one by one and by seeing the most common venue, we suggest the best residential area to the person. Team Manmohan SIngh Abhinav Ruhela Abhijeet Saxena We are a team of three members, and each one of us believes that learning data scince is the ultimate goal while the project is just a by-product! Resources IBM Cognitive Class IBM Cognitive Labs Towards Data Science In this project, we only consider one factor i.e. latitude and longitude of a specific area, there are other factors such as connectivity of the area and income of person that could influence the location decision of a finding a residence. However, to the best knowledge of this researcher such data are not available to the neighbourhood level required by this project. Future research could devise a methodology to estimate such data to be used in the clustering algorithm to determine the preferred locations for someone to settle in a specific city. In addition, this project made use of the free Sandbox Tier Account of Foursquare API that came 45 with limitations as to the number of API calls and results returned. Future research could make use of paid account to bypass these limitations and obtain more results. Foursquare What more can be done?
Transcript: More than two-thirds of FRC clients use Spanish as their primary language (68%). While 86% of FRC clients are Latino/Hispanic, many of them use English as their primary language. copy and paste as needed to add notes to your brainstorm Outcome Measure 2001 00,000 Approximately two out of every five FRC families use CalFresh (41%), the government subsidized food program. The share of those using CalFresh in the past few years shows the growing needs of FRC families. ELEMENTS Individuals Descriptive text detailing what the outcome measured Outcome Measure Outcome Measure Outcome Measure Families Sample Family Resource Center Sample Family Resource Center is a community-based collaborative with the capacity to provide on-site access to comprehensive prevention and treatment services. Our mission is to end the cycle of child abuse by strengthening at-risk families and building safe, supportive communities. This presentation offers outcome data from select assessment tools captured in a customized database for the 2013-2014 Fiscal Year. Descriptive text detailing what the outcome measured Outcome Measure 00,000 We served: Descriptive text detailing what the outcome measured Health Insurance 00,000 Our Clients Over 17% of Orange County residents do not have health insurance coverage (2012 ACS) compared to 28% of FRC clients who are uninsured. Half of adults are uninsured and “pay out of pocket” while the vast majority of children (80%) are covered by government health insurance programs. Family Income Outcome Measure Descriptive text detailing what the outcome measured According to the 2012 American Community Survey (ACS), the median family income in Orange County was $81,653. More than 70% of all Orange County families have an annual income over $50,000. In comparison, over 50% of FRC families make less than $15,000 annually. 17% of FRC families receive CalWorks. 2002 Descriptive text detailing what the outcome measured Children Descriptive text detailing what the outcome measured Ethnicity and Language Annual Outcome Highlights Government Food Program by age Describe the chart if needed
Transcript: Who are We ? Who are We? We Are Smart Analyzers We Are Data Science Society An exclusionary & synergic effort by the students of UPES offering the trending and latest insights of the Data Science world. The unique combination of expertise, core values a... At Smart Analyzers, we have Data Scientists, Data Analysts as well as actionable tools, latest techniques, technologies,and mentors. At DSS, we have Data Scientists, Data Analysts as well as actionable tools, late... What We Do? What We Do? We offer the trending and latest insights of the Data Science world as well as provide services to help businesses grow and take informed decisions We offer the trending and latest insights of the Data Science world ... Providing solutions to businesses and creating awareness among youth by relaying exact knowledge and understanding about Data Science, Machine Learning, Big Data Analytics, etc., encompassing various sectors of the IT industry through projects, workshops, lecture sessions, training, and activities. Mission Mission To analyze critical business issues and toughest business challenges and provide smart solutions Vision Vision Our Areas Of Expertise Our Areas Of Expertise Business Analytics Business Analytics Data Mining and Warehousing Data Mining and Warehousing Big Data Big Data Business Intelligence Business Intelligence Predictive Analysis Predictive Analysis Web and Social Analytics Web and Social Analytics Sentimental Analytics Sentimental Analytics Artificial Intelligence Artificial Intelligence We Have We have Mentors Mentors DR. MANAS RANJAN PRADHAN Dean – IT, INIT, International University,Malaysia Chief Mentor/Faculty, Founder- Data Science Society DR. MANAS RANJAN PRADHAN Dean – IT, IN... DR. HITESH KUMAR SHARMA Assistant Professor, Senior Scale, UPES Faculty Head- Data Science Society DR. HITESH KUMAR SHARMA Assistant Professor, Seni... Dr. T P Singh Sir Associate Professor-UPES Faculty coordinator- Data Science Society Dr. T P Singh Industry Experts Industry Experts MR. YASH CHATURVEDI Junior BI Consultant, Goldstone Technologies Co- Founder - Smart Analyzers MR. YASH CHATURVEDI Junior BI Consulta... MR. PRATUSH SONI Trainee, KPIT Co- Founder - Smart Analyzers MR. PRATUSH SONI Trainee, KPIT Co- Found... We Organize We Organize Events Events OUTBID MY BRAIN WITH FUTURE TECH ROADIES THE COMPUTER BEING MAG Workshops Workshops R Microsoft Excel Lectures Lectures BIG DATA 101 NOSQL MONGO DB And Many More.. And Many More.. We Work as a Team in various fields We Work as a Team Technical Technical Event Management Event Management Public Relations Public Relations Graphic Designing and Photography Graphic Designing and Photography Membership Membership Sponsorship Sponsorship Editorial Editorial Visual Effects (VFX) VFX We Take Initiatives We Take Initiatives An initiative in which we bring the best online IT courses from Coursera, Udemy, Udacity, Lynda, DataCamp, EDX and much more and cover a wide range of technologies – Data Analytics, Machine Learning, IOT, AI, Web Dev, App Dev, etc. Smart Skills Smart Skills More than 50 students registered And Certified in 'Introduction to R' and 'Introduction to Python' Courses More than 50 students registered And Certified in 'Introduction to R' a... With Smart Projects, we do real-time data analysis projects under the correct guidance of mentors. Smart Project Smart Project We target businesses and provide them insightful and smart solutions to their problems using Business Analytics and Business Intelligence. Completed project – Stock Market Analysis using R We target businesses and provide them insightful and smart solutions to the... With this Initiative, we handles profiles on a wide range of social media platforms where we post contents for our audience to spread knowledge and create awareness about the world of Data Science,latest news and its application. Social Media Handle Social Media Handle A series of facts about Data Science, application of Data Science in BFSI, ERA, IOT, Life Sciences, Healthcare, Sports, Telecom, Manufacturing, etc. and the business world. Smart Facts Smart Facts We post articles/blogs which talk much about the latest trends and technologies, demystifies Data Science and its key concepts and provides answers to doubts and queries of many thinkers. Articles Articles We post short informative videos which answer the buzz words like IOT, IBM Watson, Statistics, Predictive Analytics, etc. Videos Videos We Believe We Believe Learning by doing is the best way of gaining knowledge and that’s what we, at Smart Analyzers are living up to. Learning by doing is the best way of gaining knowledge and that’s what we, ... Lakshya Gupta: 9911966636 Register Today! Register Today! Contact:
Transcript: Data Scientist Interview What I Learned The Basics Dr. Singh started out at The Fed Economic Research Data Cleaning Linear and Logistic Regression Went back to school and got her doctorate in data science Now works at google as a data scientist with the YouTube Comments team The Interview Interview Process 1. Screening Call with an HR Rep. to determine the best role for you (could be different than what you applied for) 2. Technical Interview to cover your general statistical and coding knowledge (there are 4 in total) 3. Behavioral interview to asses the parts of you that don't have to do with your technical ability 4. Then everything you've done so far goes through multiple committees to make sure everything's up to snuff 5. You then get to interview other hiring managers to figure out what team you want to work with Dr. Singh said the whole process took 8 week Day To Day Day to Day For Dr. Singh her job is unique in a way that her department is full of data scientists but they all work with different product segments She works primarily with the YouTube comments team where she works closely with the software engineers and product managers Her day to day mainly contains meetings with the software engineers and product managers of the YouTube comments team to determine what they need while the rest is spent coding She best describes it as being an internal consultant who does what ever they need done, small, medium, and large, when it comes to data science Languages and Technologies Commonly Used Technologies It mainly depends on what team shes working with SQL, R, Python with a little bit of C++ are all used. Data scientists don't need to know all of these but at least at google SQL is needed because its the primary way they interact their databases Its hard to get as much support for R because only 50 of the 3000 people working with YouTube are data scientists. Dr. Singh says she has a preference for R for data analysis but is trying to move towards python for the reason stated above. Knew most of the technologies going in (had to touch up on C++ and SQL) but Google has dedicated time every quarter for learning on the Job Favorite Project Favorite Project? One of her first projects The comments team had a metric they wanted to use but the data for it was extremely noisy Dr. Singh developed a method that improved the usable data by 300%. She was able to fully implement her work in a way that it was essentially plug and play for the comments team to use. It required no changes to their current codebase. The reason she enjoyed it so much was the fact that she got to use a bunch of different languages, learned a bunch of new stuff, and in the end got to wrap it up in a nice neat package that was actually useful. Current and Future Projects Recent Projects A survey after you watch a video that lets you pick between and rate 2 different comments as to which one is more relevant. Researching the correlation between Engagement signals and user satisfaction Currently working on how to detect brigaiding and notify the creator as its happening so they can disable comments and protect themselves. This involves determining what metrics are indicative of brigaiding (dislikes, mass comments, etc) Random Information Fun Facts and Random Information Her office has a slide in it to get down stairs Google hosts YTF (YouTube Fridays) where you can talk with coworkers outside of a work environment as well as talk with the YouTube CEO in a relatively relaxed environment. She also mentioned there is awesome live music. She's only had to work outside of work once and her boss was mad that it even happened at all. She said while its extremely uncommon for data science stuff to be deadline focused but it might be a little different for software engineers. "When getting your PHD you learn how to learn" Took a shotgun approach to applying for jobs (Chase Bank, Post Doc at Yale, Social Policy Think Tank, Lawrence Liver more National Lab, and many more) Advice for younger self "Stop trying to plan so much, just let go a bit, everything will be okay".
Transcript: PLAN Data Scientist 2019 Sean F. Larsen Data Science Plan Creation Plan Creation Education Programming Skills Course Projects Tasks Education Education Statistics Statistics Basics Statistics Statistical Methods Regression Analysis Probability and Distribution Theory Statistical Regression and Inference Analysis Analysis Predictive Analysis Inferential Analysis Causal Analysis Mechanistic Analysis Decision Making Data Skills Data Skills Subject Selection Data Mining Data Cleaning Data Exploration Data Presentation Data Presentation Data Visualization Story Telling With Data Programming Programming Courses Courses Projects Projects 5 Types of Data Science Projects 5 Types Importing data Joining multiple datasets Detecting missing values Detecting anomalies Imputing for missing values Data quality assurance Exploratory Data Analysis Exploratory Data Analysis Ability to formulate relevant questions for investigation Identifying trends Identifying correlation between variables Communicating results effectively using visualizations Interactive Data Visualization Interactive Visualization Including metrics relevant to your customer’s needs Creating useful features A logical layout (“F-pattern” for easy scanning) Creating an optimum refresh rate Generating reports or other automated actions Reason why you chose to use a specific machine learning model Splitting data into training/test sets Selecting the right evaluation metrics Feature engineering and selection Hyperparameter tuning Machine Learning Machine Learning Know your intended audience Present relevant visualizations Don’t crowd your slides with too much information Make sure your presentation flows well Tie results to a business impact Communication Communications First 5 Projects First 5 Continued Work Continued Work Tasks Tasks Data Science Resume Data Science LinkedIn Profile Apply for Jobs Start a Blog Update My GitHub Account Update My Pinterest Account
Transcript: Data Science In another words ... The what, who, why and how of: Presenting: Association Analysis Example. What is Data Science? The Why? The, Who? Jorge Gamboa The, What? The How? "Data Scientist: The Sexiest Job of the 21st Century" (Harvard Business Review)
Transcript: Southeast Airlines Nathan Wendel Sharvil Turbadkar Yaksh Suresh Shobhavat Ashutosh Jha Priyank Jethva Overview Company Overview - Top-four airline in the U.S. -Bucketing the customers into Detractors and Promoters to effectively focus on causes for detraction -Analyzing the Net Promoter Score - Strong emphasis on loyalty Problems - Customer churn is difficult to calculate because it is a "lagging indicator" -Many factors have led to customer churn for which the airlines is in debt Industry Issues and Net Promoter Score NPS (Net Promoter Score) - "How likely is it that you will recommend our airline to a friend or colleague?" - Asks customers to respond on a 1-10 basis: 1-6 = Detractors, 7-8 = Passive, 8-10 = Promoters - Customers who are promoters are good customers to keep - Allows company to examine responses by specific attributes (age, gender, etc.) Net Promoter Score Percentage of each to recommend We can conclude that -Cheapset airline has most number of customers -Flyfast Airways has customers with most promoters NPS by different Airlines Analysis Analysis - Data analysis using a total of 36 attributes: - 28 attributes provided, one removed - 9 added attributes - Total of 10,282 data points Modeling Techniques Modeling Techniques - Support Vector Machines - Association Rule Mining -Random Forest Support Vector Machine Support Vector Machine Factors taken into consideration for explaining effect on Likelihood to recommend Factors -Airline Status -Flight Canceled -Price Sensitivity -Arrival Delay in Minutes -Departure Delay in Minutes -Customer Type -Loyalty -Age Buckets -Class -Type of Travel -Flights per year 1292 Promoters have been predicted correctly by our SVM Model 708 Detractors have been correctly predicted by our SVM Model 134 Detractors have been falsely predicted to be Promoters 436 Promoters have been falsely predicted to be Detractors Confusion Matrix Association Rules Association Rules- Detractors Association Rules Association Rules- Promoters Attributes Random Forest Age, Price sensitivity, Loyalty, Class Departure delay in minutes Arrival delay in minutes Likelihood to recommend Airline status Type of travel Eating and Drinking at Airport Output Correlation Matrix Correlation Matrix Detractors Descriptive Statistics Top Parameters (not in order) for detractors: No shopping at airport Not loyal Flight cancellations ("Flights Canceled") Airline Status is blue Ages below 23 and above 61 Frequent flyer amount = 0 Class = Economic Top Parameters (not in order) for promoters: Shopping at airport Loyal Flights not cancelled Airline Status is silver Ages between 23 and 61 Frequent flyer amount = 1 Class = Business Promoters Promoters Top Parameters (not in order) for promoters: Shopping at airport Loyal Flights not cancelled Airline Status is silver Ages between 23 and 61 Frequent flyer amount = 1 Class = Business Title Frequency of Attributes Loyalty Loyalty Flight Cancellations Flight Cancellations Airline Status Airline Status Age Age Price Sensitivity Airline Status by Gender Adults are more Likely to recommend the Airlines Distribution of scores by Age Recommendations Recommendations -Target Business Travelers -The Southeast Airlines should target adult passengers between the ages of 23-60. -They should be targeting business travelers. -The insights indicate that people get detracted when there is a delay in arrival or departure. -Female passengers belonging to the Silver Airline status should be targeted. -Insights indicate that customers get detracted when the flight is canceled. -Southeast Airlines should strive to keep their prices less as to not detract the passengers. Actionable Insights -Older people Customers aged from 60-85 tend average a lower satisfaction. This could be resolved by providing more personal travel amenities for senior customers and taking additional care. For instance, Southeast airlines could provide wheelchairs at the airport. -Female We can observe that all female related components appear to be in dissatisfaction. We suggest that the Southeast airlines should provide more feminine care to improve the satisfaction level. Additionally, they can also provide more help to the mothers who carry their babies. For instance, we can provide a separate area for them. -Personal Travel It is observed that the people who are traveling on their own, tend to give a lower satisfaction rating. Therefore, in order to improve the user experience of personal travel customers, we suggest that a survey be conducted based on these customers. Further analysis on the survey results can help Southeast to come up with a specific plan to increase their satisfaction level. - Blue status Passengers in the blue status are the most yet they tend to give low satisfaction. One reason for this could be that they do not get the service which they had expected. The Southeast airlines definitely need to change some policies for blue status customers and improve their service towards their clients
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