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This presentation gives analysis of the article "A Predictive Analytics Primer" by Thomas H. Davenport Slide 1: A Predictive Analytics Primer by Thomas H. Davenport Slide 2: Thomas H. Davenport Slide 3: Powers of Predictive analytics Slide 4: Predictive analytics refers to predicting future from the data of the past. Slide 5: The quantitative analysis isn’t magic—but it is normally done with a lot of past data, a little statistical wizardry, and some important assumptions. Slide 6: The Data: Lack of good data is the most common barrier to organizations seeking to employ predictive analytics. Slide 7: The Statistics: Regression analysis in its various forms is the primary tool that organizations use for predictive analytics. Slide 8: An analyst hypothesizes that a set of independent variables (say, gender, income, visits to a website) are statistically correlated with the purchase of a product for a sample of customers. The analyst performs a regression analysis to see just how correlated each variable is; this usually requires some iteration to find the right combination of variables and the best model. Slide 9: The Assumptions: That brings us to the other key factor in any predictive model—the assumptions that underlie it. Every model has them, and it’s important to know what they are and monitor whether they are still true. The big assumption in predictive analytics is that the future will continue to be like the past. Slide 10: What can make assumptions invalid? Slide 11: The most common reason is time. If your model was created several years ago, it may no longer accurately predict current behavior. The greater the elapsed time, the more likely customer behavior has changed. Slide 12: Another reason a predictive model’s assumptions may no longer be valid is if the analyst didn’t include a key variable in the model, and that variable has changed substantially over time. Slide 13: Managers should always ask analysts what the key assumptions are, and what would have to happen for them to no longer be valid. And both managers and analysts should continually monitor the world to see if key factors involved in assumptions might have changed over time. Slide 14: With these fundamentals in mind, here are a few good questions to ask your analysts: Can you tell me something about the source of data you used in your analysis? Are you sure the sample data are representative of the population? Are there any outliers in your data distribution? How did they affect the results? What assumptions are behind your analysis? Are there any conditions that would make your assumptions invalid? Slide 15: Thank You!
Analysis of the article "A Predictive Analytics Primer" by Thomas H. Davenport
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The presentation is based on analysis of the ted talk by Jer Thorp on 'Make Data more Human.' Slide 1: Introduction to the topic Slide 2: Jer Thorp makes one of the most beautiful data visualizations in the world. Slide 3: He designed the naming algorithm for the 09/11 Memorial, Ney York city, wherein all are connected near to the person they were connected with. Slide 4: He has insisted on giving data human traits, and treat them like one.Following methods: Slide 5: Data is bigger, doesn't mean its better. It may have lots of errors, redundancy, irrelevancy. Slide 6: We need to give data a human-like context. Slide 7: Data can be related to the most human of the attribute, Emotion. Slide 8: Visualization of data solves the majority of the problems. Slide 9: Jer Thorp co-founded icascade, a start-up meant to construct a detailed picture of how information propagated through the social media space. Slide 10: Visualization helps plot huge amounts of complex data, clarifying all the factors. Slide 11: Benefits of Data treatment Slide 12: Absorb info. in new and more constructive ways. Visualize relationships and patterns between operational and business activities. Identify and act on emerging trends. Manipulate and interact directly with data. Foster a new business language. Slide 13: Have a storytelling data. Slide 14: openpaths.cc a site to donate your data for research purposes. Slide 15: Thank You
Analysis of the ted talk by Jer Thorp on 'Make Data more Human.
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This presentation does analysis of the ted talk, "3 ways to spot a bad statistics" by Mona Chabali. It goes through key insights mentioned by her during her talk.
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This presentation gives analysis of the article "A Predictive Analytics Primer" by Thomas H. Davenport Slide 1: A Predictive Analytics Primer by Thomas H. Davenport Slide 2: Thomas H. Davenport Slide 3: Powers of Predictive analytics Slide 4: Predictive analytics refers to predicting future from the data of the past. Slide 5: The quantitative analysis isn’t magic—but it is normally done with a lot of past data, a little statistical wizardry, and some important assumptions. Slide 6: The Data: Lack of good data is the most common barrier to organizations seeking to employ predictive analytics. Slide 7: The Statistics: Regression analysis in its various forms is the primary tool that organizations use for predictive analytics. Slide 8: An analyst hypothesizes that a set of independent variables (say, gender, income, visits to a website) are statistically correlated with the purchase of a product for a sample of customers. The analyst performs a regression analysis to see just how correlated each variable is; this usually requires some iteration to find the right combination of variables and the best model. Slide 9: The Assumptions: That brings us to the other key factor in any predictive model—the assumptions that underlie it. Every model has them, and it’s important to know what they are and monitor whether they are still true. The big assumption in predictive analytics is that the future will continue to be like the past. Slide 10: What can make assumptions invalid? Slide 11: The most common reason is time. If your model was created several years ago, it may no longer accurately predict current behavior. The greater the elapsed time, the more likely customer behavior has changed. Slide 12: Another reason a predictive model’s assumptions may no longer be valid is if the analyst didn’t include a key variable in the model, and that variable has changed substantially over time. Slide 13: Managers should always ask analysts what the key assumptions are, and what would have to happen for them to no longer be valid. And both managers and analysts should continually monitor the world to see if key factors involved in assumptions might have changed over time. Slide 14: With these fundamentals in mind, here are a few good questions to ask your analysts: Can you tell me something about the source of data you used in your analysis? Are you sure the sample data are representative of the population? Are there any outliers in your data distribution? How did they affect the results? What assumptions are behind your analysis? Are there any conditions that would make your assumptions invalid? Slide 15: Thank You!
Analysis of the article "A Predictive Analytics Primer" by Thomas H. Davenport
Analysis of the article "A Predictive Analytics Primer" by Thomas H. Davenport
Vaibhav Srivastav
This presentation gives an analysis of a famous Ted talk by Hans Rosling, "The Best Stats you've ever see."
Analysis of, "The Best Stats you've ever seen."
Analysis of, "The Best Stats you've ever seen."
Vaibhav Srivastav
Slide 1: Beauty of Data Visualization Intro Slide 2: The Billion Dollar O-gram Slide 3: World's Fear Landscape overtime in media Slide 4: Explanation of landscape. Slide 5: Break-up times, as per Facebook status updates. Slide 6: Attack probability data of countries Slide 7: "Let dataset, change your mindset." -Hans Rosling Slide 8: Language of the eye (Pattern) and language of the brain (learn) creates beautiful visualizations. Slide 9: How could managers use data visualization? Slide 10: Data Visualization can compile a huge number of databases into few pages. Slide 11: It will help giving a visual aspect to your data. Slide 12: It will help in discovering new facts. Slide 13: Beautiful, Lovely data. Slide 14: Thank You.
Beauty of Data Visualization
Beauty of Data Visualization
Vaibhav Srivastav
The presentation is based on analysis of the ted talk by Jer Thorp on 'Make Data more Human.' Slide 1: Introduction to the topic Slide 2: Jer Thorp makes one of the most beautiful data visualizations in the world. Slide 3: He designed the naming algorithm for the 09/11 Memorial, Ney York city, wherein all are connected near to the person they were connected with. Slide 4: He has insisted on giving data human traits, and treat them like one.Following methods: Slide 5: Data is bigger, doesn't mean its better. It may have lots of errors, redundancy, irrelevancy. Slide 6: We need to give data a human-like context. Slide 7: Data can be related to the most human of the attribute, Emotion. Slide 8: Visualization of data solves the majority of the problems. Slide 9: Jer Thorp co-founded icascade, a start-up meant to construct a detailed picture of how information propagated through the social media space. Slide 10: Visualization helps plot huge amounts of complex data, clarifying all the factors. Slide 11: Benefits of Data treatment Slide 12: Absorb info. in new and more constructive ways. Visualize relationships and patterns between operational and business activities. Identify and act on emerging trends. Manipulate and interact directly with data. Foster a new business language. Slide 13: Have a storytelling data. Slide 14: openpaths.cc a site to donate your data for research purposes. Slide 15: Thank You
Analysis of the ted talk by Jer Thorp on 'Make Data more Human.
Analysis of the ted talk by Jer Thorp on 'Make Data more Human.
Vaibhav Srivastav
Slide 1: Welcome slide on analysis of the article by Thoman C Redman on 'How to start thinking like a Data Scientist?' Slide 2: Why, Why do we need to think like Data Scientists? Slide 3: Because Data are forcing their way into all the industries. Data is the new currency. Slide 4: Procedure to think like a Data Scientist Slide 5: Step 1- Define the problem statement to be solved. Slide 6: Step 2- Think about all the data that can solve your problem. Slide 7: Step 3- Collect your data using necessary functions and protocols. Slide 8: Clean your data for missing and irregular files. Slide 9: Have confidence in the efficiency of your data. Slide 10: Be wise to your data, Don't get too hard on it. Slide 11: Visualize your data, Plot the graphs. Slide 12: Do data analysis. Slide 13: Check for variations in the data. Slide 14: Formation of hypothesis based on observation from data. Slide 15: Test your hypothesis on real-valued function. Slide 16: Communicate the results of the evaluation. Slide 17: Don't be data illiterate. Slide 18: Thank You!
Analysis of the article by Thoman C Redman on 'How to start thinking like a D...
Analysis of the article by Thoman C Redman on 'How to start thinking like a D...
Vaibhav Srivastav
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You may not need big data after all
You may not need big data after all
Analysis of ted talk, "Lies, Damned Lies and Statistics" by Sebastian Wernicke
Analysis of ted talk, "Lies, Damned Lies and Statistics" by Sebastian Wernicke
How to use data to make a hit TV show
How to use data to make a hit TV show
Stop Searching for That Elusive Data Scientist
Stop Searching for That Elusive Data Scientist
The big-data revolution in healthcare
The big-data revolution in healthcare
Analysis of ted talk, "3 ways to spot a bad statistics" by Mona Chabali
Analysis of ted talk, "3 ways to spot a bad statistics" by Mona Chabali
Analysis of the article "A Predictive Analytics Primer" by Thomas H. Davenport
Analysis of the article "A Predictive Analytics Primer" by Thomas H. Davenport
Analysis of, "The Best Stats you've ever seen."
Analysis of, "The Best Stats you've ever seen."
Beauty of Data Visualization
Beauty of Data Visualization
Analysis of the ted talk by Jer Thorp on 'Make Data more Human.
Analysis of the ted talk by Jer Thorp on 'Make Data more Human.
Analysis of the article by Thoman C Redman on 'How to start thinking like a D...
Analysis of the article by Thoman C Redman on 'How to start thinking like a D...
What do we do with all this Big Data?
What do we do with all this Big Data?
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5.
D.J. Patil Coined the
term Data Scientists
6.
Who is a
Data Scientist?
7.
Data Scientist
8.
9.
How will Data
Scientists benefit us?
10.
11.
Managers need certain answers for finding the right Data scientists!
12.
what they do in businesses?
13.
What skills do they need ?
14.
what fields are those skills most readily found in?
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