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Lawrence Dignan wrote a new post on the site MIS 0855: Data Science Spring 2018 6 years, 6 months ago
Here is the exercise.
And here is the spreadsheet you’ll need [In-Class Exercise 2.1 – 2015 Car Fuel Econ [Start]]
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Lawrence Dignan wrote a new post on the site MIS 0855: Data Science Spring 2018 6 years, 6 months ago
Some quick instructions:
You must complete the quiz by the start of class on January 29, 2018.
When you click on the link, you may see a Google sign in screen. Use your AccessNet ID and […]
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Lawrence Dignan wrote a new post on the site MIS 0855: Data Science Spring 2018 6 years, 6 months ago
The reading list for the week are the following:
The Ashley Madison Hack Is Only the Beginning
NSA phone calls
Spying on yourself
In search of America’s Best Burrito
Open Data
What the Fox knows -
Lawrence Dignan wrote a new post on the site MIS 0855: Data Science Spring 2018 6 years, 6 months ago
Leave your response as a comment on this post by the beginning of class on Jan. 29, 2017. Remember, it only needs to be three or four sentences. For these weekly questions, I’m mainly interested in your o […]
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In the news today, there is always headlines about immigrants committing major crimes. It is true that some immigrants commit crime, but do immigrants actually commit more crimes than the average American citizen? If I wanted to discover this answer I would collect data from a large diverse pool of Americans and an equally large diverse pool of immigrants. I would place the two groups side by side and compare the statistics of each groups number of incarcerations and the number of crimes each has committed.
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A piece of conventional wisdom I’ve heard is if you hold your breath when you have the hiccups, it’ll make them go away. In order to test this theory you would need to collect data on how often this method actually works. To do this you would need to record several people on how long it takes to get rid of them with this method, if it works at all.
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I have heard that people should avoid eating oysters and other shellfish in months that do not have the letter “r” in them. The “R” rule has been conventional wisdom due “red tides” that are most prevalent during the months of May through August. Red tides are when large blooms of algae grow along the coast and spread toxins that shellfish can absorb. The rule usually meant that places that have warm water temperatures were most vulnerable to having their shellfish filled with toxins. Most of the seafood sold in US supermarkets is commercially farmed which means one’s odds of being affected by harmful toxins is decreased. Modern farming methods regulate algae concentration and shellfish are monitored for toxins before being sold.
There are many variables at play when testing out this conventional wisdom. Two of the most telling variables I could use to answer debunk this conventional wisdom could look at US consumption of farmed versus wild shellfish, and reported cases of paralytic shellfish poisoning (PSP) in the US during “R” months versus reported cases between May and August and from there look at whether the toxic shellfish was purchased from a supermarket, fishmonger, or other source.
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A piece of conventional wisdom is the saying “you get what you paid for” when making purchases. Usually, this statement is said when you are displeased with an item that you did not pay much for. To test this, I would purchase the same item at various price points to compare using 10-15 people. This test should be conducted as a blind test, meaning that the person who is examining the item does not know the price. Using a Likert Scale, the blind testers would place value on the product.
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An example of conventional wisdom would be “Money can’t buy happiness.” To test this, I would collect data on individual’s incomes, spending habits, and overall happiness. Some ways to evaluate someone’s happiness could be surveys and enrollment in mental health services.
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I have heard that if people stay or walk in the rain then they will catch a cold. We can gather about 10 people that are interested in this experiment and let them walk in the rain and observe them afterward to record the rate of people catching a cold. We can also send out a survey to students and ask them did they catch a cold after they walk in the rain. We then compile the data to see the rate of people catching a cold.
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A piece of conventional wisdom that I have heard of is ” you have to go to college to be successful in life”. To analyze this we can take a group of a large amount of people and divide them to two groups into those who graduated college and those who have not. Then we can ask these two groups of their overall success in life in terms of money, family, and happiness. Once the data is collected we can compare it and see if college really does mean a more successful life.
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Conventional wisdom is a very common thing, which is a statement that is accepted by people, even though there are no tests proving or disproving it. An example of this would be “you will get sick if you go outside in the cold without a jacket.” The data I would collect to test this statement would be getting a group of healthy people and having some go throughout their day without wearing a coat, and some wearing coats. They would have to do the same tasks and stay isolated from other people with colds. I would then record the data from the different individuals within the group and either prove or disprove this conventional wisdom.
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An example of conventional wisdom that I have heard is “sleep with a spoon under your pillow for good luck when there is a chance for a snow day”. This can be tested by having people try this day-to-day. Have an experiment done to see what the statistics could be and also a survey. This will prove to be wrong because of the random times that it might work.
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A piece of conventional wisdom that I can think of off the top of my head is that people say you should not eat right before you workout or you will get sick. In order to test this one could collect data from people who eat before they partake in some sort of exercise. They could measure the amount of time in between eating and working out to see if this shows that their needs to be a certain amount of time in between in order to digest properly. This theory could be proven not true if individuals do not get sick even if they ate before hand. Results could also vary depending on the individual because certain people may be able to digest food quicker than others or it may just not effect them in the same way.
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Conventional wisdom may be used in many different accounts in our lives. The term also reflects on simple logic and reasoning used through evidence or data. The commonly known phrase, “don’t let the bed bugs bite” is a great piece of conventional wisdom because one can research data that explains the amount of bed bugs or termites that are on the average person’s bed or sheets. This study should be demonstrated through analyzing the statistics of how many bed bugs may be actually biting us without most of us knowing at all.
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Conventional wisdom is defined in the Merriam-Webster Dictionary as “the generally accepted belief, opinion, judgment, or prediction about a particular matter” This term can relate to practically any field of knowledge. There are countless examples of widely held views on particular matters. One example that pertains to the health/fitness field is that being at a caloric deficit will help you lose fat. A simple way to test this is to gather a group of 20 random individuals and have them record their weight and log their caloric intake every day for 5 weeks. They will be instructed to be at a caloric deficit for that period of time. After 5 weeks I should be able to prove that the individuals who successfully at a caloric deficit will have experienced a loss of body fat.
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“Troubles of children are always the parent’s fault” is an example I could think of related to conventional wisdom. By collecting data of the parents of successful people then comparing to the parents of people that have made the wrong choices in life we could probably see if this statement is correct. Usually, for me, if you give a child fast food and do not manage how much you give them that food, that child would most likely become obese.
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The belief that people who spend more time watching TV and using other electronics each day are considered lazy could be considered a theory of conventional wisdom. This theory could be tested by collecting the reasons people use their electronics through a survey (some people watch television or browse social media for work, others use it for school or educational purposes), recording what people are watching and whether or not what is being watched dictates the degree of perceived laziness. (Does watching National Geographic documentary marathons make someone less lazy than another who spends more time watching The Bachelor re-runs?) The main challenge would be creating an clear-cut way to determine the degree of laziness; but, this could be resolved with the use of a “lazy scale” of 1 to 5.
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Lawrence Dignan wrote a new post on the site MIS 0855: Data Science Spring 2018 6 years, 7 months ago
Here is the exercise
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Lawrence Dignan wrote a new post on the site MIS 0855: Data Science Spring 2018 6 years, 7 months ago
Here is the exercise
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Lawrence Dignan wrote a new post on the site MIS 0855: Data Science Spring 2018 6 years, 7 months ago
Programming note about the Monday night classes. We will be covering two sections in the classes. As a result you should be familiar with the reading for 1.1 and 1.2 ahead of the first class Jan. 22. The reading […]
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Lawrence Dignan wrote a new post on the site MIS 0855: Data Science Spring 2018 6 years, 7 months ago
This course uses Tableau a good bit and this year you’ll need your own individual license key (we used bulk keys before but typically would run out or run into issues). You can get a full copy of the Tableau […]
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Lawrence Dignan wrote a new post on the site MIS 0855: Data Science Spring 2018 6 years, 7 months ago
Here is the syllabus for the course.
You should read the syllabus carefully. Everything you need to know is in this document.
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Lawrence Dignan wrote a new post on the site MIS 0855: Data Science Spring 2018 6 years, 7 months ago
We are all drowning in data, and so is your future employer. Data pours in from sources as diverse as social media, customer loyalty programs, weather stations, smartphones, and credit card purchases. How can you […]
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Lawrence Dignan wrote a new post on the site MIS 0855: Data Science Spring 2017 7 years, 3 months ago
Here is the study guide for the third (final) exam.
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Lawrence Dignan wrote a new post on the site MIS 0855: Data Science Spring 2017 7 years, 3 months ago
Yes, data scientist is the hot career of the moment, but when someone asked on Quora what the downsides were the answers were pretty telling. Here’s a look at what data scientists had to say.
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Lawrence Dignan wrote a new post on the site MIS 0855: Data Science Spring 2017 7 years, 3 months ago
Just a reminder that your final exam will be on Monday, May 8 at 5:30pm in the same room as class. Please be on time. Students will not be permitted to enter late. Please make sure that all missing a […]
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Lawrence Dignan wrote a new post on the site MIS 0855: Data Science Spring 2017 7 years, 3 months ago
Here are some additional links for the people analytics chat we’ll have in class. Beyond this week’s reading it’s worth checking out how analytics will be used for HR via these links. It’s not required reading, […]
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Lawrence Dignan wrote a new post on the site MIS 0855: Data Science Spring 2017 7 years, 3 months ago
I found the reading this week–beyond the people analytics one–to be a bit lacking on what we’re talking about on Monday. So I aggregated some items that better define descriptive, prescriptive, and […]
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Lawrence Dignan posted a new activity comment 7 years, 3 months ago
Advertising is becoming data science. Between the programmatic and algorithm driven ads to the direct response, advertising execs need to know the data more than the creative to survive. The creative still wins, but the metrics are what keep you employed. In advertising I’d recommend researching Tradedesk, which just went public. Tradedesk has the…[Read more]
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Lawrence Dignan wrote a new post on the site MIS 0855: Data Science Spring 2017 7 years, 4 months ago
Here is the link for the driver download
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Lawrence Dignan wrote a new post on the site MIS 0855: Data Science Spring 2017 7 years, 4 months ago
Here is the exercise.
And here is the spreadsheet you’ll need [In-Class Exercise 13.2 – VandelayOrdersAll.xlsx].
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Lawrence Dignan wrote a new post on the site MIS 0855: Data Science Spring 2017 7 years, 4 months ago
Leave your response to the question below as a comment on this post by the beginning of class on April 24, 2017. It only needs to be three or four sentences.
What was the most important takeaway (from y […]
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The most important takeaway from this course, in my perspective, is simply the fact that data can be applicable to almost any situation. This is true in two ways: 1) almost everything done or doable can have data to be extracted from it, and 2) when data is extracted, its applicability and usage is essentially infinite. Data can be found everywhere, and data analysis can be used for anything. I would tell a future MIS 0855 scholar that this course is about what data is, the various kinds of data, how companies, organizations, and governments use data, and the various flaws/shortcomings of big data.
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The most important takeaway from this course is that not only is data anywhere, but everyone can benefit from using it and understanding it. This course teaches students where to find data, the benefits of data, how to explain it to others, and how to apply it to everyday life. Using software like tableau and excel helps students understand where the data is coming from, learn how to manipulate it, analyze it, and use it productively.
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A data-driven service that I use every-day would be my iPhone’s music app. When organizing the applications aspects in a spreadsheet, the rows would possess the song title in which would be to correlate with the columns. Each column would have a unique data target, those would include, Artist name, Album title, the release date, the length of the song, as well as the genre of music in which the song is native to.
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The most important takeaway I have from this class will be the in-class assignments we did on sentiment analysis. While I have taken classes on data analytics before and have used these skills in various projects and internships, I had never before heard of or worked with sentiment analysis. It was particularly cool to analyze Twitter data. I have logged the files we used to complete these analyses so that I can use them later on in the real world. To future students, I would say this class is about learning about real tools you can use in various work environments. It’s not a class where you learn about vocabulary and processes and such. It’s a class where you really learn how to analyze and present data… and this is extremely beneficial in future work applications considering how much we rely on data nowadays.
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I believe the most important takeaway from this course was that data can be applicable to any major, not just MIS. I think this course is beneficial for any major because you also get to learn and use a new software. I would explain to a future MIS0885 scholar that this course will give you a better understanding of data and the various types and specifics of data.
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I just changed my major from visual studies to advertising and data science was a Gen. Ed. I chose to take for my graduation requirements. This semester, I took core advertising classes and learned about the field of advertising and how each role contributes to the final product. When I first began this data science course, I did not quite understand how it related to my major. I knew data was important in advertising, but I was not clear of what this course would teach me. After almost completing this course, I now understand that data is everywhere and can be applied to almost any situation. Specifically when dealing with my major, data can be used to learn and understand more about a product, service, or even a person to be able to market and sell more products.
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Advertising is becoming data science. Between the programmatic and algorithm driven ads to the direct response, advertising execs need to know the data more than the creative to survive. The creative still wins, but the metrics are what keep you employed. In advertising I’d recommend researching Tradedesk, which just went public. Tradedesk has the data game down better than anyone else for now.
It’s probably a nice place to think about working down the line.
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The most important takeaway from this course is that data is available, accessible, but also can be deceiving. Data is everywhere, and everything technology related is heavily correlated with data. The government provides an extensive amount of data, and companies themselves are beginning to employ individuals in managing their own forms of data. But, with this heavy data use comes problems. Data can be used to say just about anything, and that answer may not even be a relevant one. Though data is used in a positive way, and is becoming more prevalent in today’s society and businesses, we also have to be careful in how we use that data.
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The most important takeaway from this course would be that data comes in many forms (good and bad) and that it can be found nearly anywhere. Data science is a course where students learn the difference between good and bad data sets, how to integrate the data set into some sort of graph to make it simple for the viewers, and to combine all the graphs to make an infographic to display all the findings. These skills are useful and can be applied in our daily lives.
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The most important takeaway from this class would have to be the different forms of data manipulation using software programs such as Tableau and Excel. Excel data analysis is imperative for anyone pursuing a market research/data analyst position. Data science embodies all of the processes necessary with bring analysis to fruition using data of all sorts.
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The most important takeaway from this course is how to analyse data and visualize data in an effective way. Data science plays a more and more important role in today’s society, so it is necessary for everyone, including who are not data science major, to learn how to use data to tell good stories, and make it simple and clear for the audiences to understand the data presented. Data science can be applied to many different fields, so we ought to learn something about how to deal with data.
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For me the most important takeaway is that I realize the important impact of data analysis. People can use past data to predict future and make decision. We are able to have a scientific and rational way to solve many problems. This course also taught us how to use useful tools to analyze data, like Tableau and Excel, which has wide application in the future and help us work efficiently.
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The most important take away from this course is that data is everywhere and how important data to our lives now. Raw data does not tell or mean anything, but once it turns into information, it can give a lot of benefits. This is course for me is to realize data is everywhere, how important it is and how to analyze it to apply it to everyday life.
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In my perspective, the most important takeaway from this class is how important data can be in all different situations and that data is everywhere. As a finance major, I realized that I will be dealing with data for the rest of my life. Tableau was also a very important tool that I have never used before this class, but think it will be useful in the future for me.
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My most important takeaway from this course was how to use tableau to analyze data sets and visually represent my findings. I feel as though I will be able to better work with data for future projects/courses. I also have a greater appreciation for gathering data to create data sets and transforming it so ti is standardized.
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The most important takeaway from this class was the introduction to the importance and impact of data. Not everyone may use tableau moving forward in their careers, but learning how impactful data can be, how to spot bad data, or understanding the relationships between data and users will be relevant in everyone’s careers. I would tell someone looking to take this class that you actually get a lot of useful information from this gen ed.
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The most important takeaway from this class in my opinion is how important data aggregation and analytics has become to big companies. Companies are spending millions of dollars to better understand their customers and employees through data analytics. I would say this course is about understanding the different forms of data, where it comes from, and how people today are using data to change the world
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The most important takeaway from this course is that data is everywhere. In addition to needing to understand how to manipulate and analyze data in any type of career, it is equally important to understand how to spot bad data visualizations and the like in our everyday lives when we look at social media and television. It is often important, both in work and our personal lives, to dig deeper into important issues instead of accepting the possible incorrect data that is thrown at us. The key is to be questionable, and take the time to find the truth.
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As a business major, I would say the most important takeaway from this class was learning to properly use tools such as Tableau and, most importantly, Excel. Excel is a tool that essentially everyone in the field of business will be forced to use at some point, and tricks like cleaning data can be applied to a wide spectrum of sheets and tasks. Excel proficiency is a great addition to a resume, and some jobs even require it.
I would tell a future MIS0855 student that this class is focused on learning about the importance of data, how we come across data every day, how we can improve it, and how we can properly use it. -
The most important takeaway from this class is how to present the data, and identifying good/bad data. Management of data is not as easy as it sounds, after this class I learned a lot with managing data in many ways and which is the best to present it. Through this course, I can identify bad data and visualize it. I also feel that I am more competent to be in a job which requires presentation and analyzing hypnosis through data.
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I would say that data is everywhere all the time. No matter what field of work you end up going into, data will be involved somehow. In this course you learn about data and different types of data. Through this we learned to interpret it and do things like are charts to read it better. Overall this course a great course to help you understand how our world works.
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I would say the most important takeaway from this course is definitely knowing how to apply data and learning that data is absolutely everywhere. We learned different ways to read data and the different types of data there are.
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The most important take way from this course is that data is everywhere, and it is important to take it into account when working on projects. It is also important to assure that data is credible and is captured appropriately. It is also important to always ask questions about the data you come across and see what else you can find out.
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Lawrence Dignan wrote a new post on the site MIS 0855: Data Science Spring 2017 7 years, 4 months ago
Some quick instructions:
You must complete the quiz by the start of class on April 24, 2017.
When you click on the link, you may see a Google sign in screen. Use your AccessNet ID and password to […] - Load More