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Amy Lavin wrote a new post on the site MIS2502 Spring 2016 8 years, 8 months ago
MySQL Workbench Instructions
We’ll be using MySQL Workbench to create and execute SQL queries. That will start the week of February 2, 2016.If you’re using a lab computer, you’ll need to configure a connec […]
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Amy Lavin wrote a new post on the site MIS2502 Spring 2016 8 years, 8 months ago
Leave your response as a comment on this post by the beginning of class on February 1, 2016. Remember, it only needs to be three or four sentences. For these weekly questions, I’m mainly interested in your op […]
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The trickiest part about creating an ERD from a problem statement is steps to figure out what cardinality each entity has to another. That is challenging because you have to pay close attention to the wording of the problem. Another element to this wrinkle is that there can be attributes to relationships, and that can be confusing too. Because it was not very clear in the class example that one attribute didnt belong to any entity.
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I think the toughest part, at least for me, is determining cardinalities based off of the problem statement. I think its always confusing trying to explain the relationships between everything. My advice would be to carefully read and try to understand the relationships. For example when reading the problem statement underline what each entity means to the others, really try to learn what the relationships mean.
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The trickiest part of an ERD diagram in my own opinion is figuring out the cardinality from entity to entity. The advice I would give someone is to carefully read the scenario/statement and once you pick a cardinality, make sure it makes sense and try to match it to the scenario/statement to the best of your ability.
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I think the trickiest part of ERDs are figuring out whether certain attributes are attributes of the entity or the relationship between two entities in many to many relationships. For example, in the relationship between student and course, grade is an attribute of the relationship. My advice is to just first worry about the cardinality before worrying about attributes you are unsure of.
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The trickiest parts of creating an ERD is figuring out the cardinality between entities and parts of the scenario that actually need to be added to the diagram. My advice to someone would be to read the scenario multiple times and then taking note of the “important” nouns. After making note of possible entities and attributes, I would then go over the scenario again and narrow them down to what the reader believes is important.
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With more complex scenarios I believe the most difficult part is deciphering entities from attributes. It took me some time to figure out what should be it’s own entity and what was just the attribute of that entity. Determining the entities that should exist before adding attributes helped me with this issue.
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ERD’s are diagrams that show the relationships between entities. One of the hardest things with ERDs is finding the relevant information within all the information that is given to you. Many times irrelevant information can trick you into thinking its relevant. Especially because some information may be relevant to the problem but not be relevant to the ERD. That in my opinion is the toughest thing about ERD’s.
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What is most difficult about creating ERDs is how differently every person thinks logically. No matter how hard I try to make sure every aspect of the ERD is correct, I will the consult with a friend or a peer only to find that they think the exact opposite of what I did is correct. For Assignment 1, I sat down with my parents to get some different view points on the second ERD. At one point, my Mom thought “A-B-C’ was logical, my Dad thought “C-B-A” was logical, and I thought “B-A-C” was logical. I thought that this instance with my parents truly defined just how difficult it is to make an ERD that is 100% correct because of how differently every mind thinks.
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The most difficult part about creating an ERD is trying not to overthink it. Whenever I create an ERD I usually reread the scenario two or three times and each time I picture in my head what the ERD will look like. Another tricky thing about it is figuring out whether or not relationships have attributes and when one entity can have multiple relationships and attributes attached to it. This is the part I find myself struggling with the most. Another aspect that I’ve found to be confusing is cardinality. The relationship aspect of zero to many, one to one, etc. has been difficult for me to understand. The advice I would give for solving these issues is to think literally about it and not overthink it. Also reviewing the basics and features of ERD’s as well as doing examples can help you comprehend them better.
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The trickiest part of creating an ERD is not overthinking it. During assignment 1 I kept finding myself second guessing myself. I also found myself struggling with the primary key. It is easier to determine the primary key if the scenario states a unique ID, but if it does not then it gets a bit more difficult. A piece of advice I would give is to through each scenario and underlining or high lighting each entity and attribute. This will really help when making your diagram.
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The trickiest part of an ERD is figuring out the cardinality between the entities. To figure out the cardinality between entities, you have to use logic and pick and pull things from the problem statement. I guess one way to solve this is to dissect the problem statement word by word, line by line. Take an objective stance and try to see the overall picture while constructing the diagram.
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The trickiest part of initiating an ERD is figuring out the cardinalities between relationships and entities. I tend to overthink the situation that drives me crazy. I guess the best way to try to figure it out is by reading slowly, carefully, and paying attention to all details within the description. in addition, I always like to draw it by hand and make changes as I go, it helps me with the process. finally, when you reach what you are satisfied with and think is right create your final diagram.
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Since the last assignment that we submitted before class today. I think I learned more about ERD diagrams, and got a feeling for it more. Although while figuring the diagrams out I had few difficulties. For some reason I always can see something missing in the diagram or I can do it in a different way which is more efficient. I honestly think with more practice of ERD diagrams I will be fine, but the trickiest part is when you have multiple relationships that somehow connect together. I think just by practice it will be more clear!
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I find that the trickiest thing about creating an ERD from a problem description is staying organized throughout the process. In many scenarios, attributes will be scattered throughout the description, and not all conveniently listed together next to where the entity is mentioned. I find that the best way to tackle this problem is by using five or six different colored highlighters to highlight all of the attributes that fall under the same entity, which makes navigating the prompt a lot quicker and easier. This is especially useful during exams where you have a limited amount of time to focus on the ERD.
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Personally, the trickiest part about creating an ERD is the determining the cardinality between the entities. This is difficult because it can be subjective, and many times it can rely on a case by case scenario. In particular, determining if a situation is one to many or 0 to many can sometimes change between situations. I usually figure out cardinality by closely reading the write-up, and determining from there. If it is not explicit in the write-up I usually determine cardinality by making an educated guess on what fits in the particular situation.
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For me, one of the trickiest parts was assigning the cardinality. I didn’t think it would be that hard, but when I had to figure out the specifics, I really started to over think it which tripped me up. Something that helped me was breaking the ERD down piece by piece. Also, I found it helpful to actually draw out the schemas in the next step to see if what I put makes sense in the database.
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I think the hardest part of creating an ERD is determining cardinality. I feel like the descriptions aren’t detailed enough and leaves too much up to matter of opinion. Some cardinality can be different depending on how literally you read the description. How I deal with this is I try and put myself in the situation and imagine all the externalities that might be associated with the decision
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The most difficult part in creating an ERD from a problem description in my opinion is figuring out how to map out the actual diagram. I think it’s important to find out the proper placement for each entity, relationship, etc. because entities are connected to one another quite often. In addition to entities being connected to one another, it also is difficult to determine when relationships acquire attributes. I think they receive attributes when a specific occurrence is taking place i.e. student taking a course or inspector completing inspection. Mapping out the diagram correctly is essential because proper arrangement will make it much easier to connect entities to one another.
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For me, I think the trickiest thing about creating an ERD from a problem description is keeping track of all the entities and attributes that are present. A tactic that I’ve learned to attack this problem is reading the scenario with a few highlighters at hand, highlighting all the nouns that you see present. After that re-read the scenario, focusing only on the nouns, and highlight the entities again in a different color. What ever is left over with the color of the first round of highlights are your attributes. I found this to be much more easier and time-efficient.
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The hardest thing for me when creating an ERD determining the cardinality when its clear from in the problem description. In these cases, I just try to take a step back and think of all the logical outcomes/scenarios that could exist for this relationship.
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Creating an ERD is kind of like learning a new language, so I think the trickiest part is gearing your mind to think in such language. Once one can understand what words and connections to look for in problem statement, the process will go much quicker. Therefore, my advise would be to practice finding those connecting words that create the relationships between each entity and the rest of the structure falls into place.
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The trickiest part about creating an ERD, in my opinion, is identifying the relationship and correct cardinality. When reading the problem description they sometimes include phrases that help to identify the cardinality. When the answer is not so clear, you must step back and think how the two entities interact.
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I think that the trickiest part of creating an Entity Relationship diagram is selecting the right entities and figuring out the cardinality between each entity. I will read the narrative multiple times and then underline what I think could be entities and circle the attributes. To determine the unique identifier, I just put either “number” or “ID” after the entity name unless the social security number is provided as an attribute, since that is already a unique identifier. Finding the cardinality is difficult sometimes if the description does not give much detail about the relationships. I also find myself second guessing while creating ERDs, which makes the process a lot longer than it should be.
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The trickiest thing about designing an ERD from reading a problem description in my opinion is identifying the cardinality between relationships and entities, as well as identifying whether some particular attributes belong to a relationship or an entity/entities. As far as identifying the cardinality, I normally mark the words identifying it some how in the case description and then come back to it at the end once all Entities, Relationships and Attributes are mapped out. When identifying if an attribute belongs to a relationship, one entity or multiple entities, I normally try and decipher the flow of which pieces of information(attributes) travel in between relevant entities. If the attribute is only necessary under the combination of both entities, then it is usually safe to say that the attribute belongs to the relationship.
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The trickiest part about creating an ERD from a problem description is picking the right cardinality. I had alot of trouble deciding which cardinality to use and i was over thinking the situations. my advice is to right down and separately map out everything like we did once in class since it clears up alot of the confusion and helps me to not overthink and change my mind.
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The trickiest thing about creating an ERD diagram I believe is finding the cardinality between entities. This can be a bit tricky since cardinality seems to be very subjective. The way an individual interprets the problem description could be right for one individual but wrong for another. If individuals can correctly explain the choice of cardinality then it is really no right or wrong answer. The advice I would give is to completely dissect the statements, draw the diagram out, and then work backwards to see if the chosen cardinality is logical given the relationships between entities.
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In my opinion, the trickiest things about creating an ERD from a problem description are to identify the entity, primary keys and the attributes that associate with relationships. It is really hard to differ the attributes for entities and the attributes for the relationships. To be honest, I am still confused about this issue, so I really cannot give any advice for this issue. However, I can give some advice for how to find primary keys. In order to identify the primary keys, it is important to identify the “unique” attribute within the all of the attributes.
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Personally, I think that the trickiest parts about creating an ERD is identifying the entities and using the right carnality. The advice I would give to deal with this issue is to read the description carefully and to identify the entities involved. The more you practice and read over the problem description the more you will become familiar with finding the entities. As far as cardinality is concerned, the only way your really going to get familiar with cardinality is practice, practice, practice. The more you read the description and know how all of the entities connect, the easier cardinality will become and the easier it will be to label entities and attributes.
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Q: What do you think is the trickiest thing about creating an ERD from a problem description?
A: I think the trickiest thing about creating an ERD from a problem description is to identify one to more or more to more relationship between entities. And sometimes the descriptions would interrupt your thoughts, you might be circle a wrong attributes for an entity. I always add additional attributes to entities, then make the whole database look so messy and complex.
Q: What advice would you give to deal with that issue?
A: To differ the attributes in the problem description, the first step is to make sure the main entities/tables in an ERD. You can have a draft with simple fliter, separate parts of attributes into their own entities. Use of verbs to make relationships between entities, read carefully on specific numberic words like once, both, only one and at least to identify the cardinality. -
What do you think is the trickiest thing about creating an ERD from a problem description? What advice would you give to deal with that issue?
The trickiest part of making an ERD is appropriately assigning the cardinalities to the entity-entity relationships. It can be tricky if you have many entities displayed; there could be discrepencies in cardinalities if you are dealing with entities that are intertwined. A good way to combat this problem later in the process would be to write down the cardinalities of the entities before creating the ERD.
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For me the hardest part was navigating the wordiness of the scenarios. This simulates a real world experience where the subjects you interview for gathering requirements will want to tell you every last detail, whether or not it is relevant. So having to sort the redundant and irrelevant information was the hardest part for me.
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For me the trickiest part about creating an ERD diagram from a problem statement was identifying the correct cardinality from the various different entities. It was relatively easy to pick out the correct entities and it was also relatively easy to pick out what the attributes were for each entity. However, it got tricky when you had to pick out what the correct cardinality it was from entity to entity. My best advice would be to read it very carefully and try and decipher what cardinality the problem is trying to ask for.
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The most difficult thing about creating an ERD from the problem was deciding what entities went where. At first you make the process and it seems right, but then you play out the process in your head and you start second guessing on where the entities should be placed. An easy fix for this is to simply read the problem repeatedly. There is no use in creating an ERD until you are certain what the start to finish process is.
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Amy Lavin wrote a new post on the site MIS2502 Spring 2016 8 years, 9 months ago
Here’s the doc for In Class Exercise #3 – In-Class Exercise #3 – Creating Schemas
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Amy Lavin wrote a new post on the site MIS2502 Spring 2016 8 years, 9 months ago
Here’s the link to the class capture for 1/22/16
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Amy Lavin wrote a new post on the site MIS2502 Spring 2016 8 years, 9 months ago
Class Capture for January 20, 2016
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Amy Lavin wrote a new post on the site MIS2502 Spring 2016 8 years, 9 months ago
Here’s the doc for Assignment 1 – Assignment #1 – ER Modeling
Please use the email address upload.Assignm.cu4t98x7lb@u.box.com in the To Line of your email to upload your completed assignment to OWLbox.
The […]
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Amy Lavin wrote a new post on the site MIS2502 Spring 2016 8 years, 9 months ago
Leave your response as a comment on this post by the beginning of class on January 25, 2016. Remember, it only needs to be three or four sentences. For these weekly questions, I’m mainly interested in your op […]
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I read an article last semester explaining how the NBA game has been transformed due to data analytics. The data showed that players had nearly the same probability of successfully converting a mid range shot as they do a 3-point shot. The 3-point shot has a 50% increased benefit over the mid-range shot worth only 2 points. Therefore, teams like the Houston Rockets and Golden State Warriors have taken the information from the raw data and take 3-point shots if they cannot score on a layup.
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As a student, I always strive to achieve high grades. During finals, I have a clearer picture of what my grade will actually be. By taking the data of my grades over the semester from tests, quizzes, and assignments, I was able to calculate my current grade. I then analyzed my grades and devoted more studying time for tests in classes where I needed the best final test score to get the grade I wanted.
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When working as a Retail Manager for Armark during the World Meeting of Families last semester, I sold “pope attire” to the mass crowd the event attracted. We kept a strong track of inventory numbers and used that data to determine what inventory needed to be replenished during the next re-stocking period. By effectively tracking and analyzing this data, we were able to maximize profits by keeping items in high-demand stocked throughout the event.
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An important part of eCommerce is advertising. Companies such as Facebook and Google (YouTube) use data collected from customers to create personalized ads that are tailed to each customer. For example, if someone has recently been shopping for shoes, the software will in return display more shoe ads for a person. These tailored ads create a better experience for not only the customer, but for the seller because they are more likely to receive more business because their customer bases come from advertisements that are specialized to someone’s personal interests.
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Over the summer, I decided that I was interested in figuring out just how much money I spend while I am at Temple’s campus. So, when the fall semester began, I started to record all of my purchases that occurred at Temple in an app on my phone. Besides the price, I would also record what kind of purchase it was, like food or clothes. When the semester concluded, I calculated the total I spent for the semester, as well as how much I spent in each category. Through the collection of this data, I was able to turn it into useful information and understand where I spend the most money and what on. Since then, I have concluded that packing my lunch would be in my best interest.
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Working in retail for Dick’s Sporting Goods this past summer has taught me a lot about data and efficient ways to store it. For example, we have devices that not only keep track of our own inventory, but also the inventory of other Dick’s Sporting Goods stores in the district. This way if our store doesn’t have the product or size in stock that a customer is looking for, we can scan the item and up will come all the stores in the district that have the product. This has proved to be effective for our sales as well as the customer because they are getting the product that they desire.
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I shadowed a co-worker who is currently working in the demand-planning department at my current job one day. He was working on a project that had lines and lines of information on an excel sheet. On that excel sheet was information that could estimate how long a distributor or a vendor will take to ship out a product to us. One column would have the vendor’s name, the next one would state the location, the next one have the approximation of when we received the package or if we have not. With the use of the filtering function on excel, he was able to create a graph that condensed the information and gave him an approximation of when a package or product will be received. For most vendors we could see that it can take about 3-5 days, then there are the more extremes which maybe coming from Italy, which can take from 6-7 weeks. (Fun Fact!: Most products we receive from Italy is shipped via boats). It was really neat to be able to see all of the data that was raw, to be turned into information that can help the company long-term.
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As a student in high school I spent my last year working in retail, more specifically, Sears. While working there, the management had been going under some changes in how we go about interacting with our customers. Ultimately, the goal was to “convert customers into members”. Our POS systems now required us to ask our customers for their basic info (name, email, phone #) and input this data into our database. Just from giving this data to us, they became members and could now expect several coupons in their email on what they are most likely to buy next. This was all possible due to the information that was mined from the data collected.
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During my internship with an Insurance broker, I gathered raw data from my clients like their premium, their total insurable assets, previous year’s losses and so on. I analyzed that data to determine whether or not their premium would rise or fall. For example, if they had a year with no losses, and they paid their premiums in full, and on time, then I would nudge the carrier to lower my clients’ premiums for the next policy period come renewal time. The opposite could happen as well, my cleints’ can have a terrible year filled with losses, and then I would not be able to control the carrier from raising the premiums, and would proceed to look for another carrier to place that client with. That continues to be my experience with raw data turning into information.
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An example of raw data being turned into information comes from 2101 with professor Lavin from last semester. Amazon likes to track what items users often view and or purchase. With that data, Amazon is able to come up with suggestions and reminders for users. For example, if someone buys their laundry detergent from Amazon; within a month or so the user may see the detergent popping up as a suggestion for them to buy more detergent as they may be close to running out.
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I worked as an underwriting intern for an employee benefits consulting firm where I gathered raw data regarding high claimants, such as number of high claimants, diagnoses to determine future losses, dollar amount over the threshold, and RX claims. I would use this information to figure out how the high claimants were affecting the overall insurable population, which helped to determine if rates needed to increase or if the client could benefit from switching carriers. In this way I turned the raw census data into information that we could use to benefit the client.
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Currently, I am working a part time internship at Philadelphia Gas Works in the Gas and Acquisition Department. PGW controls all the gas that reaches the city gates, including that provided by external suppliers. In the department, they complete a lot of work with Excel and a system called Retail Operations to keep track of different suppliers who want to provide citizens in Philadelphia with gas. The department I work for needs to tell the suppliers how much gas they are permitted to bring into the city to provide for their clients. Using Retail Operations, the temperature is used to forecast how much gas will be needed for the week. If the temperature is especially high that week less gas is needed, if the temperature is drastically low then more gas will be needed. Forecasting uses past data, how much gas was used at the particularly temperature last year, and applies that knowledge to how much gas will be needed for that same temperature this year.
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I used to work for a limousine company where I was assigned to insert date into their systems. By data I am referring to all pickups, drop-offs, times of arriving, revenue from each job, reviews about drivers, ..etc. So after inserting and organizing all the date; we find out who are our returning customers, loyal customers, what seasons or months that we are most busy in. Also an important point is knowing more about our drivers if they are satisfying the customers. All of which prepares the company to know how many and which drivers to have available in specific seasons. We also get a feel of what jobs to prefer over others because some might be taking more time and isn’t really worth doing anyway.
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For my final project in Data Science I used data from the Federal Election Commission (FEC) to come to conclusions about the effect of Dark Money in the 2012 Presidential and Congressional elections. I downloaded data that outlined all individual contributions as well as contributions coming from PAC’s and aggregated it into a working model on Tableau. From there I found that in 2012, the congressional campaigns that spent the most outside money didn’t necessarily win. Also, I found that in both the Congressional and Presidential election that the most effective use of outside money was on ad buys.
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Past experience in the management field of a franchise business proves how I can relate the usage of raw data that was turned into information. While; working in a franchise store, at the end of the day there is always a report that takes place. Which; explains when we had the most sales, how many customers we had in each hour, and a lot of more great data! That data was download on the computer and turned into information that could make the franchise more profitable. Sharing an example; after the process of turning the data into information I was able to determine how many employees I needed to assign for each shift. I knew what products are most wanted during specific hours resulting in what products I needed to order for my next shipment. The best thing about raw data, numbers, and information that it is quite impossible for it to be wrong. By reporting data at the end of the day and turning it into information makes a great business strategy.
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Previously I have worked for a store called Five Below. As each product is being purchased there is a system in the back office where we can track which products are being sold and the quantities of products being sold. From viewing these we are able to recognize which categories or products are sold the most.
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As a Risk Management and Insurance major student, I understand that collecting raw data from potential insured such as, their names, addresses, genders and occupations is extremely common and important for insurance companies to determine how much risk each potential individual insured has. The insurance companies compare their potential clients’ raw data to their huge database in order to turn the raw data into useful information-risk levels of the potential clients. The risk levels of the potential client are important information for insurance companies because insurance companies rely on the information to determine the premiums for their potential clients.
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Now in 2016 many scientist are finding raw data and putting it to use. In my experience, I have seen raw data turned into information used in politics. In 2012, Obamas staff used metadata to focus on issues that interested voters. They also would use Facebook so when people liked or followed Obamas campaign it would give staffers much needed information about his voters and there network.
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As a senior graduating in May, I conduct a lot of research on the Human Resources field to stay abreast of current trends and for interview preparation. Deloittte’s Human Capital Management sector recently posted an article on how Human Resource Departments will soon leverage data analytics tools to conduct performance reviews and manage talent. Managing talent is the new approach that companies are taking to ensure there employees are updating their skills respective to their line of work. Organizations will have their employees take online courses. Using this data, the organizations will then be able to gauge where their employees are in terms of the skillsets necessary for that industry. By leveraging this data, companies can continue to identify skill gaps and offer courses to help employees fill those gaps.
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As a baseball fan there has been a lot of examples over the last couple years of how data has been turned into information that is used make a team more successful in preventing and scoring runs. One of the most obvious examples has been the use of shifts in the MLB. Teams have had a lot of data on where hitters have hit balls on the field and what types of pitches and pitch locations caused this. So teams have used this data to predict where a hitter will hit the ball when they are pitched to a certain way and in order to prevent that batter from getting on base they shift their fielders to the certain spots they think they are most likely to hit it (rather then keep the fielders in the classic/typical fielding spots). This strategy was criticized at first but now has been adopted by the majority of the league very quickly.
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I ran a camp kitchen last summer in St. Croix, and kept a detailed inventory of food products and prices. Since the cost of food in St. Croix is much higher than state-side, I had to track every dollar spent. I collected data on each item, its price, quantity, where I bought it, and how much I actually used for one week. With these data pieces, I was able to generate information on which store had the best prices and how much of each item I really needed. With this information, I was able to stay hundreds of dollars below budget and create an efficient operations standard for the kitchen.
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I read an article a while ago on the 76ers, and their strategy behind consistently losing games on purpose. In the NBA, the teams with the lowest records get the best draft pick opportunities for the following season. The 76ers have gotten both praise and much criticism from both the NBA and its general basketball community for intentionally tanking, however they’ve taken the statistics of wins and loses in games and used that data to discover that consistently underscoring by a specific amount of points each game can assure themselves of having a strong team next season. This is an investment decision no different then any organization might make at some point, to take a hit one year to assure themselves a better long-term gain in the future years to come.
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I worked as an intern for a financial magazine production company back in Singapore. I was once given the responsibility to observe and report trends from what kinds of information magazine users prefer. I was given a huge chunk of data which showed how many people preferred reading huge chunks of informations compared to images and infographics. There was obviously a greater number of individuals who preferred images and infographics to chunks of paragraphs. From this I came up with the recommendation that the magazine should incorporate more images and infographics, which led to the increase in sales.
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During my time working for Postmate’s, I was enlisted to the “street team”. If youre unfamiliar with Postmates, it is essentially a delivery service app that delivers food or whatever you want/need within an hour to you. With the street team we had to do some flyering, or “canvasing” to get the word out about Postmates in Philadelphia while, then it was still early and not as big as it currently is. We took the transnational data as well as all of the customer location data to see where the most popular spots for Postmates in the city was. Using that we could see what areas needed more attention to fully cover all of Philly and try to inform all areas of the city.
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In high school, I ran cross country. When it came to qualifying for states, it was hard to compare runners across different districts, because we all ran on different courses in different weather conditions. They had all the information about the courses we ran and the time we ran, but that raw data didn’t do much to explain who deserved to go to the state championship. So, the committee had to match up our “numbers” that we wore during the race up to the time we crossed the finish line, and compare those statistics to runners from other districts. From that, they were able to decide which runners had run the fastest on the hardest courses, and who deserved to go to states.
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Over the summer I worked for a country club called Squires. The day of the week would determine how many members would show up. After a while of gathering the data of which members and how many members would come on certain days, that data would determine what would needed to be properly down for that day. We used that data to plan accordingly as to how much food, drinks, and workers we would need for that day as well as set up a schedule to the tee times that they desired.
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At the country club I worked at over this past summer, we started having live music on Friday nights. For the first month of the summer, we had a different genre play each week, and in the following days asked the members what they thought of the performer. The ‘Jimmy Buffet” style of music got the highest appreciation by far, so we stuck with that genre for the rest of the summer, and it was a big success.
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I am on the rowing team here at Temple and last year I experimented with my diet as how it impacted my physical output I can produce at practice. Everyday I would chart my food and also record the time it took me to complete the workouts at practice. After a while I looked back to see the results and could determine the best combination of carb/protein/calorie intake that my body best responds to so I can improve my fitness and speed.
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At my current employer we have setup our virtual server environment to monitor and report on several key factors, two of them being Memory and CPU utilization. We can see peak usage hours, low or no usage as well as when both get over utilized. The data metrics are reported in real time as well as daily summary reports. We can adjust resources on the fly (most of it is automated now) in order to try and reduce contention for system resources. If certain application servers are constantly pegging resources we can make adjustments when needed.
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An experience where I turned raw data into information was when I participated in the Data Analytics challenge last semester. My group members and I looked at raw data from the Pennsylvania Ballet website, transferred it to an excel sheet, and created a pivot table to utilize the information. We were able to discover what state and city sold the most ballet tickets, what show was the most popular, and the method in which customers purchased their tickets. We then used all of that information to come up with our marketing campaign.
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In high school i worked a country club where perfecting service was always our goal. There were 4 types of memberships you could get for all different prices and upon choosing one we asked a few different questions such as drinking preferences, kids name and age, food allergies, and a few others i cant exactly remember. From this information,we were able build on as they came in for dinner and eventually could predict the inventory needed to meet the needs of customers
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I love to watch movies and TV shows and have noticed that big data plays a huge role is businesses such as Netflix or Comcast. When I watch a movie or show on say Netflix, they will make suggestions on which shows or movies you may want to watch that are similar to the ones you’ve previously watched. For example I was watching a documentary on Netflix. After I had watched it, there was a category on my page that said “Because you watched…” It gave me a list of other documentaries that were similar to the one I had just watched. This is important for customers of Netflix and other similar companies because it will help them expand their range of customers and improve customer satisfaction and well as increase profits.
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The place where I work, they were used to be analog in their some place. Now they updated almost everything in digital format. For example, they use to keep employee attendances just punching a card. Now they gave a card with chip which employees have to swipe or touch with a device then device can collect all the data.
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A few years back I worked for a car wash. It was the busiest wash in town and we would often have days where the line of cars was backed up onto the street a few blocks down. By taking the raw data from our average sales we were able to determine whether or not we would have enough soap and materials to withstand the massive demand on those busy days. Once we gathered that data, we put it into a broader perspective and use the raw data itself to calculate how much the wash truly costed per car. By looking at the data and producing information, it was determined that the wash profited tremendously due to charging $8+ per wash when the wash truly only spent close to $0.10 on supplies per car. The profit was massive and gathering the raw data made it simple and easy to view our goals.
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Amy Lavin wrote a new post on the site MIS2502 Spring 2016 8 years, 9 months ago
Here’s the doc: In-Class Exercise #2 – ER Modeling
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Amy Lavin wrote a new post on the site MIS2502 Spring 2016 8 years, 9 months ago
Here is the document for ICE # 1: In-Class Exercise #1 – Identifying Entities
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William Black joined the group MIS4596-003 Spring 2016 – Messina 8 years, 9 months ago
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Paul F Carbone joined the group MIS4596-002 Spring 2016 – Messina 8 years, 9 months ago
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Joseph M. Allegra wrote a new post on the site Fox Computer Literacy Test 8 years, 11 months ago
Freshmen students who entered the Fox School after 2008 must take the FOXCLT. Transfer students who are in the core-to-core curriculum or transferred with 45+ credit hours are strongly advised. If you are […]
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Amy Lavin's profile was updated 9 years ago
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Amy Lavin wrote a new post on the site Information Systems in Organizations 9 years, 1 month ago
I have made the following updates to the class site:Updated the PowerPoint from last weekUpdated the PowerPoint for this week (just a minor change)Added the activities that we will complete to the schedulePlease […]
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Amy Lavin wrote a new post on the site Information Systems in Organizations 9 years, 2 months ago
Welcome to MIS2101, Information Systems in Organizations. We will not be using Blackboard for this class. Instead we’ll be using this site which is hosted by Community.MIS.Temple.Edu. This site is built on Wo […]
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Amy Lavin wrote a new post on the site MIS2101 – Summer 1 2015 9 years, 4 months ago
Posted for Miller Harris: Miller Harris Class Reader
Beyond Reality, Web 3.0, and Applications We Can barely Imagine
Included below is an interesting TED Talk from 2007 that describes a program […]
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This sort of sums everything up – “I think people are starting to realize this is the future of building consumer devices,” he says. “But it involves big challenges at the intersection of optics, electronics, algorithms, and understanding the human visual system.” This technology is amazing but hard to wrap your head around – so to speak! I’d be interested to know if anyone has used anything like this – and your thoughts! Great article on what is coming!
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Amy Lavin wrote a new post on the site MIS2101 – Summer 1 2015 9 years, 5 months ago
Pearson MyMISLab Book, Technical and Customer Support
BOOK INFORMATION:The link below is for the Pearson Student Registration Process.
Pearson Student Registration Instructions
Use Course ID: <span style="color: […]
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Amy Lavin wrote a new post on the site MIS2101 – Spring 2015 9 years, 6 months ago
Here is an updated syllabus: MIS2101_Spring_2015_Lavin_Syllabus-v2.0 (9)
Remember – project 2 is now due on April 14.
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Amy Lavin's profile was updated 9 years, 6 months ago
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Amy Lavin wrote a new post on the site MIS2101 – Spring 2015 9 years, 7 months ago
Here’s the word document for Project 3: Digital Identity Management Project
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