David Schuff

One of the most important takeaways I have gained from Data Analytics is being able to communicate with a relational database through SQL. Today I actually had the opportunity in an interview with a UHS Director of Information Services, Supply Chain where he talked about the importance of SQL in their division of the supply chain. I communicated my experience with the computer language and my experience with joining tables and subselects in SQL during my time in this course.

To me, the most important takeaway from this course was learning about where data comes from, how it is stored, and how it can be transformed. Before this class, I did not think too much into the process, but now I know a lot more about it and totally understand it. If I were to explain what I have learned from this class to a future employer at a job interview, I would explain that we learned about different types of databases, cardinality in Entity Relationship Diagrams, schemas, SQL, and R. I would emphasize that all these topics helped me better understand how to interpret and gain insight out of data sets.

My most important takeaway from the course was how to visualize data effectively. In a future job interview, I would explain how I applied various strategies in data visualization like reducing chart junk and telling a story to place third overall in the data analytics track for the 2018 Data Analytics Challenge.

My most important takeaway from this class was how to utilize data that is gathered, by entering it into a data base, storing it, and transforming it. Another important takeaway was pulling data from a database by using SQL language. In future interviews, I can now explain to potential employers that I have abilities to take their data and translate it into way that they can improve their business. I would explain to them that I am familiar with SQL language, R studio, and data storage.

The most important takeaway from this course for me was the concept of data cleaning and using reliable sources of raw data to analyze. Through the various different exercises used throughout this course, we were always given “clean” data which allowed for easy analysis right away. In most cases, raw data can often times be unorganized or misleading which can cause many complications. In a job interview, I would explain the different methods we used to clean and find reliable data to ensure correct analysis.

The most important takeaway from MIS2502: Data Analytics is cleaning and modeling data with Entity Relationship Diagrams (ERD) and Schemas, transforming data, and using data mining to uncover hidden trends to support decision making. To a future employer, I would explain that I can communicate with any database because I have knowledge using MySQL, which is the foundation for communicating with databases that are similar to others like SAP. I can retrieve data using SELECT statements and summarize data from a relational database, and ultimately the data cube. I use association rules to uncover relationships among predictor variables.

Personally I feel the most important topic we’ve learned in this course was how to visualize analytical data. Prior to starting this course I didn’t know there was rules and guidelines of displaying data. When interviewing for a potential employer, I can advise them that during studies here at Temple University I have learned how to extract data, reconcile all that data to become analytical data and effectively conveying a story using that analytical data.

For me, the most valuable part of this course was exposure to R. I am an actuarial science major, and I have to take a predictive analytics exam using R. In my interviews I have described MIS as a way of utilizing data to help a business run and make decisions. I explained that I furthered knowledge of SQL and R.

The most important takeaway from this course for me was learning how to “visualize” data correctly, learn the structuring (in’s and out’s) of how a database functions, and also learning basic code and how to analyze a running script. I also found participating in the Data Analytics challenge to be a very interesting headache – it is definitely not a competition I ever thought I would be a part of.

In a future job interview I would explain how in class we learned the basics of several different coding languages (SQL, R), among numerous other strategies in data visualization: how to organize miscellaneous data, how to reduce chart junk, and how to ultimately read/analyze a plethora of chart and data info.

Overall a pretty challenging but very rewarding class.

To me the biggest takeaway is how applicable the whole body of work I learned this semester will be in the future. I learned to analyze data and how to present it as business information to future colleagues and clients. I put the skills learned to use by modeling and designing different database centric solutions. I can now assist in managing multiple database systems, source that data, make it homogenous, and generate an analysis and visualization to communicate the data as information. I also learned multiple coding languages and concepts using both SQL and R, two tools businesses rely on heavily.

The most important takeaway from this course for me was probably participating in the Analytics Challenge. I believe this was a good challenge because we had to deal with ambiguity and learn to utilize our resources when the answers weren’t as obvious. It was also a good experience because we used real-life data to create a visualization; it combined my analytical and creative sides. I would tell an interviewer that participating in this challenge allowed me to see how real data can be used to fix companies’ problems, as well as helped me understand how different components of this class come together.

I believe the most important thing to take away from this class is MySQL and data mining. I believe these two are the most beneficiary to me because of the way I could now explain data and view it in certain forms. I would explain to an employer that I can use these skills to my advantage in any data related problem that may have occurred or that will face me in the work day.

Being a digital marketing minor, I thought the data visualization unit was most important and relevant to my career. A lot of my current job revolves around me presenting data to my boss and I feel after this class I feel confident in my ability to present it in the right manner. When talking to a future employer I would speak about the data analytics challenge and how i went about selecting the most effective methods of data visualization.

How would you explain what you’ve learned to a future employer in a job interview?

The most important takeaway I got from the MIS 2502 course was how data and technology are constantly changing our lives. With the use of SQL and R, students in the class were able to discern how to effectively analyze, interpret, and display data in a way that explicitly communicates certain patterns. For example, using SQL, we were able to determine which type of movies certain customers watched. Through R, we were able to create decision trees to determine the customer profile that is most likely to repay his or her loans. To what I would explain to a future employer for a job interview, I would talk about the same material: I learned how to effectively use data to communicate certain information necessary to solve certain job problem sets.

The most important takeaway for me was learning the purpose and effectiveness of ERDs. ERDs have a unique purpose inside many businesses and learning all about them has helped me in previous interviews and will also help in future interviews. In an interview I had recently, they actually had an ERD posted on the wall behind the interviewer. It was an ERD of the HR department’s hiring process. The interviewer and I discussed the ERD during the early part of the interview and I believe it is one of the reasons that I was asked to come back for a second round.

I believe that the biggest takeaway from this course would be my ability to use R and my SQL. I only have one more semester before I will be entering the business world where I will be working with Risk Advisory. Specifically, data trees in R are helpful because they are able to tell the percentage of the likeliness for a person to do something, for example repay a mortgage. After this course, I will be able to tell my future employer that I am able to take data files in order to run scripts into different ways to visualize and understand the data. Since technology and big data are taking over the business world, my ability to use these programming languages may put me beyond some other candidates in line for a similar position.

The most important takeaway for me from the course is data visualization. I learnt how to transform lots of data into table, charts and infographics which is easier for people to understand the information. I would explain to a future employer that the technical skills I learnt from this class includes ERD, SQL and R. Those are some languages that really useful to analyze data.

For me, the most important takeaway from the MIS 2502 course was learning how data is collected, displayed, stored, manipulated/transformed, and interpreted. I learned how data and technology are changing our lives every day. The use of SQL and R helped me expand my experiences in software use. SQL is used by many companies, including the one I Intern at currently.
To a future employer in a job interview, I would explain to them some of the assignments we had done in this class to provide an example of how I used SQL and R and how I collected, displayed, stored, transformed, and interpreted. Providing an example always a way to provide employers with an idea of how you work.

The most important takeaway from this class is how to analyze and interpret data. I never knew how to use MySQL before this class so that was a big part for me. I would explain to an employer than I can use these skills I’ve acquired in order to organize and interpret data for a particular problem.

My most important takeaway from this course are ways to organize and analyze data using MySQL, RStudio, ERD diagram and etc. And learning about how to transform those datas to make it easier to understand. After this course, I would be able to tell my future employer about the technical skills I’ve learned and how to apply it to different issues in the business world.

The most important thing I learned in this class was how to use tools tools such as R and data visualation software like Tableau to be better. I leave this term ‘better’ intentially vauge because I will use these tools to be a better intern at EY for FSO Assurance, a better leader in my organization, Student Center for the Public Trust, a better student as I go on to the Temple MAcc program next year, a better networker by implementing visuals like pictograph on my LinkedIn, and a more effective ‘household leader’ because I can better analyze the data I collect on our bills, etc. There are limitless uses to the things we learned in this class.
If I was to explain this in an interview, I would say high-level, I learned data integrity and how to better work with and leverage data, low-level, I would give specific examples of the items I mentioned above.

The most important takeaway from the course in my opinion was learning SQL and Rstudio. I believe having an idea of how they work will come in handy when interviewing for jobs. I would explain to a future employer that I know how to SELECT, DELETE, ALTER, etc. statements in SQL as well as interpret information from Rstudio.

The most important takeaway from the course for me was learning how to communicate with SQL servers. SQL is a very useful tool that I see myself using in the future. Also, I was amazed at how I could master using complicated joins to find specific data that I wanted. I would explain to a future employer that I learned the necessary skills to draw data from a table and analyze that specific data. In addition, I would tell them that I learned how to store data and fix inaccuracies in data which would allow me to give the data they have meaning.

I think one of the most important and useful takeaways from the course is the knowledge of excel functions such as VLOOKUP, Pivot Tables and Concatenate. These are tools that may not be the most advanced skills learned this year, but they are great ways to organize large amounts of data and helpful for data visualization. I would explain to a future employer that this class has given me practical skills that I can use right away, such as these excel skills, SQL and Rstudio.

The most important takeaway from this course comes directly from all the exercises and homework of SQL and R. The analytics of data is on the rise and having an extra skillset is always prevalent. I’ve already been able to speak upon my ability to analyze data and any employer has always been interested in what I have said. As a risk major, I explain how I am differentiated in that I would be able to predict risk better because I can look at numbers behind premiums and claims.

The most important takeaway I personally got from the course was learning about SQL & R. These two programs helped me better understand how to analylize a data list and how to read the outcome in a clean way. I also learned more about data, how it is transformed and read. It was also helpful to know what types of data visualizations were important and more appealing to a user. I would explain to a future employer I would emphasize all the lessons ive leafned and mostly about how these programs have helped me look at data more efficently.

The most important takeaway from this class was understanding where data comes and how it can be organized to be easily understood. The process of how data is transformed, stored, and relayed is important because it creates a foundation for further applications of data usage. I would explain to a future employer that I have background in SQL and R and that I understand the core mindset behind the actual coding.

For me, the idea of “garbage in, garbage out” is very important for data analysis. It is the big issue that the people who design data systems are not the users. Moreover, the users do not understand IT enough and can not predict uncertain business conditions and how these conditions affect the way to store and extract data. The ineffective communication, which can come from lack of knowledge either one of two fields (IT and business), causes issues at the first step of design ERD and schema. Of course, the most important takeaway from this class is modeling data and how to eliminate to create dirty data at the design step or even how to clean data.
In future interview, I would explain that this class helps me to have background knowledge to design data, to make me beware of the distance of skillsets, needs, and wants between the data designers and the users, to know how to use many types of tools like MySQL Workbench, advance Excel with Pivot Tables, Pitktochart, RStudio, and to get the studying tips how to learn to use a new tool or software from my MIS 2502 professor.

In my opinion, the most important takeaway from this course was VLOOKUP, Pivot Tables and Concatenate of Excel. Although I’m not major in MIS, but I think it will helps me a lot in my future career. Those skills helps me in organize the massive datas and helpful for data visualization. I would explain to a future employer how this excel skills, SQL and Rstudio helps me have a better understand in datas and how I use this data analysis to help and support my works.

For me, this course helped me a lot. I was not in MIS major, but after this class, I felt it is very useful in my future career. I learned how to use SQL and R studio to deal with a huge amount of data. Our lives will be full of data in the future, so these tools will help me very much.

The most important takeaway from this course was learning how to retrieve data through SQL and writing and understanding queries and how to do Data Mining and make plotter charts. It was fun to learn and do all the assignments as it gave me information about so many different aspects of Data Analytics. While talking to my employer, I will let them know about my learning of SQL, R-studio and Pivot tables and how i have learned all these things and I enjoyed it.

The most important lessons that I will take away from this course is the SQL. Knowing SQL is very important in today’s world where there are billions of data being created and stored. Being able to organize and pull the data is a very good skill to have. SQL is a very important skill that employer will be happy to see on the resume. and i would explain the employer the ease of getting data quickly and efficiently which would help in decision making.

For me, the most important thing I learn from the course is mySQL and Rstudio. Understand these two platforms and how data is stored will help me stand out during interview. In addition, the data visualization assignment is also a good project to put in the portfolio so that employer could see how I understand data and apply what I learn in practice.

The most valuable part of the course for me was exposure to SQL and R studio. I believe that understanding the basics of SQL and R studio will be quite beneficial to my marketing degree and will set me apart from other marketing candidates who may not have exposure to these fields. I would explain to an employer that I have a great understanding, as well as knowledge of the basic functions, of SQL and R studio that I am able to build upon.

While there was an abundant source of important information from this course, the most important takeaway for this course is understanding how to gather data from CSV files and import them to R. Then we can collect this analysis to implement clustering of data, decision trees, data mining, etc. This data can be used to predict certain outcomes which could benefit millions of people. This type of information would be beneficial in life after college.

The mos important takeaway from Data Analytics for me was learning both MySQL and getting an into to RStudio. Having a knowledge and the skill set to use different analysis tools has helped me in my ability to be able to transform and extract value from data. It shows employers that I have the ability to pick up on new technologies quickly and have a more well rounded view of data analytics.

I believe the most important takeaway was learning how databases work, from figuring out what requirements a particular database might need to implementing a schema. Most of us deal with sort of database every day, so as MIS students, it is very important for us to understand the “behind the scenes.” I also really enjoyed learning the basics on how to use SQL and RStudio and I plan on continuing to learn R on my own.

In a job interview, I’d argue that knowing how to manipulate data and quickly extract relevant information is a crucial advantage. As we’ve seen in class, many of the things that we have done through the softwares can be done by hand. However, a task that would take a human being a couple minutes or hours can be executed in seconds through a program when you know speak its “language.”

To me, the most important takeaway was learning how to use MySQL. It was awesome to be able to add it to my resume and to learn how to create my own database within mySQL as well as extract data from an existing database. I would describe this class as teaching me the fundamentals of big data and how to work with data. I would say that it taught me how to both extract and input data from/into a database and how to gather insights from data through different statistical tests.

For me, the biggest take away from data analytics was just how to play with data in general.Last year, when I attempted the data analytics challenge, I had no idea where to start or what I was looking at. This year when I attempted it, I knew exactly what to do to make the data readable and meaningful, and had a variety of tools at my disposal. Though I used excel for the challenge and prefer using it in general, I feel comfortable using R for statistics and manipulating scripts to output the data I want.

The most important things I learned from the course are MySQL and Rstudio. They are both very useful tools for us to deal with data. I can use MySQL to get data out of the database or put new data into the database. I understand how RStudio analyzes data and could use it to do three analytics tasks: decision tree, clustering, and association rule mining. If I get an interview, I would explain to the recruiter how to extract, transform and load data effectively and how to apply data mining and predictive analytics to generate business value for the company.

Awesome ! Wishing you all the very best. !!!

During the Thanksgiving season, many stores/restaurants like Boston Market and Cracker Barrel offer special Thanksgiving dinners to customers. A Thanksgiving-themed question that I could answer using clustering would be, “Group similar restaurants/stores that sell Thanksgiving dinner-themed family meals.” The data I would gather for this would be # of Thanksgiving-related dishes offered and # of non-Thanksgiving-specific dishes offered in a meal. Upon performing cluster analysis, restaurants would be grouped according to how many Thanksgiving dishes (stuffing, turkey, etc.) and how many non-thanksgiving-related dishes, like Boston Market’s dip and crackers, they had.

A Thanksgiving question involving clustering would be which regions in America like which kinds of side dishes? The kind of data I would gather would be region (South, Northeast, Midwest etc) and top 3 kinds of side dishes eaten on Thanksgiving. Then data would be grouped to see if people from the same region like the same kinds of food.

An example of a thanksgiving- themed clustering example will answer this question: which areas of the United States consume a Christmas ham versus a traditional turkey for thanksgiving? The data for collected for this experiment is the location (urban, suburban or rural), demographics and the total sales units of Christmas hams and traditional turkeys. Groups will be clustered together to see if similar data points within a particular group consume the same type of meat. Stores can then use this data to manage its inventory to have enough turkeys or hams on hand to satisfy consumer demand. It will also give insight to the reason why a particular group consumes a particular meat (could it be for religious reasons, preference, dietary restrictions,etc.).

A Thanksgiving question that could be answered using clustering is: Which pies are most popular during Thanksgiving in each region of America besides pumpkin pie? The data collected would be the geographic location, demographics such as age and gender and units sold.

A Thanksgiving-themed question using clustering could be, “Which dish (turkey, stuffing, sweet potatoes, etc.) is most popular among different age groups (under 10, 10-17, 18-25, etc.)?” The data can be collected to determine if people in the same age groups generally like the same Thanksgiving dishes, and companies can use this data for marketing efforts by targeting their products differently by age group.

A Thanksgiving-themed question that we could answer using clustering is: what kind of family will go out for Thanksgiving dinner instead of eating at home? The data we need to collect is the number of family members and the annual income of each family. After the cluster analysis, we can see which groups prefer to eat outside, so restaurants can develop promotion to attract specific groups of customers.

A Thanksgiving themed question that could be answered using clustering is, “How many people put gravy on their mashed potatoes in relation to their geographic region.” The data needed to be collected includes how many people purchase gravy, how many people put gravy on their mashed potatoes, and the geographic regions where Thanksgiving is celebrated.

One thanksgiving related question that could be answered using clustering is: based on geographic region, in what room do families eat thanksgiving dinner in? The data collected would be region (Northeast, Southeast, Midwest, Southwest, Northwest, etc.), and room eaten in (Dining room, Living room, Kitchen, etc.). The data could show that depending on where a person lived, it might affect where their family eats thanksgiving dinner.

A Thanksgiving – themed question that you could answer using clustering would be “Which areas in Pennsylvania prefer turkey and/or ham as the main dish for Thanksgiving dinner?”. The data to be collected to answer this question would be, the types of areas (rural, urban, or suburban), total sales of ham and turkeys in those respective areas, and the general demographics of the areas (age, gender, etc).

An entertaining Thanksgiving themed question involving clustering could be demonstrated by the following: “Which age group (Under 10, 10-15, 15-20, etc.) is most likely to use their phones at the table first – and for how long were they viewing whatever they desired?” The data collected would determine if people in different age groups behave similarly in today’s day and age – and what they are viewing while bored at the family table. This would help fuel marketing efforts by determining what type of advertisements to publicize on which platforms during this dinner time – and also let companies know what people are interested in while “bored” around their families. Ultimately allowing marketers to have the proper fluff appear in front of their consumers eyes – relaying this information to their surrounding family members and having it become a topic of dinner conversation.

A Thanksgiving question that could be answered using clustering is “What is the most popular time to eat Thanksgiving dinner across America?”. The data collected would be the time that people eat dinner, the geographic location.and age.. We would be able to see how different age groups or different areas of the country may have different tendencies in their Thanksgiving routines.

A Thanksgiving themed question that could be answered using clustering would be: How many families travel during the holiday versus how many stay local? The data collected would be geographic location they live, geographical locations traveled to, demographics such as age, size of family, married or single, After the cluster analysis, we will be able to tell if certain geographical locations have the most people leaving, which geographical locations have the most people coming to visit, if large families travel, if single people move away from home, etc.

One Thanksgiving themed question that could be answered using clustering would be grouping which regions of the U.S. watch Football during Thanksgiving which could give advertisers data for targeting certain markets. The data used would be how long they watch football, if at all, and which region they are a part of.

Grocery stores can collect data during the Thanksgiving season to see which days are the busiest shopping days and which store locations get the most traffic. They can create clusters on what days have the most traffic on a calendar and which areas on a map have the most traffic. From this data they can ensure that they order the correct amount of products and will have them ready for the busy days and don’t run our of stock. The grocery stores can also see what stores do not get a lot of traffic because they may be in a lower population density and make stock decisions based on that.

A Thanksgiving-themed question that could be answered using clustering is “Which regions in the USA watch NFL Football Games on Thanksgiving Thursday?” We would need to collect age, gender, and location of NFL viewers on Thanksgiving Thursday. Because these games are national broadcasts, it typically prevents regional advertisers from promoting during these games because they are unsure if their target region is watching. By collecting this data, an advertising company can tailor their advertisements to those that fit with who is watching the NFL games on Thanksgiving.

A Thanksgiving question would be that what type of alcohol do customers prefer with their turkey dinners? This could be done by looking at different liquor and wine stores in America and sales of the type that is sold the most in that season. The data collected would be from units sold, price of liquor, geographic locations and would give a deep insight on customer preferences. Furthermore, In big grocery stores like Wegmanns, we can see that how many people bought, what kind of alcohol as they were doing their turkey/ Thanksgiving shopping.

A Thanksgiving-themed question which could be answered using clustering is “What Thanksgiving-specific items (i.e. – food, cooking utensils, spices) are typically bought together?” To perform an analysis and answer this question, data about when Thanksgiving-specific items are bought and how many of each item is purchased at a time must be collected. This data will give way to an analysis that will give business owners insight on what items are most popular a few days leading up to Thanksgiving as well as Thanksgiving day itself. This will allow business owners to organize their displays and shelves based on the needs of their customers so they can find the items they are looking for with ease.

A Thanksgiving-Themed question that could be answered using clustering is “What kind of product is most purchased before and during Thanksgiving based on different regions?” This data can be collected for a chain supermarket where they can stock the right amount of each product so there is a right amount of inventory for each product to maximize revenue and consequently maximize profit. (wholesale prices for larger quantities may be cheaper and therefore the chain may be also be able to offer a more competitive price)

A Thanksgiving question that could be answered with clustering is: Which regions have the most Thanksgiving night shopping? You could collect data such as what times people go shopping after dinner, the region of the shoppers, and the type of household (ex. young parents with children vs. elderly with no children). This clustering would allow stores to better prepare for pre-Black Friday shopping, if they participate in it.

A Thanksgiving themed question you could answering using clustering would be “Which areas in Pennsylvania prefer turkey and which prefer ham for the main dish at Thanksgiving Dinner. An example of the type of data that would be collected to answer this question would be: types of areas (Suburban, Rural, or Urban), total sales of hams and total sales of turkeys in those areas, and the general demographics of the areas.

A Thanksgiving themed question that clustering could be useful for is, “How many families eat Ham instead of Turkey on Thanksgiving?”. The data that will be needed to complete this clustering event is who is eating ham and who is eating turkey. Also another piece of data that can further specify the clusters is why they eat ham instead of turkey.

A Thanksgiving question that can be answered by clustering is what kind of pies do different age groups prefer. The data collected could different types of pies typically eaten during Thanksgiving (pecan, pumpkin etc). The data can be clustered by age group (for example: people ages 10-15 prefer pumpkin pie) and this data can be used by companies and supermarkets to market the specified target audience.

A Thanksgiving-themed question that we could answer using clustering is: the size of thanksgiving party in relation to the demographic of the family? The data we need to collect is the number of family members, the annual income of each family as well as geographical location of the family. After the cluster analysis, we can see which groups have bigger thanksgiving party.

A Thanksgiving themed question that could be answered using clustering is “What region of the countries watch the most NFL football in relation to their geographic region?” This could be used to decide the location of where the Thursday night game should be broadcasted next year. The data to needed to be collected would be in which region are there the highest ratings for the Thursday night game and what metropolitan region with an NFL team are they closest to in proximity.

A Thanksgiving-themed question using clustering could be Which side dishes and sweets with turkey (stuffing, sweet potatoes, and pies) are most popular in each region of the different age of people? The data we need to collect the geographic location, different age of people, what kind grocery people buy during Thanksgiving, an interest of different age people. These all data would help to give the answer of the question

A thanksgiving themed question could be what state prefer online shopping on Black Friday rather than go to the mall. The data needed includes the shopping record differentiated by states, numbers of online orders and in-store purchases. This cluster analysis can help brands to assess marketing strategy, determine whether they should focus promoting Black Friday sales and advertising in stores or online.

A Thanksgiving themed clustering question can definitely be related to food, the most important part of the holiday. Using clustering, we can evaluate what dishes families eat based on ethnicity. For this example, we could evaluate the main roast, one favorite side dish, and one favorite dessert. We would have to collect data about people’s background, such as where each parent came from and where they were raised. Then we data could be grouped and we could see if people with the same background eat the same kind of foods.

A Thanksgiving-themed question that can be answered using clustering is “Based on US geographic regions, who is more likely to cook their own Thanksgiving meal rather than going out for the holiday?” The data collected includes the geographic regions in the US, where people spend Thanksgiving, age, relationship status, number of children, and income level. This data could identify which region has the most individuals that cook their own Thanksgiving meal, as well as whether families with children are more likely to cook.

A Thanksgiving-themed question could be what people from different states prefer for their sides. Data needed would the sales of the sides, the different regions of states, and some demographics. This type of data analysis would be very useful for a supermarket to know how to appeal to their shoppers in regions and what to stock up on.

A Thanksgiving question involving clustering would be：How many pies were bought based on flavor? and which one is most popular in Thanksgiving. Collecting date with how many Pies were sold in total during Thanksgiving, and what are the spice type of Pies has been sold. From this data, we can know clusters represent which flavor is the most popular one for the Thanksgiving season (pumpkin, apple, etc.)

A thanksgiving-themed question involving clustering is what is people’s favorite side dish with the main meal. The necessary data that needs to be collected includes the various amount of side dishes (like mac n cheese, mashed potatoes, etc.) that are traditionally served with the Thanksgiving turkey OR ham.

A question that could be used for clustering regarding Thanksgiving could be “Who is more likely to cook thanksgiving rather than ordering it pre made” We could look at geographic regions, and also include Black Friday along with the data. It could be different types of familes, and what they traditionally do for thanksgiving. Some familes cook thanksgiving by themselves, and others buy it pre made to make things easier. The families who pre make them could do that so they have more time for other activities. The clustering strategy could include different regions, traditions, and what regions are more likely to be open earler for black friday.

The Thanksgiving question involves the clustering would be which states in the US like to travel to where during Thanksgiving holiday. Ranking the top 2 places that people from each state that like to go to. The data should be grouped by state and to see what is the most popular place for people come from the same state.

A Thanksgiving related question that clustering can help to answer would be something like which countries around the world celebrate Thanksgiving or similar holiday. There are many other countries that celebrate thanksgivings that are either different days or different name. By, finding out the region and when it is celebrated a company can look into the clusters and find out which countries to supply the most items. The data to collect would be just finding out how many families celebrate Thanksgiving.

A Thanksgiving-themed question using clustering could be: What are the most common food items bought during the week of Thanksgiving? The data needed would be the sales of specific gricery stores, when they are bought, and how much quantity they sell for. This will allow grocery managers to stock up on certain products.

During the Thanksgiving season, many supper-markets with or without deli food departments offer turkey, chicken, sea food (such as shrimp, crab, lobster, etc.) duck, beef, and pork to make traditional and non-traditional Thanksgiving dinners. A Thanksgiving question involving clustering would be which local/regions in America like which kinds of meat and/or seafood customer prefer to have in their Thanksgiving dinners. Using the past data of sales or collecting the current data of sales helps supper-markets predicting the quantity of each type of meat and seafood items could sell and the trend of customer preference in the future caused from the diversity of population.

A Thanksgiving-themed question that could be answered by clustering is which style of turkey preparation is most popular in different states in the United States. The data that could be collected is location, whether they eat turkey (some households cook ham), method of cooking turkey, and number of people attending Thanksgiving party (could possibly effect cooking method).

A good Thanksgiving question you could answer with clustering is which regions of the US see the most prethanksgiving travel? Thanksgiving is notoriously one of the biggest travel days of the year. By figuring out which areas of the US see the most out of town travel, local governments could better prepare for traffic influx, while empty towns could ease up slightly. It would also help airline companies know where it was crucial to send airline crew to better cope with the inevitable travel troubles that always seem to plague thanksgiving.

I’ve come to realize that some families opt out of celebrating thanksgiving, so an interesting Thanksgiving-related question that can be answered by clustering is “which culture/ethnicity celebrates Thanksgiving ‘the most’?” The data to be collected includes the family’s background (which country/countries do the family’s ancestors come from?), length of residency in the U.S (how many generations have lived here?), religion, and state of current residence. The clusters would hopefully show that certain combinations (Catholic, 5-generation Italian families in NY) celebrate this holiday more than others.

A Thanksgiving question that clustering could answer is which product has the most purchases during Black Friday – Cyber Monday shopping. Data should be collected include what products people buy during that period and the amount each person buy for each product. This clustering would help retail stores like Best Buy or Walmart better prepare for Black Friday shopping.

A Thanksgiving-themed question clustering could be used to answer is ” What demographic of people naps for the longest period of time on Thanksgiving afternoon?”. The data needed for this cluster would be the length of naps from people on Thanksgiving day. You would also need their geographic location as well as their age and gender..

An example of a business question that you could try to answer by using a decision tree could be, “How do customers find our business’ website?”. A decision tree could help find out how likely people find the website through an organic search or by clicking on an ad, to better understand the traffic that comes to the website. This could also help the business change their marketing strategies to reach more people through different ways. The data that would need to be collected will have to do with what percentage of people find the website through different ways as well as defining what those specific ways are (i.e. Google search, ad clicks, etc.). The advice I would give someone regarding how to select the right predictor variables for a decision tree analysis would be to make sure you look at all the traffic coming to your website so you have a large, representative sample size that is very accurate to which means of traffic is most popular.

A business question you could answer using a decision tree is: “How can I predict whether a student will apply to Temple”? This question would help Temple University determine what it should be doing more/less of to effectively reach its target demographic. The following are examples of data that can be collected to perform the analysis: Student GPA, # of Student’s Extracurriculars, # of Student’s AP Courses, etc; essentially any data that describes a student. The advice I would give someone regarding how to select the right predictor variables for the decision tree analysis would be to choose predictors based on whether they maximize distinct outcomes and separate outcomes. I would also advise them to use a substantial dataset (ex: all of last year’s incoming freshman applicants) for data collection.

A business decision that could be answered with a decision tree is determining whether a business should make an investment or not. The expected loss/gain for the investment depending on certain circumstances (good or bad economy, amount of investment) can be determined with a decision tree. This can help a business determine the probability of an investment or expansion being worthwhile. If the expansion is profitable when the economy is good or bad, the company should invest. If it is not profitable no matter the circumstances, investment should be held off. My advice for setting predictor variables would be to set the most important variables early on in the tree, try and have a large sample size of the predictors to see how likely they are to happen, and to find accurate probabilities of the various circumstances.

One business question a decision tree can answer is whether someone will book first class flights. The data we should collect to perform this analysis includes the age of people, their annual incomes, genders and jobs. When selecting the predictor variables, we should think about if the predictor variables are relevant to the problems or if they contribute enough to differentiate the outcomes. We can use the Chi-square test to check if the predictor variable is important enough to create different groups.

A business question that you could answer using a decision tree is “Should the company book flight or train ticket for a team of employees?” This question will help the company to determine what is the most efficient way and also can save more money for the company. The data should be collected are the prices of flight and train at same time period, the time of flight and train from origins to destination etc. The most important variable is the price. The company should make a budget of how much the maximize price could be acceptable, and then compare this two methods’ time whether it’s appropriate or not. My advice is to set the most important variables at first to filter any other unacceptable choices. And then set relevant variables to make a better comparison.

A business question, not discussed in class, that you could answer using a decision tree would be: “how likely is it that someone who buys product A is going to buy product B?” This could help with providing recommendations on e-commerce sites, such at what Amazon does. Data to be collected could be: how much people spend on their initial product, the accompanying product(s) costs, what items are bought together, demographic of people that shop for certain items, time that items are bought and how often the person shops on the site and for particular items. Anything that describes specific buying behaviors of one customer could eventually show how other customers will behave. My advice for choosing predictors would be to use predictors that are more easily quantifiable. Additionally, I would tell them to choose predictors based on the data they collect. Also they should continue collecting large samples of data based on predictors that will show a relationship between consumer behavior and the prediction.

One business decision that could use a decision tree would be recruiters looking to hire new graduates. The data they would collect would be GPA, school, and major. Although the decision tree would be extremely big with many different nodes, the system would be able to automate the decision tree process for every candidate making it much easier for the recruiter to sort through all the applicants. If it is for a specific position, the first set of decisions would be for major, but if it was more general, they would maybe want the first set of decisions to be on GPA.

A business decision tree example: For a auto insurance company, whether they should offer a person insurance plan. This question helps the company manage business risks. Variables in this tree includes the person’s age, gender, occupation, license’s history/length, driving score/experience, etc. The insurance company will determine whether to make this person become their member. Also, this tree can further help the company to calculate the premium of an individual. Depending on how the customer’s background is, the system will set different levels of premiums.

The business question to be answered is whether a college student will play volleyball based on any of the following combination of variables: Age, Gender, GPA, number of courses taken in the current semester and fitness status (active = 1 or not active = 0). A decision tree will provide a simple illustration of branching and splitting of these predictor variables to determine the likelihood of a college student playing volleyball. When choosing the predictor variables, be sure select variables that are relevant to the analysis (for example, do not use the student’s TUID as a predictor variable). It is also important to select variables that give insight to the outcome (not every variable may be a useful predictor variable to explain the outcome of a business question).

A business question you can answer with a decision tree is: “Should we open a new restaurant in this location?”. This question would help a restaurant owner who has a few locations, but is looking to expand to add more stores. Some of the data I would collect is .the population of the zip code being considered. This would be a good indicator of potential customers. Another piece of data I would collect is the number of restaurants currently in business in the location. Household income and number of people living in each housing unit. The advice I would give to this business is to choose a location with a high population so that there are customers, a high household income so that there are people willing to spend, and a zip code with a lot of families because busy parents will want to take their families out to eat.

A business question that could be answered is determining whether a customer should be able to get the Chase Sapphire Preferred card or American Express Gold card. This question will help credit card companies qualify the right customers. The data to collect for this analysis includes having a credit score above 700, making sure you don’t have too many credit cards open, and your credit utilization. Some advice I would give to pick the right predictor variables include having a large sample size to pick from because it will produce the best outcome on how to pick the right applicants.

A business question you can answer with a decision tree is: “Should we build a stadium in this location?”. This question would help a city decide whether or not a new stadium would be good for the city socially and economically. There are many factors involved in this question, many being political, but the best approach starts with gathering data on the proposed area. I would collect data on the population of the area in which the stadium would be built. Another piece of data to be collected would be the number of businesses in the area’s location. I would tell developers to build in a location that needs economic help and a smaller population so those residents aren’t forced to relocate.

A business question not discussed in class that a decision tree could use is “does this individual qualify for our job listing?”. There are many questions that get asked on a job application. By using a decision tree, if would make it much easier for the hiring manager to sort through the data from the applicants. The root node could be application submitted and then the tree could split on major questions such as “legally allowed to work in the US”, “Not convicted of a felony”, “GPA higher than 3.0”, “major”, etc. The nodes would eventually get to the leaf node which would be “invite to interview or don’t invite to interview”.

One question that can be answered with a decision tree is the chances of an insured getting into a car accident in a auto insurance company to effectively price the premium based on the risk that each person is assigned to. Information such as age, gender, years of driving, is there involvement in accidents in the past, the type of car, the color of the car, the region that the insured live, etc would all be data needed to perform the analysis.

To select the right predictor, I think it would be important to know what you’re asking for. In this case, it would be the risk (how likely the insured would get into an accident) of this person based on the past data.

A business question that can be answered using a decision tree is “How can I predict how successful a 7-Eleven will be in a certain location”. This question would help 7-Eleven corporate decide on the best locations to expand their convenient stores. Data collected would include population, age ranges, number of similar convenient stores near the location, income, and whether or not they own car. This is a good indicator of potential customers in the area, how competitors could affect their success, if there are households with high incomes that can afford to shop at the convenient stores, and if those individuals can get to the store by car or not.

A business question that can be answered using decision tree is whether an athletic shoe retailer can do to raise sales. To raise sales there are multiple options: increase advertising, cut prices, launch royalty cards, etc. The data to collect for analyzing would be the number of customers after each option being executed, total sales and total costs after each option being executed. A good advice to pick the right predictor for this analysis would be gathering data in the right demographic; for example, teenagers and young adults are the most frequent athletic shoes consumers so gathering data from them can return better results than gathering from older ages who do not consume athletic shoes that much.

A business question that you could answer using a decision tree would be what types of applicants would a company accept for a job position. This would help the company to figure out which resumes and applicants to put through to an interview, and which do not fit the qualifications for the position. The data the company would collect to decide this would be college attended, major, GPA, work experience, and leadership experience. If the students GPA is not over a 3.0, they might be automatically thrown out, unless they went to a prestige University or have a strong work experience background. The advice I would give someone in regards to selecting the right predictor variables for the decision tree would be to allow as many applicants as possible in order to get the largest pool of applicants. The more applicants, the greater the chance that the company will get the strongest employees with the qualifications that they are looking for

A business question one could answer using a decision tree is as follows – does the following candidate qualify for a car lease?
The variables you would see within this tree are: Name, Address, Age, Income, Car Insurance Provider, Accident History, Employment Status (Yes=1 OR No=0).
The decision tree will utilize these predictor variables by “branching” and dividing up these categories in order to determine whether the applicant qualifies for a lease.

Important Note: When choosing variables for the decision tree be sure select variables that are relevant to the analysis (choosing “Race” has nothing to do with applying for a car lease, so it would not be a valid variable. It is important to choose variables that will help predict/determine the outcome to whatever situation you are analyzing. For this example in particular, an applicants “income”, “accident history” and “employment status” would tell us tons about whether this individual is a reliable person to lease a car too.

A business question that could be answered using a decision tree is “How likely is a customer to subscribe to our e-mail newsletter?” A decision tree could determine which customers are most-inclined to join the newsletter based on a number of different factors. The data I would collect to perform the analysis is gender, age, purchase history, amount spent on products, or any other data that would indicate how loyal a customer is to our brand. In terms of advice on selecting predictor variables, I would encourage them to use a large data set of customers to gain an accurate tree, as well as only using predictor variables that increase accuracy of the tree by a significant amount.

A decision tree could help to answer the question “should I buy this used car?”
The variables in this decision tree would include amount of mileage, year of car, make/model, price and what kind of condition it is in. The most important thing when choosing variables for a decision tree is to make sure they directly relate and are relevant to the question that is being asked.

A business question that you could answer using a decision tree is the factors that cause consumers to buy more organic options. In order to answer this question, I would need to collect data about the consumer’s monthly household income, the age of customers, the amount of individuals in the household, and the gender of the customer. In order to think of the right predictor factors to use for a decision tree, you have gather data that you believe will indirectly or directly affect the decision tree. For example, age is a big determinant of whether not an individual will buy certain products or engage in specific activities. As a result, age has a positive correlation to differing preferences in buying organic products.

A business question that can be answered using decision tree is which stock is more valuable for investor. For investment, there are multiple options: Stocks, MF, Bonds and etc, all the useful information are collected for analyzing what the optimistic expected value and what the worst expected value. Adding all the expected value up, and compare all the choice, then pick the most valuable one. For example, the stocks growth rate 40% and is the highest in all the choice, and declining rate only 13%. relatively lower than others. under the same probability, most people might choice the stocks as an investment.

An business question that can be answered using a decision tree would be if a car manufacturer is thinking about what kind of car to launch? if they want sedan,suv,truck,etc… First they would do market research and look for what is most demanded. And also finding out how much people are willing to pay for the model, which can include if the people want more of luxury or just simple car. The advise i would give is to follow with what people want. If every one wants a compact SUV then the best choice would be to choose that option. Also find the accurate number of demand.

A business question for NBA scouts could be “What is the likelihood of a certain player becoming an All Star in the league?” Based on data such as age, height, points per game, assists per game, rebounds per game, and plus/minus rating, a scout could use a decision tree to determine how good a player will be at the professional level. Some advice I would give to pick the right predictor variables is to choose variables that have a direct impact on winning games, as opposed to less important variables such as how well a player played on a certain day of the week compared to others.

A business decision that could be answered with a decision tree is ”which shoe brand people buy most between Nike and Adidas.” By using tree decision tree we can find out the average of the people which shoe brand they wear more. Data should be collected on how many people wear Adidas and Nike shoes, how often they go to store, and how much time they spend on buy shoes. The most important variable is player fans, how many people follow the players who Nike and Adidas. The advice I would give to The advice I would give someone to chose the predictor variables for the decision tree analysis based on the data. The should collect data from the busy location stores, and data of people followers their players, and they should collect large data to get a better result.

A business decision we can solve by using a decision tree is “Who is performing well enough to receive a promotion?”. A decision tree can help solve this because it will show who is performing the best, who is performing moderately, and who is performing the worst. Some data we would collect for this decision tree would be their time sheets, performance, teamwork, and evidence from other employees. Each of these things can prove that the certain employee can indeed receive a promotion. Advice for people to choose predictors in a business tree is to choose the main ideas that you want to show. Main ideas and important details are a good indicator of picking the predictors.

A business question that can be answered using a decision tree is whether or not an insurance carrier should write coverage for a workers compensation risk for a potential client. The insurer should look into past injuries in the workplace, the nature of the client’s business (for example: if it is a construction company, it is riskier to write coverage because the likelihood of workplace accidents is larger), and safety regulations the company has in place. It would be beneficial to the insurer if it used a large data set (loss history spanning across multiple years) and predictor variables that increase the accuracy of the tree.

A business question that can be answered with the use of a decision tree is whether someone qualifies for a car rental. The data that can be collected is age, gender, credit score, income, occupation, and criminal history. The advice I would give someone regarding how to select the right predictor variables for a decision tree analysis is to make sure that the chosen variables will give valuable insight to the business question and that the variables have a strong correlation.

A decision tree can help answer the question “should a business outsource its tech support?” The data that needs to be collected includes number of support tickets generated per month, whether issue requires remote or in-person support, and length it takes for IT team to solve issue. A business that has a large number of tickets that require longer assistance and/or in-person assistance would result in ‘yes, outsource’, My advice is to choose predictors that are statistically different and can further divide and relevant to the question.

The business question that could be asked with a decision tree is: What type of a car customer can purchase?
It will determine the factor of levels of income ,age, models of the car and the price. Decision tree can help an individual to make a decision effectively and consider all the options.

A business question that could be answered using a decision tree is “Which track will an advertising student choose?” A decision tree could help a university, such as temple, determine which classes a track a student will decide on in their sophomore year. From this the university can determine how many teachers to hire, classes to run, etc. The data that would be collected would be the gender, age, classes taken previously, grades in classes taken previously, GPA, credit hours completed, which extra curricular clubs they are members of, major. My advice would be to select a large sample size and to choose the correct variables to lower your risk of inaccuracy.

The business question to be answered is whether a person will pay for public speaking online course based on any of the following combination of variables such as Age, Gender, Educational level, Income, Job title, and Taking course (willing to pay=1 , not willing to pay =0). A decision tree will provide a simple illustration of branching and slitting of these predictor variables to determine the likelihood of a person who is willing to pay public speaking online course. When choosing the predictor variables, make sure to select variables that are relevant to the analysis.

A business question that can be answered using a decision tree is whether an insurance carrier should write a workers compensation risk for a potential client. The insurer should take into consideration previous losses the client has faced in regards to work place injury, the nature of the business the client is involved in, and safety measures the client takes. It would be beneficial for the insurer to use a large set of data (loss history spanning across multiple years) and use predictor variables that give the tree more accuracy.

A business that could be answered using a business tree is for when a tour manager of a band is planning a tour. The tree could be used to figure out what cities to play, what venues are in that city, the capacities of those venues and if the demand for the artist is high enough to have a tour date there. This could easily determine for a tour manager what cities have the demand and available venue options for a band to play in that city. The advice I would give someone trying to do this analysis is be as specific as possible with the inputs and make sure to only use the information that applies to the question you are trying to answer.

An example could be for a restaurant on whether or not a customer will also purchase a drink once they bought a sandwich. The data needed to perform could be age, sex, location, order price, and order quantity. An analysis like this could be beneficial for marketing purposes for a store to boost sales. Selecting the right predictor variables in this scenario is crucial because one needs to make sure the predictors are relevant to receive more accurate results.

A business question that could be answered through a decision tree would be how likely is it for a student to retake a particular upper level coruse. The data that would be put into this would, the number of students taking the class, their GPA before entering said course, and the grades they receive throughout the course. The right predictor values for answering this question would be the grades students recieved throughout the course.
This is because, if a student with a high GPA, before coming into the course, and is also getting high grades in said course has a higher chance of not retaking the course.

A business question that could be answered using a decision tree could be “Is it worth investing in a companies stock?” The data that can be collected and used in the decision tree are variables including, the value of the stock, YTD change, dividends, and quarterly revenue of the company. One piece of advice I would to someone selecting the right predictor variables would be to avoid variables about the company that do not have an effect on stock price. For example stock listings or CEO’s name as these do not have an effect on the value of a stock.

A business question that could be answered with a decision tree is whether or not someone was likely to buy a ticket to a sporting event. Good predictor variables for this would be whether or not they’re sports fans, how many hours of sports content they consume a week, what sports they’re fans of, and if they live in a major city (with a sports team) or not.

A business question that could be answered by using a decision tree could be the addition of products to a grocery store. The grocery manager would then factor potential vendors based on their product’s previous success rate and reputation. The decision tree would collect data from vendors like Herr’s for example. This data would include the consumer satisfaction of their products on a scale of 1-10 along with reputation towards other companies they do business with, this would be rated “Poor” , “Neutral” , or “Good”. Another set of data to be included could be costs for transportation and products. All of these variables are important in selecting the right vendor, but the most important variable in my opinion would be product’s success rate with customers.

Decision tree could help us determine which kind of restaurant operation will go bankrupt or keep on operating after 1 year. For example, the decision tree could find the location of restaurant who make it and go do not, the kind of food they sale, the operation time ( lunch/dinner), the price range, investment spent on restaurant. All these decisons will make or break a restaurant success.

A business question that could be answered using a decision tree is whether or not students will take a certain course or not. Several predictors could be based on the financial aid status, they’re need of the course and the time frame of when they could take it

A business example for using a decision tree could be finding out what demographic is most likely to go on cruises for a travel agency so they no who to market to. They would want to collect data about a sample groups, age, income level, marital status, children, past cruising experience, and how often they vacation. For advice for selecting decision tree variables I would suggest using something that you believe directly will influence the results of the decision. It is better to add too many and have R figure out what is important then have too little and miss a key insight.