Lawrence Dignan

Data is everywhere and is rapidly becoming an integrative part of our lives. Learning to harness the power of data is vital for our society to succeed in the future. I would say to a future MIS0855 scholar that this course is about learning how to use data to effectively to solve problems.

Data can be found everywhere. It can be the time from a digital clock or the number of bottles save by using the water fountain. Data however is not knowledge. Knowledge is when we use data to interpret the situation.

If i had to describe this course, I would describe it as an important course to take at some point of everyone’s college career because it teaches you how to find, read, and manipulate data into something you can use. In today’s age data is everywhere, and knowing how to use it for your own benefit is very important. Most jobs in the future will require you to use data sets, and this course is perfect for understanding the basics of how they work.

The most important takeaways I had from this course were how ubiquitous the collection of data is combined with how much data tracking and analysis have become integrated into our everyday lives.

If I were to explain this course to someone I would say it teaches you useful quantitative analysis and data visualization skills that one could apply to a future job. I would also say that it helps students understand the different ways in which people utilize data, how data analysis/collection has changed throughout the years along with the ambitions people/companies have for the future of data and information technology.

The most important takeaway from this course was learning about data visualization. Since I will be working with large amounts of data in my job, I am glad to have learned some different techniques and things to avoid when presenting data. For a future scholar, I would tell them that this course is an introduction to data science that will give them various tools to effectively handle data.

We live in a world where data is accessed through everything. However dirty data I thought was the most biggest take away. Just from a simple miscalculation on even one data entry could mess up an entire data base. From taking this class, I understood what to look out for and how to prevent and clean dirty data.

The most important thing that I learned from this class would be that data is everywhere and affects our daily lives.

The most important thing was by far learning how to use Tableau. The enjoyed this project because I am a visual learner, and creating informative infographics out of scattered metadata was fascinating. I would recommend any business major to take this class for their science and tech gen ed because writing that you know how to use tableau in your skills section of your resume, can impress recruiters.

The greatest takeaway I have from this course is that data is almost like a form of currency. It has endless applications if utilized correctly and can shape anything from sales to presidential elections. I would tell a future student that this class is all about data applications and a whole lot of tableau.

My greatest take away from this class was gaining a firm understanding of the basics of Data Science/MIS. By actually putting the skills we were learning into use every class, I was able to easily understand the importance of what we were learning and how it was applicable in day-to-day, professional situations. I would tell future students considering this course to definitely take it if they are pursuing a career in data science and/or financial analytics, as this course will provide valuable insight into the field.

The most important take away from this course is understanding that data is absolutely every and in everything that we do. I also enjoyed learning how to use Tableau.

My most important takeaway from this course is the ability to gauge data and think about each resource critically. I’ve learnt that data is collected and used everywhere, so I should be more careful with how I use Internet – try to use incognito mode and delete cookies more often. It was also fun to play around with tableau – I am not sure I will encounter it again in my profession, but I am glad I learnt how to use it.

For a data-driven service like Amazon.com, data storage would be very complex. If you were to put it in an excel sheet, each row could represent a transaction on their site. Columns would include the product, product description, seller, buyer, price, shipping information, and time of transaction.

A data-driven service I use daily would be Instagram. Some of the columns would be name, data, picture type, amount of likes, Amount of followers, Amount of posts, comments, location, tagged people, ect.

A data driven service like blackboard. A row would be the course name. some of the columns would be assignments, grades, readings, class activities.

The course evaluation for each classes can be a data-driven service. The columns should include course name, the professor, ratings, and students review. The row should include answers for each columns.

A data driven service like amazon would be organized in an excel spread sheet. Columns would include things such as viewed products, and their prices. And the Rows would include entities like people’s names.

a data-driven service like canvas would be organized in an excel spreadsheet. Columns would be the course you have taken and the rows would be the grade you received in that class, your professor evaluation, and the credits earned.

For a data-driven service like Amazon, a row would be a look through of an item.
Columns would be:

Product’s name
Manufacturer
Was it was ordered
Username of the person looking
Price

A data-driven service that I use is LinkedIn. In their data columns you could find:

People who have viewed your profile
Connections
Searches appeared in this week
Jobs Applied To
Saved Jobs
Career Interests

A data-driven service I use daily is Instagram. Their data columns would be: followers, following, people who you follow that don’t follow back, people that follow you that you don’t follow back, followers’ engagement rate, most liked posts by month/day of the week/time of the day, most popular posts by type of content – photo or video, most commented posts by type of content.

A data driven service that I use is Canvas. Canvas is a school tool similar to Blackboard, and the data rows would be grades, assignments, tests, quizzes, professor, course name, student ID, and activities.

A data-driven service that I use regularly is Instagram. If I was to store this data, a row would be the user. The columns would be followers, following, likes, and pictures.

Amongst a world of a large amount of different data-driven services, I often use SoundCloud. SoundCloud very basic to navigate and also has decent visual layouts. I personally use this music sharing website to release some beats that I may drop as sneak peaks. Some data columns I’d format into the data would be Followers, Following, Tracks, Likes, Trending, and even comments. These would all be placed into data columns that would represent SoundCloud data to a very certain extent.

A data-driven service I use daily is Canvas. Canvas stores all my work information from my classes and all grades, files, and spreadsheets from professors. If my data from Canvas was put into a spreadsheet, I would break into rows my overall class grades, test grades, assignment grades, etc., and also personal information, such as professors’ and classmates’ e-mails and other contact information. This spreadsheet, however, would function mainly as organizational platform, rather than serving any real analytical purpose.

I’ve been looking for a new apartment in Philadelphia and I’ve been using Trulia.com. To store the data on Trulia in a database, I would designate each row to be an individual listing with the address. Columns could be the location (neighborhood in Philadelphia), price, number of bedrooms, number of bathrooms, washer/dryer (yes or no), central A/C (yes or no), dishwasher (yes or no), utilities included (yes or no), crime rating, walkability rating, and Trulia has a feature called “Near SEPTA” so I would also make that a “yes or no” column.

For Spotify, some columns could be:
Song name
Artist
Album
Genre
Number of plays
Number of likes

Sports Injuries

Jonathan Quagliarello, RIchard Beckman, Khuong Tang, Claudio

2016 Presidential election voter turnout compared to 2012, focusing particularly on registered Democrats.
John Hammerschmidt, Alex Schlegel, Ryan Echersley, and Badr Abderrazak,

Baseball statistics. Probably going to focus on hitting. Likely to involve hitter’s exit velocity.
Brennan, Lilly, Mazurok, Patel, Samson

How has the growing use of Uber and Lyft affected the rate of drunk drivers in U.S. cities?

Patrick BIschke, June Choi, Rico Le, Colin P Mea

Our group will be measuring which statistics are most valued in the NBA concerning different award races throughout the league such as MVP, rookie of the year, and defensive player of the year.

group members include ross and max

https://www.forbes.com/sites/johnnosta/2018/04/03/data-and-its-real-demons/#21d5c7472985

This article is interesting because it recognizes that often data can be misused for the wrong reasons. Then, it shows the good sides to data. The article argues that the most important use of data is medical data. It has the ability to save lives, and the way we utilize the information to better our medical world.

https://www.forbes.com/sites/celiashatzman/2018/03/09/popsugar-is-launching-the-makeup-line-of-millennials-dreams/
This article is about how a media and technology company, PopSugar, used data to create its own line of beauty products. Makeup is a passion of mine, and I never think of it as something that has to do with data but more as an art form. It will be interesting to see if this brand is successful.

https://www.economist.com/news/britain/21739993-despite-its-flaws-new-obligation-could-spark-change-employment-practices-forcing

I think this article is interesting because of the way the way the data is presented. The article goes on to describe the difference in gender pay gap in the U.K. and illustrates a chart showing woman’s median earnings as a percentage of men’s median earnings. It then states that the data is not adjusted for different employee roles. A data set describing differences in pay gap can’t be taken seriously if it includes secretaries with executives.

https://www.forbes.com/sites/julianmitchell/2018/04/05/this-data-driven-rental-platform-makes-finding-the-perfect-apartment-quick-and-easy/#2903e4683ddc
This article is interesting because it changes the way an agent personalized your apartment search to a machine personalizing your search with much more in-depth explanation than your typical agent. This app also utilized agents to help renters but it speed through the process with the help of the app’s in-depth data.

https://www.economist.com/news/science-and-technology/21739644-securing-data-networks-new-ways-trade-data

According to Cisco, an American technology group, the amount of of data that passes through the internet amounts to more than a zettabyte each month. Usually, data deals are at between the holder of the information and those who want the infomation. Companies like Fetch, IOTA, and Uber are creating new ways to trade data safely. This article I found talks more about it, because I use these new applications to get information about where I am headed.

Artificial Intelligence and Machine Learning Awaken

Hal Lonas, the Chief Technology Officer at Webroot, goes into detail about how artificial intelligence and machine learning are being applied in the world today and how it will affect the future of data and businesses. The most interesting thing I found in the article talked about how AI and ML are nowhere close to taking a vast majority of human jobs do to the machine’s lack of adaptability. It can use probability and statistics to suggest an outcome, but these suggestions don’t work well for individual tasks.

I found this article interesting because I did not know that the worst thing you can do with data is simply just throwing it out. I also learned that the problem with that is that not all of the data can be stored. Organizations well outside the Fortune 500 realm that used to be gigabyte scale data storage shops are now petabyte-scale storage shops, facing challenges they never expected. More and more businesses will need high performing systems that will store and protect large quantities of data over long periods of time.

The Future of Data Storage

The article talks about the data breach of the nation’s 2nd biggest airline carrier, Delta. There was a cyber attack on a vendor that runs a chat function on the carrier’s website. This happened to the Sears as well. Delta responded that it would cover any unauthorized transactions resulting from the breach. It is highly possible that many incidents like this will happen often in the cyber era.

source:
https://www.wsj.com/articles/delta-says-hack-on-vendor-exposed-customer-credit-card-data-1522965773

https://www.cbssports.com/mlb/news/as-a-baseball-fan-gabe-kaplers-approach-presents-a-real-problem-to-me-and-heres-why/
I am not a huge Philadelphia sports fan, but recent events regarding the Phillies have certainly caught my attention. Gabe Kapler, who I must admit, I was a huge fan of (considering he was an extremely successful Director of Player Development within the Dodgers organization, which is my favorite team), and I was rather optimistic of a marriage between Kapler and the Philadelphia organization. In the wake of his brain-gaffe in the opening series, I am much less optimistic. The article outlines Kapler’s analytical approach & style of management.

https://fivethirtyeight.com/features/a-fast-fastball-isnt-enough-anymore/

This article looked at the evolution of hitting in major league baseball, and how hitters have become more adept at hitting fastballs clocked at over 95 mph. The article looks at data that looks at the annual amount of pitches thrown clocked at 95 mph and above since 2009, and the league-wide OPS (on-base percentage plus slugging percentage) in total and against pitches clocked at those speeds. It identifies a growing trend where players have grown more accustomed to facing higher velocity and adjusted to this trend. I found it interesting that OPS against pitches clocked at 95 mph+ grew .036 points since 2015 to .734 in 2017, and continues to grow. It also highlighted some notable transactions that took place this offseason that accounted for this growing trend and hitters ability to catch up with rising pitch velocity.

How Data Analytics Contribute to Business Growth

This article talks about how data analytics affect business growth. This article was interesting to me because it showed how analyzing data can improve the growth of a business. It first started off talking about having a plan on how the business will be able to grow and what opportunities will arise. Next, it talked about marketing and customer reach which is very important to grow a business. Data analytics can give a company certain numbers based off of their customers to improve their advertising and improve the business overall.

https://www.baseball-reference.com/teams/NYY/new-york-yankees-salaries-and-contracts.shtml

This data shows the salaries and contracts of the New York Yankees. I am a big Yankees fan. This year, we are ranked #7 in highest salaries in the MLB and that is the lowest we have been in a while. Usually, we are #1. This means that we are not spending as much money on players especially since we are a younger team than we used to be.

https://fivethirtyeight.com/features/the-trouble-with-leaving-facebook-is-that-we-like-facebook/
This article by FiveThirtyEight takes a look at the Facebook data scandal, and how people feel about their personal information. A survey collected that 91% of adults thought that consumers had lost control on how companies collect information. It would later show that information such as social security numbers and health care are data that people are most sensitive of getting taken. Amidst everything going on with Facebook, this article is interesting in looking at how users see the scandal despite staying on Facebook due to the Privacy Paradox.

This article is about the push to increase data literacy among diplomats and federal employees working for the State Department, Health and Human Services, and Department of Commerce. As the author explains, this push has risen from the demands of a more data-driven world in which numbers hold more value than words alone; diplomats have the ability to substantiate their claims, extend their influence, and justify their proposals when they are data literate which furthers their agendas. They learn these skills in newfound workshops, seminars, and courses.

The State Department wants to teach data literacy to diplomats

Datasets for teaching data science

I found this article interesting. Its a collection of data sets for teaching data science. I think I liked this article because I feel a connection since I am taking a data science course. The collector of the data for these data sets is Rafael Irizarry and he says, “my experience has been that finding examples that are both realistic, interesting, and appropriate for beginners is not easy.” Irizarry makes data science accessible to everyone by finding examples of data sets that accommodate mass consumption.

Crazy for March Madness Data

This article talks about all the different data that goes into March Madness. I found this very interesting because I am a huge basketball fan and I fill out a bracket every year and know every year there is basically no chance it will be perfect. It is something that has never been done and more than likely will never be done. Some of the big upsets ruined thousands of brackets alone which cost people a lot of money. I think it is very interesting to see how many different possibilities there are and people choosing them and no one will ever get a perfect.