Unit 9

Lesson 1

1.What time of year do people tend to search online for chocolate? My guess would be that most people tend to search online for chocolate during Halloween as that's the time when candy is given away en mass to people who decide to go trick-or-treating.

2.How could you check your guess? I could check my guess by searching it up on the internet or by googling it.

3.What pattern do you notice? What could be the reason for that pattern? Does this data support your earlier guesses? I noticed the pattern of chocolate being sold largely in the weeks leading up to October and the weeks leading up to February. The reason for this is that during October, Halloween takes place and during February, Valentine's Day takes place. Thus, this data definitely supports my answer as I identified the period before Halloween as my answer. 

4.What is the metadata for the chart you created in Google Trends?  The metadata is increasing as the Google searches are going up

Lesson 2

1.Why do people make visualizations out of data? People make visualizations out of data as data visualizations help to tell stories by curating data into a form that is easier to understand which in turn highlights the trends and outliers. A good visualization tells a story that removes the noise from data and highlights useful information.

2.Which of these makes it easier to understand the data? What do you think the "histogram" is doing to visualize the data differently?

Histogram. because it is making the data more organized and putting them in a range making it easier to read

3.Which steps of the Data Analysis Process did you see in today's activity? Where did you see them?

We used "Collect or Choose data" to find what we wanted to graph in the "Dogs" dataset.  "Clean and/or filter" was used to filter the unnecessary data we didn't need.  "Visualize and find patterns" was used to find how we wanted to represent that data in a readable and organized manner. "New information" was the things we learned from sorting the data.

Lesson 3

1.Discuss your charts with a partner. What problems came up when trying to create these charts?  What problems do you see in the data?

It is showing extra data that we did not need for the chart and so the charts came out unorganized.

2.What if I only want to look at a subset of my data? How could I do this? I would filter it to meet that subset, I would do this via traversals.

3.Why is "Clean and/or Filter" an important part of the Data Analysis Process? What are situations when you would filter vs. clean your data? They help keep data organized, up to date, and readable.  You would clean the data when needing to edit or correct incorrect data and filter data to gather a specific group of data out of all the data.

Lesson 4

1.Imagine you wanted to know which hour of the day you and your classmates are happiest. What kind of data would you collect? The data I would collect is by asking me any my classmate every hour to rate their happiness on a scale of 1 through 10. 

2.How do you think you'd analyze it? I would analyze this data by seeing which hour me and my classmate reported we were the happiest. Using this data I could then deduce and answer the question that the hour of day that me and my classmate are the happiest are when we each reported our highest happiness scores respectively.

3.How many "Herding" breeds live a maximum of 12 years?  4 "Herding" breeds live a maximum of 12 years. What is the most common maximum life span for "Working" breeds? The most common maximum life span for "Working" breeds is 12 years.

4.Which breed group lives the shortest? The breed that lives the shortest is the "Working" breed.

5.Which breed group lives the longest? The breed that lives the longest is the "Terrier" Breed.

6.How do you know? How confident are you in your answers? I know because I looked at where the maximum and minimum life spans centered around for each breed. and through this, I was able to deduce the shortest and longest living breeds respectively. I am very confident in my answers.

7. Is there a pattern? How can you tell? There is most definitely some kind of pattern present. I can tell this by looking at the graph as when analyzing the graph I notice that there is a very slight positive correlation present, hence there is a pattern.

Lesson 5

Step 1 : Collect or Choose Data 

1.What is this step and why is it important? This step chooses the data the user wants to display and this is important as this is the basis of all the data .

2.Where have we done this step together? When selecting what dog column to do.

3.What could go wrong if you do this step poorly ? The entire data becomes wrong.

Step 2 : Clean and/or Filter

1.What is this step and why is it important? This data selects the specific data from the one selected to use and display and it is important as you don't want to display any data you don't need

2.Where have we done this step together? When selecting what to use in the dog column

3.What could go wrong if you do this step poorly ? The data is not properly represented.

Step 3 : Visualize and Find Patterns

1.What is this step and why is it important? This step chooses how the user wants to represent the data and it is important as that's how the data is viewed to the user

2.Where have we done this step together? When selecting whether to use a bar graph or not for the dog data.

3.What could go wrong if you do this step poorly ? The data is not represented in a desirable way.

Step 4 : New information

1.What is this step and why is it important? This step gathers the information from representing the data and is important for research purposes

2.Where have we done this step together? When analyzing the dog graph.

3.What could go wrong if you do this step poorly ? The information isn't properly gathered and you might learn the wrong thing.

Lesson 6

1.How can machines "learn"? Machines learn by recognizing patterns in the data and make predictions once new data arrives.

2.How well did A.I. do? The A.I. did very well in all honesty.

3.How do you think it decided what to include in the ocean? It decided what to include in the ocean by analyzing the present data and made predictions on what to include in the ocean based on the data.

4.How do you think your training data influenced the results that A.I. produced? My training data influenced the results produced by A.I. by enforcing patterns through which the A.I. could analyze and produce results off of.

4.How could biased data result in problems for artificial intelligence? What are ways to address this? Biased data is most definitely a problem for A.I. as this data throws off the A.I. from making accurate deductions and thus it results in inaccurate A.I. results. A way this could be fixed by having customized data filters in place to limit the amount of biased data.

5.How can computing innovations which make use of Machine Learning reflect existing human bias? Growing computer innovations which make use of Machine Learning reflect existing human bias as the outputs that the Machine Learning produces could have any human bias integrated in them in one way or another.

6.How could it be used to discriminate against groups of individuals? If the bias, which is inevitably going to be mixed into Machine Learning regardless of the intensity, happens to be on the extreme and radical end, it could unfortunately definitely lead to discrimination of others and maybe even racism.

7.How can that bias be minimized? Like I aforementioned, a way to minimize the amount of bias and biased data that is received is by implementing certain filters that will work to mitigate any data based off bias suspicion.

8. Which steps of this process do you think have to be done by humans? Would you be concerned if any of them were automated?The new information step is the one that definitely has to be done by humans. If this step was automated, then it would be a problem as A.I. would struggle to carry this step out in its entirety.

This is the 4th screen of the AI for Oceans Learning Activity

Lesson 7/8 Project

Image above is the graph used in the project

Study Guide