Define “community counseling” and include the concepts of community, multicultural competence, social justice, and the healthy development of clients and communities
Define “community counseling” as discussed beginning on page 9 of the text and include the concepts of community, multicultural competence, social justice, and the healthy development of clients and communities
Share an example from the “Growing Up Online” documentary of a self-serving bias and explain how it impacted the communication of the person or people who were engaging in the self-serving bias
Growing Up Online Discussion Questions
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Hi everyone,
Please take some time to watch the following documentary this week: “Growing Up Online”: http://video.pbs.org/video/1082076027/
After watching the documentary you will answer the questions below. You will have until this Sunday at 11:59pm to submit your answers. If you do not get them in by then, the questions will close and you will receive a zero for the activity.
You can refer to the Chapter 3 Outline located on Canvas in the Modules section at the top where it says “Chapter Outlines” while answering the questions. Remember to use specific examples from the documentary, write using complete sentences, and explain your answers with extra detail versus just using one-sentence answers.
When you are ready to submit your answers, you will click “Reply” to this post and type in your answers. Let me know if you have any questions. If not, I will talk with all of you soon!
Professor Kidwell
Discussion Questions:
- In the Chapter 3 Outline on Perception & Communication, one of the steps identified in the perceptual process is called negotiation. Negotiation refers to the process of sharing personal narratives (personal stories and experiences) with other people in order to achieve a common understanding or agreement. While we negotiate with others we exchange these personal narratives, which represent our view of the world. In the documentary “Growing Up Online”, we learned that the Internet and social media have changed the way younger generations communicate and express themselves. Share one example from the documentary that illustrates how individuals may use the Internet to share their own personal narrative. Do you believe this to be an effective way to share personal narratives with other people? Why or why not?
- In Chapter 3, Perception & Communication, the authors discuss how cultural differences influence the way people interpret the communication of others. This can come from ethnic culture, national culture, family culture, or even the culture of your particular friend group or family. Share an example from the “Growing Up Online” documentary of how the Internet and social media have shaped part of the younger generation’s culture in a way that’s different from their parents and explain how this has affected the communication between the teenagers and their families.
- One of the concepts in Chapter 3 is called gender roles. Gender Roles are the socially approved ways that men and women are expected to behave. Find an example from the “Growing Up Online” documentary of a male gender role and female gender role when it comes to using the Internet specifically. In other words, share an example of how the boys and girls are taught to use the Internet differently depending on their gender role. What differences did you notice? Why do those differences exist? What can we learn from this?
- A Self-Serving Bias is defined as blaming a person’s personal qualities when they make mistakes, but blaming the circumstances instead when we, ourselves, make mistakes. What Chapter 3 teaches us is that we judge others more harshly than we do when it comes to ourselves. We expect more from others and do not hold ourselves to the same standards. Share an example from the “Growing Up Online” documentary of a self-serving bias and explain how it impacted the communication of the person or people who were engaging in the self-serving bias
Should people be discouraged from using the ER for non-emergency care by making it more expensive to use the ER when it’s not an emergency?
Should people be discouraged from using the ER for non-emergency care by making it more expensive to use the ER when it’s not an emergency? Why or why not?
Compute a new field called interaction count whose value is the sum of favorite count and retweet count.
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TMA – July Semester 2021
ICT233
Data Programming
Tutor-Marked Assignment
July 2021 Presentation
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TMA – July Semester 2021
TUTOR-MARKED ASSIGNMENT (TMA)
This assignment is worth 24% of the final mark for ICT233, Data Programming.
The cut-off date for this assignment is Monday, 13 Sept 2021, 2355 hrs.
Note to Students:
You are to include the following particulars in your submission: Course Code, Title of
the TMA, SUSS PI No., Your Name, and Submission Date.
Answer all questions. (Total 100 marks)
Question 1 (47 marks)
Objectives:
● Understand dataset with data scientist mind-set.
● Understand and design computation logic and routines in Python.
● Assess use of Python only and Python data structures to perform extract, load,
and transformation operations.
● Assess the design and use of database SQL and methods to perform extract, load,
transformation and calculation operations.
● Structure code in appropriate methods (functions), looping and conditions.
(a) From tweets.json, find and apply all entity types using Python code. For example,
hashtag is one entity type.
(2 marks)
(b) Create the tweets schema and store tweets in tweets.json file to a SQLite database.
The tweets schema contains the following fields: id, created_at, full_text,
favorite_count and retweet_count. The field id is the primary key.
(5 marks)
(c) Compose and create schema and store data.
(i) Create the entities schema with the following fields and requirements:
id: primary key
tweet_id: foreign key, which links to the id field of the tweets table
type: possible values found in Question 1(a)
value: contains entity text values, which appear in a tweet’s full_text
start index and end index: stored the values found in the JSON key
indices
(2 marks)
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(ii) Extract entities from every tweet to store into the entities table in the
SQLite database. Note that the entities of a tweet can be extracted from
the JSON key entities of each tweet.
(8 marks)
(d) Use SQL statement(s) to identify all unique values in hashtags.
(3 marks)
(e) Use SQL statement(s) to find all tweets which have a value of hashtags appearing
more than 1 time.
(5 marks)
(f) Use SQL statement(s) to calculate how many tweets that each value of hashtags
appears in. Sort the tweet counts in descending order. Note that a hashtag which
appears more than 1 time in a tweet, count only 1time for that tweet.
(5 marks)
(g) Plot the tweet counts of the top 10 common hashtags obtained in Question 1(f)
on a bar chart
(5 marks)
(h) Compute a new field called interaction_count whose value is the sum of
favorite_count and retweet_count.
(2 marks)
(i) Note that the time zone of created_at from the JSON file is UTC+0. To
visualize data relationships respectively as required below and draw
insight(s) from the visualizations:
the interaction_count and hour of the day (in Singapore time zone) of the
created_at
the interaction_count and day of the week (in Singapore time zone) of
the created_at
(10 marks)
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Question 2 (25 marks)
Objectives:
● Design computation logic and routines in Python.
● Assess use of Pandas dataframes to perform extract, load, transformation and
calculation operations.
● Conduct visualization in an appropriate way.
(a) Copy the template in Table 1 into your program. Analyse and implement the
TWO (2) Python functions. These two functions perform the pre-processing
logic to prepare each tweet for the word cloud visualization subsequently. Read
the detailed instruction and steps in the provided template.
import nltk
from nltk.corpus import stopwords
nltk.download(‘stopwords’)
from spellchecker import SpellChecker
def remove_entities(tweet_id, tweet):
# MUST use start and end indices extracted in the
question 1c) to remove entities
# Output: the preprocessed tweet with all entities
(hashtags, urls, media and user mentions) removed
return “”
def preprocess_tweet(tweet_id, tweet, remove_entities):
# 1) Lower case the input tweet
# 2) Call the remove_entities function to remove
hashtags, media, urls and user mentions from the input
tweet
tweet = remove_entities(tweet_id, tweet)
# 3) Remove all special characters
# 4) Remove English stop words which are defined at
“from nltk.corpus import stopwords”
# 5) Use the pyspellchecker library:
https://pypi.org/project/pyspellchecker/ to remove
misspelled words
# Output: a list of words constituting the input tweet
# For example, with the tweet “#SUSSSustainability:
what are the three incorrect assumptions about”,
# the output is the list of words: [three, incorrect,
assumptions].
return []
assert preprocess_tweet(‘1408411651238371337’,
‘#SUSSSustainability: What are the
three incorrect assumptions about climate change? A/P Koh
Tieh Yong from SUSS Centre for University Core addresses
these issues with Karen Cheah, Founder and CEO of
AlterPacks: https://t.co/DtjRrIzPTK’,
remove_entities) == [‘three’,
‘incorrect’, ‘assumptions’, ‘climate’, ‘change’, ‘tieh’,
‘yong’, ‘suss’, ‘centre’, ‘university’, ‘core’, ‘addresses’,
‘issues’, ‘karen’, ‘founder’, ‘ceo’]
Table 2: Template contains 2 functions to be implemented
(20 marks)
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TMA – July Semester 2021
(b) Apply the function preprocess_tweet implemented in Question 2(a) to every
tweet in the database. The lists of pre-processed words are then concatenated into
a single text as the input of WordCloud library
(https://pypi.org/project/wordcloud/) to plot a word cloud visualization.
(5 marks)
Question 3 (28 marks)
Objectives:
● Perform simple exploratory data analysis.
● Design computation logic and routines in Python.
● Assess use of Python only and Python data structures to perform extract, load,
transformation, and calculation operations.
● Assess use of Pandas dataframes to perform extract, load, transformation and
calculation operations.
● Assess the design and use of database ORM and methods to perform extract,
load, transformation and calculation operations.
Use ORM (unless state otherwise) to compute tf–idf
(https://en.wikipedia.org/wiki/Tf%E2%80%93idf), which can be used as a feature to
classify tweets or to search tweets by user queries.
(a) Create and develop new SQLite table called tweet_word_pairs with 2 columns
tweet_id and word. Break the words column obtained in Q2(b) into multiple rows
to form pairs of column tweet_id and word. Insert (tweet_id, word) pairs
computed from the previous step into this tweet_word_pairs table.
Each row in the dataframe generated in Q2(b) corresponds to a tweet and the
words field of each row contains the list of pre-processed words computed from
the full_text. One of the rows in the dataframe generated in Q2(b) is shown in
Figure 1. Considering the above row as the example, we will have the expected
result show in Figure 2.
(6 marks)
Figure 1: One of the rows in the dataframe generated in Q2(b)
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Figure 2: Expected result of the given row
(b) Compute the number of times each word appear in each tweet and store the value
ftd(word, tweet) in the column called ftd. Example output format is shown in Figure
3.
(4 marks)
Figure 3: Sample output format for Q3(b)
(c) Apply and use dataframe to compute the largest ftd(word, tweet) per tweet. Example
output is shown in Figure 4.
𝑚𝑎𝑥{𝑓𝑡𝑑(𝑤𝑜𝑟𝑑 𝐵, 𝑡𝑤𝑒𝑒𝑡)
, 𝑓𝑜𝑟 𝑒𝑣𝑒𝑟𝑦 𝑤𝑜𝑟𝑑 𝐵 ∈ 𝑡ℎ𝑒 𝑡𝑤𝑒𝑒𝑡}
(2 marks)
Figure 4: Sample output format for Q3(c)
(d) Use dataframe to compute the term frequency per (word, tweet). Example output
is shown in Figure 5.
tf(word A, tweet) = 0.5 + 0.5 * 𝑓𝑡𝑑(𝑤𝑜𝑟𝑑 𝐴, 𝑡𝑤𝑒𝑒𝑡)
𝑚𝑎𝑥{𝑓𝑡𝑑(𝑤𝑜𝑟𝑑 𝐵, 𝑡𝑤𝑒𝑒𝑡)
, 𝑓𝑜𝑟 𝑒𝑣𝑒𝑟𝑦 𝑤𝑜𝑟𝑑 𝐵 ∈ 𝑡ℎ𝑒 𝑡𝑤𝑒𝑒𝑡}
(4 marks)
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Figure 5: Sample output format for Q3(d)
(e) Compute the number of unique tweets which the word appears for each word.
Example output is shown in Figure 6.
(4 marks)
Figure 6: Sample output format for Q3(e)
(f) Use dataframe to compute inverse document frequency. Example output is
shown in Figure 7.
idf(word A) = log 𝑛𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑢𝑛𝑖𝑞𝑢𝑒 𝑡𝑤𝑒𝑒𝑡𝑠
𝑛𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑢𝑛𝑖𝑞𝑢𝑒 𝑡𝑤𝑒𝑒𝑡𝑠 𝑤ℎ𝑖𝑐ℎ 𝑡ℎ𝑒 𝑤𝑜𝑟𝑑 𝐴 𝑎𝑝𝑝𝑒𝑎𝑟𝑠
(4 marks)
Figure 7: Sample output format for Q3(f)
(g) Use dataframe to compute term frequency-inverse document frequency.
Example output is shown in Figure 8.
ifidf(word A, tweet) = tf(word A, tweet) * idf(word A)
(4 marks)
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Figure 8: Sample output format for Q3(g)
—– END OF PAPER —–
How will you help the stakeholders see the new vision and achieve the desired outcomes?
For this discussion, imagine you have presented your proposal to the stakeholders and you are met with resistance. How will you help the stakeholders see the new vision and achieve the desired outcomes?
Describe the processes you plan to use to engage all stakeholders including informing them, consulting with them, involving them, collaborating with them, empowering them, and addressing the resistance as appropriate for the organization and for your plan.
Review the article by Hemmatian (2019), on classification techniques. What were the results of the study?
- Review the article by Hemmatian (2019), on classification techniques. In essay format answer the following questions:
- What were the results of the study?
- Note what opinion mining is and how it’s used in information retrieval.
- Discuss the various concepts and techniques of opinion mining and the importance to transforming an organizations NLP framework.
At what value should the land be recorded in Snap Repair Service’s records?
On February 3, Snap Repair Service extended an offer of $153,000 for land that had been priced for sale at $174,000. On February 28, Snap Repair Service accepted the seller’s counteroffer of $166,000. On October 23, the land was assessed at a value of $249,000 for property tax purposes. On January 15 of the next year, Snap Repair Service was offered $266,000 for the land by a national retail chain
Which areas of the value chain do you think should be included in calculating product costs and why?
The value chain includes costs from research to product design, to production, to marketing and sales, to distribution, and to customer support after the sale. Which areas of the value chain do you think should be included in calculating product costs and why? Where would the other costs be reported, if at all?
Embed course material concepts, principles, and theories (requires supporting citations) along with at least one scholarly, peer-reviewed reference in supporting your answer. Keep in mind that these scholarly references can be found by conducting an advanced search specific to scholarly references.
You are required to reply to at least two peer discussion question post answers to this weekly discussion question and/or your instructor’s response to your posting. These post replies need to be substantial and constructive in nature. They should add to the content of the post and evaluate/analyze that post answer. Normal course dialogue doesn’t fulfill these two peer replies but is expected throughout the course. Answering all course questions is also required.
What key event sparked the 1st World War?
History question
What key event sparked the 1st World War?
What changes would you propose the organization make to the mission statement?
Mission Statement – NBC Universal.
Choose an organization to use as your focus for answering the following questions.
Mission statements guide an organization’s decisions and strategic plans. Locate an organization’s mission statement. Evaluate the effectiveness of the organization’s mission statement. What changes would you propose the organization make to the mission statement?
APA 2 References
