engineering writing question and need an explanation and answer to help me learn.
q1 Please watch the TED Talk “Moving Beyond Data Visualization”(need asap )
1. What does speaker Evans mean by windshield?
2. Explain experiments, the speaker has explained?
3. Respond to your classmates’ posts and comment on their selections
q2 According Visual Analysis Best Practice(need asap)
1. Select and list/describe 5 practices as presented in the white paper.
2. Of the 5 practices, in your opinion, what are the top 2 points that are vital for data visualizations. Explain why you chose these 2.
3. Mention any Data Visualization tool other than Tableau, and its industry
4. Respond to at least 2 classmate in a substantive manner.
q3(will give more time for this assignment)
1. Define the following terms. For each, use your own words.
Describe each in the context of our textbook. (30 points)
◦ k-nearest neighbor KNN
◦ Data Mining
◦ Web Analyitics
◦ Natural Language Processing
2. What is a support vector machine?
Why are support vector machines a popular machine-learning technique?
How can this be done using R?
3. What is natural language processing?
Describe the specific techniques used.
List and describe several of the specific challenges of NLP.
4. How does sentiment analysis relate to text mining?
What are the common challenges with which sentiment analysis deals?
What are the most popular application areas for sentiment analysis? Why?
5. Describe how sentiment analysis is done.
Give the steps required to do this in R. Be specific.
6. What is a correlation? How is correlation done in R?
Give an example and explain the meaning for the results in R.
7. What is a confusion matrix?
Give an example of a confusion matrix, and explain the meaning.
8. Most of the applications of deep learning today are developed using R- and/or Python-based
open-source computing resources. Identify those resources (frameworks such as Torch, Caffe,
TensorFlow, Theano, Keras) available for building deep learning models and applications.
Compare and contrast their capabilities and limitations. Based on your findings and
understanding of these resources, if you were to develop a deep learning application, which one
would you choose to employ? Explain and justify/defend your choice.
9. Consider 3 different sources for text data to use for continual analysis. Describe how this data
could be obtained, processed, and what could it be used for.
Do not complete the analysis.
– Use Python Twitter API to search for new Tweets about, but not authored by, Elon Musk.
– Remove, strip, process the text
– Using sentiment analysis, determine if Elon is being a Jerk today.
– Calculate and post the ‘Daily Elon Threat Level’ on a website for the Tesla employees break
room. (10 points)
10. Go to https://www.crummy.com/software/BeautifulSoup/bs4/…
– Review the documentation for the Beautiful Soup library for Python.
– Complete the Quick Start Guide examples. Document this work.
Then answer the following:
1. How can this be useful for machine learning and AI?
2. List 3 websites that could be useful for data/text mining with BeautifulSoup.
For each, give the URL and a specific feature to look for.
3. After using BS to get the data, how would you process it?