Do some basic research on security certifications. See https://www.giac.org/. Write a brief summary of certifications that are open. Consider if any of the certifications would be valuable for your career. Investigate and report on exam options. Do some basic research on security certifications. See https://www.giac.org/. Write a brief summary of certifications that are open. Consider if any of the certifications would be valuable for your career. Investigate and report on exam options.
Think about a good or bad service experience that you have had. Briefly describe the experience. What factors contributed to this experience? What changes could have been made to make this an even better service experience? Why is service marketing important? Think about a good or bad service experience that you have had. Briefly describe the experience. What factors contributed to this experience? What changes could have been made to make this an even better service experience? Why is service marketing important?
Clustering techniques Part 1 Describe in detail the difference between SOM and LLE. Which techniques do you think is more effective than K-means clustering. Clustering techniques Part 1 Describe in detail the difference between SOM and LLE. Which techniques do you think is more effective than K-means clustering.
MNIST / Fashion MNIST image data The main objective for this week is to write a fully executed R-Markdown program performing clustering using SOM and LLE on the image data containing MNIST (digits) and Fashion MNIST (apparel) images that are 28 x 28 pixels resolution. Make sure to describe the final hyperparameter settings of all algorithms that were used for comparison purposes. MNIST / Fashion MNIST image data The main objective for this week is to write a fully executed R-Markdown program performing clustering using SOM and LLE on the image data containing MNIST (digits) and Fashion MNIST (apparel) images that are 28 x 28 pixels resolution. Make sure to describe the final hyperparameter settings of all algorithms that were used for comparison purposes.
Describe various types of collaborative filtering and a business use case. Describe various types of collaborative filtering and a business use case.
Write a fully executed R-Markdown program and submit a pdf file solving and answering questions listed below under Problems at the end of chapter 14. For clarity, make sure to give an appropriate title to each section. Problem 2: Identifying course combinations Problem 3: Cosmetic purchases (a, b, c) Write a fully executed R-Markdown program and submit a pdf file solving and answering questions listed below under Problems at the end of chapter 14. For clarity, make sure to give an appropriate title to each section. Problem 2: Identifying course combinations Problem 3: Cosmetic purchases (a, b, c)
Discussing how insurance companies use text mining to reduce fraud. Discussing how insurance companies use text mining to reduce fraud.
Attached Files: tweets_julia.csv (1.223 MB) tweets_dave.csv (1.335 MB) Using the attached files of around 3200 tweets per person, show a histogram (frequency distribution) of the tweets of both Dave and Julia. Use `UTC` to create the time stamp. Remember that the case of column headers matters. Make a dataframe of word frequency for each of Dave and Julia. Plot the frequencies against each other. Include a dividing line in red showing words nearby that are similar in frequency and words more distant which are shared less frequently. Create a stacked chart comparing the odds ratios of the top 15 words used by each tweeter. Remove twitter handles from the list of words. Calculate the word usage ratios (usage v. total) and display it on a log scale. Do you notice any interesting differences? Does anything stand out as a difference? Comment your code, line by line. Submit one document with screenshots of your work in R Studio. Include a slice of your desktop with your screenshots. Attached Files: tweets_julia.csv (1.223 MB) tweets_dave.csv (1.335 MB) Using the attached files of around 3200 tweets per person, show a histogram (frequency distribution) of the tweets of both Dave and Julia. Use `UTC` to create the time stamp. Remember that the case of column headers matters. Make a dataframe of word frequency for each of Dave and Julia. Plot the frequencies against each other. Include a dividing line in red showing words nearby that are similar in frequency and words more distant which are shared less frequently. Create a stacked chart comparing the odds ratios of the top 15 words used by each tweeter. Remove twitter handles from the list of words. Calculate the word usage ratios (usage v. total) and display it on a log scale. Do you notice any interesting differences? Does anything stand out as a difference? Comment your code, line by line. Submit one document with screenshots of your work in R Studio. Include a slice of your desktop with your screenshots.
Discussing the benefits of using R with Hadoop. Discussing the benefits of using R with Hadoop.
How many nodes can be supported in a Hadoop cluster? What happens if you should lose a block of memory in Hadoop? What does the Resource Manager User Interface in YARN do? What does YARN do? What outcomes can you achieve by running MapReduce jobs in Hadoop Comment your code, if any, line by line. Submit one document with any screenshots of your wo?rk in R Studio. Include a slice of your desktop with your screenshots. How many nodes can be supported in a Hadoop cluster? What happens if you should lose a block of memory in Hadoop? What does the Resource Manager User Interface in YARN do? What does YARN do? What outcomes can you achieve by running MapReduce jobs in Hadoop Comment your code, if any, line by line. Submit one document with any screenshots of your wo?rk in R Studio. Include a slice of your desktop with your screenshots.
