datamining
Description
Unformatted Attachment Preview
MCIS-6273 Data Mining (Prof. Maull) / Fall 2022 / HW3c
Points Possible
Due Date
Time Commitment (estimated)
5
Thursday, Dec 8 @ 8pm CT
up to 2 hours
RADING: Grading will be aligned with the completeness of the objectives.
NDEPENDENT WORK: Copying, cheating, plagiarism and academic dishonesty are not tolerated by University or
course policy. Please see the syllabus for the full departmental and University statement on the academic code of honor.
OBJECTIVES
earn about data science from the command line
WHAT TO TURN IN
You are being encouraged to turn the assignment in using the provided Jupyter Notebook. To do so, make a directory in your
Lab environment called homework/hw3c. Put all of your files in that directory. Then zip that directory, rename it with your
name as the first part of the filename (e.g. maull_hw3_files.zip), then download it to your local machine, then upload the
.zip to Blackboard.
If you do not know how to do this, please ask, or visit one of the many tutorials out there on the basics of using zip in Linux.
If you choose not to use the provided notebook, you will still need to turn in a .ipynb Jupyter Notebook and corresponding
files according to the instructions in this homework.
ASSIGNMENT TASKS
(100%) Learn about data science from the command line
We have learned a lot about Jupyter notebooks in this course, yet there are a lot of other tools that should (and will) be in the
quiver of the working data scientist.
You will learn about such tools in this interactive recording of a live stream that is an interview / live coding with Jeroen Jannsens
a data science consultant and author of the book Data Science at the Command Line.
You will watch the video of the podcast Talk Python[/dcast`Episode #392 Data Science from the Command Line, December 2, 2022: where the host and guest discuss and review some different patterns in the data science toolchain.
You can listen to / watch the show from one of the links below (there are others (i.e. Apple Podcasts) if you search):
(main page) Talk Python[/dcast`Episode #392
(mp3 direct) MP3 file direct download
(youtube) YouTube: Talk Python[/dcast`Episode #392
ask: Listen to the podcast and answer the following 4 questions:
1. Summarize the episode in 3-5 sentences.
2. List 5 detailed things you learned from this episode.
3. Relate the things you learned in this class with what you learned in the podcast.
You should list at least 3 things to get full points for this question.
1
Purchase answer to see full
attachment
Points Possible
Due Date
Time Commitment (estimated)
5
Thursday, Dec 8 @ 8pm CT
up to 2 hours
RADING: Grading will be aligned with the completeness of the objectives.
NDEPENDENT WORK: Copying, cheating, plagiarism and academic dishonesty are not tolerated by University or
course policy. Please see the syllabus for the full departmental and University statement on the academic code of honor.
OBJECTIVES
earn about data science from the command line
WHAT TO TURN IN
You are being encouraged to turn the assignment in using the provided Jupyter Notebook. To do so, make a directory in your
Lab environment called homework/hw3c. Put all of your files in that directory. Then zip that directory, rename it with your
name as the first part of the filename (e.g. maull_hw3_files.zip), then download it to your local machine, then upload the
.zip to Blackboard.
If you do not know how to do this, please ask, or visit one of the many tutorials out there on the basics of using zip in Linux.
If you choose not to use the provided notebook, you will still need to turn in a .ipynb Jupyter Notebook and corresponding
files according to the instructions in this homework.
ASSIGNMENT TASKS
(100%) Learn about data science from the command line
We have learned a lot about Jupyter notebooks in this course, yet there are a lot of other tools that should (and will) be in the
quiver of the working data scientist.
You will learn about such tools in this interactive recording of a live stream that is an interview / live coding with Jeroen Jannsens
a data science consultant and author of the book Data Science at the Command Line.
You will watch the video of the podcast Talk Python[/dcast`Episode #392 Data Science from the Command Line, December 2, 2022: where the host and guest discuss and review some different patterns in the data science toolchain.
You can listen to / watch the show from one of the links below (there are others (i.e. Apple Podcasts) if you search):
(main page) Talk Python[/dcast`Episode #392
(mp3 direct) MP3 file direct download
(youtube) YouTube: Talk Python[/dcast`Episode #392
ask: Listen to the podcast and answer the following 4 questions:
1. Summarize the episode in 3-5 sentences.
2. List 5 detailed things you learned from this episode.
3. Relate the things you learned in this class with what you learned in the podcast.
You should list at least 3 things to get full points for this question.
1
Purchase answer to see full
attachment
Explanation & Answer:
4 Questions
User generated content is uploaded by users for the purposes of learning and should be used following our honor code & terms of service.
Have a similar assignment? "Place an order for your assignment and have exceptional work written by our team of experts, guaranteeing you A results."
Complete the 
Complete the order payment after filling in the order form. We’ll receive your order and assign it to a writer.
The assigned writer will complete your paper and forward it to our Editing Team for review and approval.
Once approved by the editor, the completed paper will be uploaded to your account for you to download, review and approve.