Assignment 1 Why are the originalraw data not readily us.docx

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Assignment 1 : Why are the original/raw data not readily usable by analytics tasks? What are the main data preprocessing steps? List and explain their importance in analytics. Refer to Chapter 3 in the attached textbook: Sharda, R., Delen, D., Turban, E. (2020). Analytics, Data Science, & Artificial Intelligence: Systems for Decision Support 11E. ISBN: 978-0-13-519201-6. Discuss the process that generates the power of AI and discuss the differences between machine learning and deep learning. Requirement: ****Separate document for each assignment.**** Minimum 300-350 words. Cover sheet, abstract, graphs, and references does not count. Add references separately for each assignment question. Double Spaced and APA 7th Edition Format No plagiarized content please! Attach a plagiarized report. Check for spelling and grammar mistakes! $5 max. Please bid if you agree. Assignment 2 : What are the privacy issues with data mining? Do you think they are substantiated? Refer to Chapter 4 in the attached textbook: Sharda, R., Delen, D., Turban, E. (2020). Analytics, Data Science, & Artificial Intelligence: Systems for Decision Support 11E. ISBN: 978-0-13-519201-6. Requirement: ****Separate document for each assignment.**** Minimum 300-350 words. Cover sheet, abstract, graphs, and references does not count. Add references separately for each assignment question. Double Spaced and APA 7th Edition Format No plagiarized content please! Attach a plagiarized report. Check for spelling and grammar mistakes! $5 max. Please bid if you agree. .

$5 max. Please bid if you agree.
Assignment 2
:
What are the privacy issues with data mining? Do you think
they are substantiated?
Refer to Chapter 4
in the attached textbook:
Sharda, R., Delen, D., Turban, E. (2020). Analytics, Data
Science, & Artificial Intelligence: Systems for Decision Support
11E.
ISBN: 978-0-13-519201-6.
Requirement:
****Separate document for each assignment.****
Minimum 300-350 words. Cover sheet, abstract, graphs, and
references does not count.
Add references separately for each assignment question.
Double Spaced and APA 7th Edition Format
No plagiarized content please! Attach a plagiarized report.
Check for spelling and grammar mistakes!

The information age that we are living in is characterized by the rapid growth in the amount of data and information collected, stored, and made available in electronic format. The vast majority of business data is stored in virtually unstructured text documents. Therefore there is a need for text mining, and the following are the most popular application areas of text mining: 1. Information extraction 2. Topic tracking 3. Summarization 4. Categorization 5. Clustering 6. Concept linking 7. Question answering. Discussion question: Pick two of these most popular application areas of text mining and discuss extensively on them. After that, distinguish between text mining and data mining. Additionally, post some examples or find a related topic on the Internet and comment on it in the discussion post. Reference: Sharda, R., Delen, Dursun, and Turban, E. (2020). Analytics, Data Science, & Artificial Intelligence: Systems for Decision Support. 11th Edition. By PEARSON Education. Inc. ISBN-13: 978-0-13-519201-6 .

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1. Discussion (Chapter 3): Why are the original/raw data not readily usable by analytics tasks? What are the main data preprocessing steps? List and explain their importance in analytics. 2. How do you describe the importance of data in analyt-ics? Can we think of analytics without data? Explain. 3. Considering the new and broad definition of business analytics, what are the main inputs and outputs to the analytics continuum? 4. Where do the data for business analytics come from? What are the sources and the nature of those incoming data? 5. What are the most common metrics that make for analytics-ready data 6. Go to data.gov—a U.S. government–sponsored data portal that has a very large number of data sets on a wide variety of topics ranging from healthcare to edu-cation, climate to public safety. Pick a topic that you are most passionate about. Go through the topic-specific information and explanation provided on the site. Explore the possibilities of downloading the data, and use your favorite data visualization tool to create your own meaningful information and visualizations. .

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