Discussion 1: How do you describe the importance of data in analytics? Can we think of analytics without data? Explain. (250 words min) Discussion 2: Considering the new and broad definition of business analytics, what are the main inputs and outputs to the analytics continuum?

Please help with following written assignment:

Discussion and exercises:

Chapter 3:

• Discussion 1: How do you describe the importance of data in analytics? Can we think of analytics without data? Explain. (250 words min)
• Discussion 2: Considering the new and broad definition of business analytics, what are the main inputs and outputs to the analytics continuum? (250 words min)
• Discussion 3: Where do the data for business analytics come from? What are the sources and the nature of those incoming data? (250 words min)
• Discussion 4: What are the most common metrics that make for analytics-ready data? (250 words min)
• Exercise 12: 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 education, 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. (Include explanation along with graphs, charts etc, 250 words min)

Chapter 4

• Discussion 1: Define data mining. Why are there many names and definitions for data mining? (250 words min)
• Discussion 2: What are the main reasons for the recent popularity of data mining? (250 words min)
• Discussion 3: Discuss what an organization should consider before making a decision to purchase data mining software. (250 words min)
• Discussion 4: Distinguish data mining from other analytical tools and techniques. (250 words min)
• Discussion 5: Discuss the main data mining methods. What are the fundamental differences among them? (250 words min)
• Exercise 1: Visit teradatauniversitynetwork.com. Identify case studies and white papers about data mining. Describe recent developments in the field of data mining and predictive modeling. (250 words min)

Your paper should meet these requirements:

• Please submit work with min word counts, not including the required cover page and reference page. Also question word count is not counted.
• Follow APA 7 guidelines. Your paper should include an introduction, a body with fully developed content, and a conclusion.
• Support your answers with the readings from the course and at least two scholarly journal articles to support your positions, claims, and observations.
• References should be relevant and recent (in last 3 years). No bogus and made-up references
• If you need to quote from reference, do not exactly quote, rephrase the quote as direct quote increases plagiarism percentage.
• Be clearly and well-written, concise, and logical, using excellent grammar and style techniques.
• No plagiarism