Analysing and visualing data.

Review The Power of Good Design and select three of the ten principles noted for good design. Next in R, utilize these three principles in a problem that you will solve. First note the problem to solve, the dataset (where the information was pulled from), and what methods you are going to take to solve the problem. Ensure the problem is simple enough to complete within a two-page document. For example, I need to purchase a house and want to know what my options are given x amount of dollars and x location based on a sample of data from Zillow within each location. Ensure there is data visualization in the homework and note how it relates to the three principles selected. Link for the power of good design (https://www.vitsoe.com/gb/about/good-design) Homework 2 ( Business Intelligence(chapter 3 and 4)) Complete the following assignment in one MS Word document: Chapter 3. How do you describe the importance of data in analytics? Can we think of analytics without data? Explain. Considering the new and broad definition of business analytics, what are the main inputs and outputs to the analytics continuum? Where do the data for business analytics come from? What are the sources and the nature of those incoming data? What are the most common metrics that make for analytics-ready data? 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. Chapter 4. Define data mining. Why are there many names and definitions for data mining? What are the main reasons for the recent popularity of data mining? Discuss what an organization should consider before making a decision to purchase data mining software. Distinguish data mining from other analytical tools and techniques.

Sample Solution

Data is the indispensable foundation of analytics, as crucial as oxygen is to life. Without data, analytics would be a hollow shell, incapable of generating insights, guiding decisions, or revealing hidden patterns. It's through data that we flesh out the bones of analysis with substance, allowing us to:

Quantify the world: Data transforms intangible notions like customer satisfaction, market trends, or operational efficiency into measurable figures, enabling objective analysis and comparison.
Uncover hidden truths: Buried within massive data sets lie undiscovered correlations, patterns, and anomalies. Analytics, powered by data, extracts these hidden truths, shedding light on the workings of complex systems and informing more informed decisions.

Full Answer Section

Predictive power: Data acts as a crystal ball, allowing us to anticipate future trends, model potential outcomes, and prepare for upcoming challenges. By analyzing historical data and identifying patterns, analytics empowers us to make proactive decisions and mitigate risks.
Drive continuous improvement: Data acts as a constant feedback loop, revealing areas for improvement in processes, products, and services. Through data-driven analytics, we can refine strategies, optimize operations, and continuously increase efficiency and effectiveness.

Imagine trying to analyze customer behavior without purchase records, predict market trends without historical data, or optimize operations without performance metrics. It would be akin to navigating a dark room blindfolded - a futile and potentially disastrous endeavor.

Simply put, data is the raw material from which analytics crafts its magic. It fuels the engine of insight, propels the journey of discovery, and ultimately serves as the guiding compass towards better decisions and improved outcomes. Without data, analytics would be a mere theoretical exercise, devoid of the power to translate into meaningful impact.

It's important to remember that the quality and relevance of data significantly impact the insights derived from analytics. Ensuring clean, accurate, and timely data is crucial for reliable analysis and actionable results.

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