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Tyumen Industrial University Data Analytics Discussion Questions

Tyumen Industrial University Data Analytics Discussion Questions

Question Description

I’m working on a business discussion question and need an explanation to help me learn.

Consider the following questions and respond in a minimum of 175 words:

  • How is data analytics different from statistics?
  • Analytics tools fall into 3 categories:descriptive, predictive, and prescriptive. What are the main differences among these categories?
  • Explain how businesses use analytics to convert raw operational data into actionable information. Provide at least 1 example.
  • Consider the organization you work for (or another organization you’re familiar with). Does this organization use data analytics?If so, how is it used? If not, how could the organization use data analytics to improve its performance?


Reply to at least 2 of your classmates and/or address any of the following subjects:

  1. Explain the difference between descriptive and inferential statistics/analytics.
  2. Define the type of graphs and charts you are most likely to see in your workplace. Why do you think these graphs and charts are used for the subjects they represent?
  3. What is a box plot and how do you assess symmetry of a box plot?
  4. What is a histogram and how do you assess symmetry or skewness of a histogram?
  5. What is a normal curve and how do we determine a normal curve?
  6. Define the measures of central tendency and the measures of dispersion.

Be constructive and professional.

Copyright 2020 by University of Phoenix. All rights reserved.

Coleen Maciel

Respond in a min of 125 words

Hello Professor and Peers,

Data analysis regulation place attention on drawing out awareness from data. It makes up the operations of the tools and skills of data analysis and management counting the collection organization, and archive of data. Statistics and statistical analysis permit data-based decision making and reduce leadership need to rely on conjecture. I work for a hospital organization, in which, my hospital organization use data analytics for a tool as a metrics system. This main metrics can be use for treatment errors, readmission cases and patient admission. This metrics will allow for the organization to identify with their standards and recognize any issues that need to be corrected to ensure that patient safety is being met. The data analytics can provide an overview of what is happening with the organization so that ways can be implemented to address any concerns that may be. This will present a good overview for the hospital organization to track and measure certain areas that need to be monitor so the right feedback can be produce for areas of improvement.

Thank you,

Coleen

Doane, D. P., & Seward, L. E. (2019). Essential Statistic in Business and Economics McGraw-Hill Education (6th ed.).

Joseph Groulx

respond in a min 125

Data analysis involves taking large amounts of data and making it presentable to people who have little or no experience in reading this technical data. I work for a manufacturer that produces a precursor batch formula used in food production. Each machine in this process is connected to hardware that analyzes inputs, outputs, uptime, downtime, and things like bottlenecks.

Most of this information is useless on its own, with periodic data collection showing similar numbers. The software that collects this data organizes each input from each machine and produces real-time data on a dashboard that is viewable by management who has no idea what each individual machine does or what any of this data means. What they get instead of hundreds of thousands of numbers is a dashboard that shows them an accurate present and historical picture of how their machines are running. They want to know where bottlenecks occur, how to alleviate bottlenecks, how to maximize production efficiency, and where to allocate resources to ensure that production happens as scheduled.

Before, companies sometimes kept paper copies of production figures. This was useful but very time consuming to collect and analyze this data. Today, it can be done in real time. It can be shared with customers if necessary. It can be used to coordinate shipments with suppliers. It can be used to automate processes and ultimately save money.

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