Week 1 Reading Questions (2)
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School
University of North Texas *
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Course
5250
Subject
Information Systems
Date
Nov 24, 2024
Type
docx
Pages
3
Uploaded by AdmiralPonyMaster845
1
Moheed Mohammed
Business Analytics
18
th
May 2023
Week 1 reading questions
1.
Describe big analytics, big data, and predictive analytics. What do they have in
common?
Big analytics is the process of examining enormously complicated datasets, also known
as big data, in order to derive insightful conclusions and make wise judgments. It entails using a
range of analytical methods and algorithms to draw out patterns, trends, and relationships from
enormous amounts of data.
Big data refers to large and diversified datasets that are difficult to manage, process, or
analyze using conventional data processing techniques. It frequently contains organized, semi-
structured, and unstructured data gleaned from a variety of sources, including social media,
sensors, transactions, and more.
Predictive analytics is a type of analytics, used to anticipate future outcomes or behaviors
using historical data and statistical algorithms. In order to find patterns and create predictive
models that can anticipate upcoming events, trends, or behaviors, machine learning, data mining,
and statistical modeling approaches must be applied.
2.
Describe an example of an organization that uses predictive analytics and how it has
added value to the company.
Netflix is one company that makes use of predictive analytics. Predictive analytics are
used by Netflix to tailor the user experience and offer suggestions for TV series and movies.
Netflix creates predictive models by examining user behavior, watching trends, and preferences
2
in order to recommend material that matches the user's interests. As a result, the organization has
gained significantly from an increase in customer happiness, engagement, and retention.
3.
Discuss the nine steps of the predictive analytics model.
The nine steps of the predictive analytics model:
Define the problem
Gather and prepare data
Explore and understand the data
Select the predictive model
Split the data into training and testing sets
Train the model using the training data
Validate the model using the testing data
Deploy and monitor the model in a production environment
Use insights from the model to make informed decisions.
4.
Why is a critical thinking mindset important in a career in analytics?
To effectively ask the proper questions, objectively assess the information, recognize
patterns and trends, assure accuracy, and solve difficult problems, critical thinking is essential in
analytics.
5.
Discuss at least three different possible career opportunities enabled by predictive
analytics.
Data scientist:
A person who builds models and gains insights using predictive analytics.
Data analysis and predictive analytics are used by
business analysts
to spot patterns and
business prospects.
3
Manage and process massive datasets for applications requiring predictive analytics as a
data engineer
.
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