Data Science For Business - Chapter 1: Introduction to Data Analytic Thinking Summary
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Data Science 141 (14026141)
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Stellenbosch University (SUN)
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Data Science for Business
Detailed Summary for the first chapter (Introduction to Data Analytic Thinking) for the book "Data Science for Business" that explains the fundamental concepts. This summary also contains examples to illustrate these fundamental concepts and improve your understanding.
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Data Science 141 (14026141)
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1
Data Science 141 Data Science for Business
Introduction to Data
Analytic Thinking
Introduction
Over the past fifteen years, significant investments in business infrastructure have been made.
Nowadays, almost every area of company is open to data collecting and frequently even equipped
with data collection instruments.
These areas include:
- Operations
- Manufacturing
- Supply-Chain Management
- Customer Behaviour
- Marketing Campaign Performance
- Workflow Procedures, etc.
Information is publicly available on recent events such as:
- Market Trends
- Industry news
- Competitors Movements
Due to the widespread availability of data, there is growing interest in techniques for collecting
knowledge and information from data.
This is in the domain of data science.
The Ubiquity of Data Opportunities
The volume and variety of data have far exceeded the capacity for manual analysis as was done in
the past when firms employed teams of statisticians, modelers, and analysts to manually explore
datasets, and as a result, companies in almost every industry are focused on utilizing data for
competitive advantage.
Computers now have far greater power.
Networking has become ubiquitous.
- Ubiquitous: Present, appearing or found everywhere.
Algorithms have been created that can link datasets to allow for wider and more in-depth studies.
These phenomena have come together, and as a result, data science ideas and data mining methods
are being used more and more frequently.
Data-mining techniques are most frequently used in marketing for activities such as:
- Targeted Marketing
- Online Advertising
- Recommendations for Cross-Selling
o Cross-selling: Sales technique involving the selling of an additional product or service to
an existing customer.
, 2
Data Science 141 Data Science for Business
Data mining is used for:
- General Customer Relationship Management
- Analysing customer behaviour in order to manage attrition and maximise expected customer
value.
o Attrition: The act of diminishing the size of a workforce on purpose by not replacing
workers who leave on their own volition (employee attrition). It might also mean that a
company's customer base is getting smaller as a result of existing customers leaving and
not being replaced by as many new ones (customer attrition)
- Credit Scoring and Trading
- Fraud Detection
- Workforce Management
- Marketing
- Supply-Chain Management
A significant number of companies have used data science to strategically differentiate themselves,
sometimes to the point that they've turned into data mining businesses.
The structure of data-analytic thinking is key.
There are also specific situations in which domain expertise, common sense, inventiveness, and
intuition are required.
A data perspective will offer guidelines and structure, which will result in a framework for
methodically analysing these issues.
The phrases "data science" and "data mining" are frequently used synonymously, and the former has
acquired a life of its own as various people and organizations attempt to profit from the current
excitement around it.
Data Science is a set of fundamental principles that guide the extraction of knowledge from data.
Data Mining is the extraction of
knowledge from data, via technologies Even if you never plan to use data science yourself, it is still
that incorporate these data science necessary to understand it. You are able to assess ideas for
data mining initiatives by using data-analytic thinking. This
principles.
does not imply that you will be able to predict if it will work
Although the phrase "data science" is because data mining initiatives frequently require trial and
frequently used more broadly than it has error, but you should be able to identify glaring errors,
in the past, data mining techniques exaggerated claims, and missing information.
nevertheless offer some of the best
examples of how the ideas of data science are put into practice.
There are typically numerous distinct approaches that represent each principle.
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