Data; Data manipulation, sorting, grouping, rearranging. Plotting the data. Descriptive statistics. Inferential statistics. Python Libraries for Data Science.

Ch. Eick Introduction to Data Science 8/28/2023
What is Data Science?
 What words come to mind when you think of Data
Science?
 What experience do you have with Data
Science?
 Why are you taking the Data Science I class?
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Data Science Definitions
Data Science is an interdisciplinary field about processes and systems to extract
knowledge or insights from data in various forms, either structured or
unstructured, which is a continuation of some of the data analysis fields such as
statistics, data mining, and predictive analytics, similar to Knowledge Discovery
in Data (KDD).
Data Science is a multi-disciplinary field that uses scientific methods, processes,
algorithms and systems to extract knowledge and insights from structured and
unstructured data. Data science is the same concept as data mining and big
data: "use the most powerful hardware, the most powerful programming
systems, and the most efficient algorithms to solve problems" Data science is a
"concept to unify statistics, data analysis, machine learning and their related
methods" in order to "understand and analyze actual phenomena" with data
technology“ (Wikipedia)

Data Science Definitions II
 There are many, but most say data science is:
– Broad – broader than any one existing discipline
– Interdisciplinary: Computer Science, Statistics,
Information Science, Databases, Mathematics
– Applied focus on extracting knowledge from data to
inform decision making.
– Focuses on the skills needed to collect, manage,
store, distribute, analyze, visualize, reuse data and on
data storytelling.
 There are many visual representations in Data
Science
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Some More Definitions
4

More Definitions
5

Data Science is Broad!
6

Data Science Word Cloud
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Data Analysis
 We analyze data to extract meaning from it.
 Virtually all data analysis focuses on data
reduction
 Data reduction comes in the form of:
– Descriptive statistics
– Measures of association
– Graphical visualizations
 The objective is to abstract from all of the data
some feature or set of features that captures
evidence of the process you are studying
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The Data Lifecycle
 Data science considers data at every stage of what is
called the data lifecycle.
 This lifecycle generally refers to everything from
collecting data to analyzing it to sharing it so others
can re-analyze it.
 New visions of this process in particular focus on
integrating every action that creates, analyzes, or
otherwise touches data.
 These same new visions treat the process as
dynamic – archives are not just digital shoe boxes
under the bed.
 There are many representations of the this lifecycle.
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Data curation is a term used to indicate management
activities related to organization and integration
of data collected from various sources, annotation of the
data, and publication and presentation of the data such
that the value of the data is maintained over time, and the
data remains available for reuse and preservation.
Data Curation

News August 29, 2023
 Task1 ProblemSet1 should be available on Sept. 5 or earlier;
check course website!
 Plans for today’s class
– Finish Introduction to Data Science/Data Mining
– 3p: Conduct Survey R/Python Knowledge of Students in the
course
– Data Science Basics & Exploratory Data Analysis
 Plans for the August 31 class
– Continue Data Science Basics & Exploratory Data Analysis
– Preprocessing for DS/Data Mining
– Optional: Brief Discussion of Task1 of ProblemSet1
– More Discussion GHC and Announce GHC Groups
 There will be a lab as part of the Sept. 5 class; bring laptop!
12

What is Missing?
 Most definitions of data science underplay or
leave out discussions of:
– Substantive theory
– Metadata
– Privacy and Ethics
13

Privacy and Ethics
 Data, the elements of data science, and even so-
called “Big Data” are not new.
 One thing that is new is the greater variety of
data and, most importantly, the amount of data
available about humans.
 Discussion and good policy regarding privacy,
security, and the ethical use of data about people
lags behind the methods of collecting, sharing,
archiving, and analyzing data.
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Big Data
 The launch of the Data Science conversation has
been sparked primarily by the so-called “Big
Data” revolution.
 As mentioned, we have always had data that
taxed our technical and computational capacities.
 “Big Data” makes front-page news, however,
because of the explosion of data about people.
 Contemporary definitions of Big Data focus on:
– Volume (the amount of data)
– Velocity (the speed of data in and out)
– Variety (the diverse types of data)
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Talk Outline
1. Importance of Data Science
2. Data Science is More than Using Tools
3. Data Storytelling
4. Examples of Data Storytelling
5. Conclusion
Data Analysis and Intelligent Systems Lab
Data Science According to Swami Chandrasekaran

Data Science and Storytelling
 Google’s Chief Economist Dr. Hal R.Varian stated, "The
ability to take data—to be able to understand it, to process it,
to extract value from it, to visualize it, to communicate it—
that’s going to be a hugely important skill in the next
decades."
 “When hiring data scientists, people tend to focus primarily
on technical qualifications. It’s hard to find candidates who
have the right mix of computational and statistical skills. But
what’s even harder is finding people who have those skills
and are good at communicating the story behind the data.”
Michael Li

Data Science and Storytelling 2
 “Data scientists are involved with gathering data, massaging it into a tractable
form, making it tell its story, and presenting that story to others.” – Mike
Loukides, VP, O’Reilly Media
 “Our challenge as data scientists is to translate this haystack of information
into guidance for staff so they can make smart decisions…We “humanize” the
data by turning raw numbers into a story about our performance. Data
scientists want to believe that data has all the answers. But the most important
part of our job is qualitative: asking questions, creating directives from our
data, and telling its story. ” Jeff Bladtand and Bob Filbin
 Telling Stories with Data in 3 Steps (Quick Study) - Bing video (showed
this video on Aug. 24, 2023)
 The Power in Effective Data Storytelling | Malavica Sridhar | TEDxUIUC -
Bing video starting 6:40
 20 of the Best Video Storytelling Examples EVER | Wyzowl
 20 Best Data Storytelling Examples (updated for 2023) — Juice Analytics
Not covered in August 2023, except we showed one video!

Comment
 This concludes the Introduction to Data
Science/Data Mining
 The remaining slides of this slide show and other
slides will be discussed in a lecture centering of
“Data Storytelling”, in the second half of
November 2023…
 There will be also some limited coverage of Data
Science Ethics in November, 2023.

Data Science is More than Using Tools
 The problem with data is that it says a lot, but it also says nothing.
‘Big data’ is terrific, but it’s usually thin. To understand why
something is happening, we have to engage in both forensics and
guess work.”- Sendhil Mullainathan, Professor of economics,
Harvard
 “But a theory is not like an airline or bus timetable. We are not
interested simply in the accuracy of its predictions. A theory also
serves as a base for thinking. It helps us to understand what is
going on by enabling us to organize our thoughts. Faced with a
choice between a theory which predicts well but gives us little
insight into how the system works and one which gives us this
insight but predicts badly, I would choose the latter, and I am
inclined to think that most economists would do the same.”
― Ronald H. Coase, Essays on Economics and Economists
Slides which follow were not covered in August 2023!

Data Storytelling is Currently “Hot”
Evidence:
 Quotations of Leaders in Data Science we
presented earlier
 Watch Commercials: https://www.tableau.com/solutions/customer/storytelling-data-0
 Popularity of TED Talks, most of which mostly
center on data storytelling.
 New productsaps: https://www.esri.com/arcgis-blog/story-maps/
 …Learn Data Story telling on Tableau 2022 in 7 mins - Bing video
 A lot of data storytelling contests

Importance of Data Science
 “It is a capital mistake to theorize before one has
data.”- Arthur Conan Doyle, Author of Sherlock
Holmes
 If you’re a scientist, and you have to have an
answer, even in the absence of data, you’re not
going to be a good scientist.” – Neil deGrasse
Tyson, Astrophysicist
 “Without big data analytics, companies are blind
and deaf, wandering out onto the Web like deer
on a freeway.” – Geoffrey Moore, Partner at MDV

Resposibilities of Data Scientists
1. We have to have some committment to telleth
„Torture the data long enough and it will confess to anything."
Nobel Prize winning economist Ronald Coase
“To find signals in data, we must learn to reduce the noise -
not just the noise that resides in the data, but also the noise
that resides in us. It is nearly impossible for noisy minds to
perceive anything but noise in data.”
― Stephen Few, Signal: Understanding What Matters in a
World of Noise
2. We have to know what we are doing

COSC Data Science Curriculum
COSC 3337: Data Science II (starting in 2019)
Credit Hours: 3.0
Lecture Contact Hours: 3 Lab Contact Hours: 0
Prerequisite: ‘Data Structures’
Data science process, data preprocessing, exploratory data
analysis, data visualization, basic statistics, basic machine
learning concepts, classification and prediction, similarity
assessment, clustering, post-processing and interpreting data
analysis results, use of data analysis tools and programming
languages and data analysis case studies.

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