Around Data Science

data & content design
Frieda Brioschi - frieda.brioschi@gmail.com
Emma Tracanella - emma.tracanella@gmail.com
AROUND DATA SCIENCE
LESSON 5 - 2019/20

WITH YOUR DATA PROJECT
LET’S START

LESSON 5
4
DESCRIBE YOUR PROJECT
Photo by William Iven on Unsplash

LESSON 5
MARGARET HAMILTON
5

LESSON 5
AN EXAMPLE OF ONTOLOGY
http://mappings.dbpedia.org/server/ontology/classes/
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LESSON 5
LINKED DATA / LOD
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Linked data is structured data which is interlinked with other data so it becomes
more useful through semantic queries.It builds upon standard Web technologies
but rather than using them to serve web pages only for human readers, it extends
them to share information in a way that can be read automatically by computers.
Part of the vision of linked data is for the Internet to become a global database.
Linked data may also be open data, in which case it is usually described as linked
open data (LOD).
▸ https://en.wikipedia.org/wiki/Linked_data

LESSON 5
SCHEMA.ORG
http://schema.org/docs/full.html
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LESSON 5
GOOGLE KNOWLEDGE GRAPH
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https://www.youtube.com/watch?v=mmQl6VGvX-c

LESSON 5
WHY LINKED DATA MATTERS
Linked data is a method for publishing structured data using vocabularies like
schema.org that can be connected together and interpreted by machines. Using
linked data, statements encoded in triples can be spread across different
websites.
This enables data from different sources to be connected and queried.
▸ https://wordlift.io/blog/en/entity/linked-data/
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LESSON 5
12
To Making Better
Data-Informed
Decisions

LESSON 5
13
Formulate
a focused
question
ASK
Data-Informed
DECISION
MAKING
PROCESS
Monitor the
outcome
ASSESS
Search for the
best available
data
ACQUIRE
Critically
appraise and
analyze the data
ANALYZE
Integrate the data
with your professional
expertise and be
conscious about your
mental models
APPLY
Decide and
communicate
ANNOUNCE

LESSON 5
14
ASK
Turn the business questions into analytical question(s).
ACQUIRE
Find and source all relevant data. Remember to think
about the question systemically and include any
interrelated data that could be relevant. This includes
not only internal but external data and information too.
Ensure the sourced data is available, trusted, and in
the right form (extracted, profiled, tagged, cataloged,
standardized, treated for sensitivity, etc…)
ANALYZE
Create a measurement framework
to describe your data with KPIs.
Use exploratory analytics to find patterns and
trends and relationships that may exist and
not be obvious to start to drill into root cause.
?

LESSON 5
15
ANNOUNCE
Announce your decision at the right level to ALL stakeholders
(direct, indirect, upstream, and downstream) by leveraging methodologies
like the ‘Rule of 3’ and the ‘Pyramid Principle’ in your storytelling
APPLY
Review and orientate yourself to the
information and data so far and apply your
personal experiences to it.
Challenge the data and look for information
and data to disprove it.
Review with a cognitively diverse team (or if
you are alone, be aware of your bias and
play devil’s advocate and reframe).
If applicable, leverage predictive analytics
to run simulations or similar to test
potential decisions
and solutions.

LESSON 5
16
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About Qlik®
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management and analytics platform brings together all of an organization’s data from any source, enabling people at any skill level to use their
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ASSESS
Setup a review mechanism to monitor the
impacts of the decision after it is made and
acted upon.
Leverage that review mechanism and
fail/fix/learn fast including improvements to
data, measurement frameworks, accountability,
decisions, and anything else relevant
To learn more about Data-Informed Decision Making and explore our free courses and resources, visit
qlik.com/GetDataLiterate.

DATA MINING
CLASSICAL
Photo by ev on Unsplash

LESSON 5
CONTEXT
You don’t have to be a fancy statistician to do data mining, but you do
have to know something about what the data signiﬁes and how the
business works.
Only when you understand the data and the problem that you need to
solve can data-mining processes help you to discover useful
information and put it to use.
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LESSON 5
NINE LAWS OF DATA MINING - 1
Pioneering data miner Thomas Khabaza developed his “Nine Laws of Data Mining”
to guide new data miners as they get down to work
▸ 1 - “Business Goals Law”  
Business objectives are the origin of every data mining solution. 
A data miner is someone who discovers useful information from data to support
specific business goals. Data mining isn’t defined by the tool you use.
▸ 2 - “Business Knowledge Law” 
Business Knowledge is central to every step of the data mining process. 
You don’t have to be a fancy statistician to do data mining, but you do have to
know something about what the data signifies and how the business works.
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LESSON 5
▸ 3. “Data Preparation Law” 
Data preparation is more than half of every data mining process. 
Pretty much every data miner will spend more time on data preparation than on
analysis.
▸ 4. “No Free Lunch for the Data Miner” 
The right model for a given application can only be discovered by experiment. 
In data mining, models are selected through trial and error.
▸ 5 - “Patterns” 
There are always patterns in the data. 
As a data miner, you explore data in search of useful patterns. Understanding patterns
in the data enables you to inﬂuence what happens in the future.
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LESSON 5
▸ 6. “Insight Law” 
Data mining amplifies perception in the business domain. 
Data mining methods enable you to understand your business better than you
could have done without them.
▸ 7 - “Prediction Law” 
Prediction increases information locally by generalization. 
Data mining helps us use what we know to make better predictions (or
estimates) of things we don’t know.
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LESSON 5
▸ 8. “Value Law” 
The value of data mining results is not determined by the accuracy or stability
of predictive models. 
Your model must produce good predictions, consistently. That’s it.
▸ 9. “Law of Change” 
All patterns are subject to change. 
Any model that gives you great predictions today may be useless tomorrow.
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LESSON 5
PHASES OF THE DATA MINING PROCESS
The Cross-Industry Standard Process for
Data Mining (CRISP-DM) is the dominant
data-mining process framework. It’s an
open standard; anyone may use it.
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LESSON 5
BUSINESS UNDERSTANDING
Get a clear understanding of the problem you’re out to solve, how it impacts your
organization, and your goals for addressing it.
Tasks in this phase include:
▸ Identifying your business goals
▸ Assessing your situation
▸ Deﬁning your data mining goals
▸ Producing your project plan
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LESSON 5
DATA UNDERSTANDING
Review the data that you have, document it, identify data management and data quality
issues.
▸ Gathering data
▸ Describing
▸ Exploring
▸ Verifying quality
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LESSON 5
DATA PREPARATION
Get your data ready to use for modeling.
▸ Selecting data
▸ Cleaning data
▸ Constructing
▸ Integrating
▸ Formatting
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LESSON 5
MODELING
Use mathematical techniques to identify patterns within your data.
▸ Selecting techniques
▸ Designing tests
▸ Building models
▸ Assessing models
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LESSON 5
EVALUATION
Review the patterns you have discovered and assess their potential for business
use.
▸ Evaluating results
▸ Reviewing the process
▸ Determining the next steps
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LESSON 5
DEPLOYMENT
Put your discoveries to work in everyday business.
▸ Planning deployment (your methods for integrating data mining discoveries
into use)
▸ Reporting ﬁnal results
▸ Reviewing ﬁnal results
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DATA AGGREGATION
CLASSICAL

LESSON 5
DATA AGGREGATION
Data aggregation is the process where raw data is gathered and expressed in a summary
form for statistical analysis.
For example, raw data can be aggregated over a given time period to provide statistics. After
the data is aggregated and written to a view or report, you can analyze the aggregated data
to gain insights about particular resources or resource groups.
There are two types of data aggregation:
▸ Time aggregation - All data points for a single resource over a speciﬁed time period.
▸ Spatial aggregation - All data points for a group of resources over a speciﬁed time
period.
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LESSON 5
SUMMARY STATISTICS
When data is aggregated, groups of observations are replaced with summary statistics based on those observations.
Summary statistics are used tto communicate the largest amount of information as simply as possible.
▸ Mean
▸ Count
▸ Maximum
▸ Median
▸ Minimum
▸ Mode
▸ Range
▸ Sum
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LESSON 5
TABLES
Tables are the format in which most numerical data are initially stored and analysed and
are likely to be the means you use to organise data collected during experiments and
dissertation research.
Tables are an effective way of presenting data:
• when you wish to show how a single category of information varies when
measured at different points (in time or space).
• when the dataset contains relatively few numbers.
• when the precise value is crucial to your argument and a graph would not convey
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LESSON 5
BAR CHARTS
Bar charts are one of the most commonly
used types of graph and are used to display
and compare the number, frequency or other
measure for different discrete categories or
groups.
The bars can be drawn either vertically or
horizontally depending upon the number of
categories and length or complexity of the
category labels.
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LESSON 5
HISTOGRAMS
Histograms are a special form of bar chart
where the data represent continuous rather
than discrete categories. Since a
continuous category may have a large
number of possible values the data are
often grouped to reduce the number of data
points.
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LESSON 5
PIE CHARTS
Pie charts are a visual way of displaying how
the total data are distributed between different
categories. Pie charts should only be used for
displaying nominal data. They are generally
best for showing information grouped into a
small number of categories and are a
graphical way of displaying data that might
otherwise be presented as a simple table.
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Pie chart of populations of English native speakers

LESSON 5
LINE GRAPHS
Line graphs are usually used to show time
series data – that is how one or more
variables vary over a continuous period of
time. Line graphs are particularly useful for
identifying patterns and trends in the data
such as seasonal effects, large changes and
turning points. As well as time series data,
line graphs can also be appropriate for
displaying data that are measured over other
continuous variables such as distance.
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LESSON 5
SCATTER PLOT
Scatter plots are used to show the
relationship between pairs of quantitative
measurements made for the same object or
individual. By analysing the pattern of dots
that make up a scatter plot it is possible to
identify whether there is any systematic or
causal relationship between the two
measurements.
▸ https://www2.le.ac.uk/ofﬁces/ld/resources/
numerical-data/numerical-data
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DATA SCIENCE
WHAT IS

LESSON 5
DEFINITION
Data Science is a blend of various tools, algorithms, and machine learning
principles with the goal to discover hidden patterns from the raw data and solve
analytically complicated problems.
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LESSON 5
APPLICATION OF DATA SCIENCE
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LESSON 5
42

LESSON 5
EXPLAINING VS PREDICTING
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By 2020 more than 80 % of the data
will be unstructured. This data is
generated from different sources like
ﬁnancial logs, text ﬁles, multimedia
forms, sensors, and instruments.

LESSON 5
44https://databasetown.com/introduction-to-data-science-a-beginners-guide/#What_is_Data_Science

LESSON 5
45

LESSON 5
46
The Data Scientist has the ability to handle the crude data using the latest
technologies and techniques, can perform the necessary analysis, and can
present the acquired knowledge to his associates in an informative way.

LESSON 5
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The Data Analyst works with R, Python and SQL; the role combines technical
and analytical knowledge.

LESSON 5
48
The Data Architect integrates, centralizes, protects and maintains data
sources.

LESSON 5
49
The Statistician can be seen as the pioneer of the data science ﬁeld. It is often
he who reaps the information from the data and transforms it into actionable
insights.

LESSON 5
50
The Database Administrator ensures that the database is accessible to every
stakeholder in the organizations and performs the necessary safety measures
to keep the stored data safe.

LESSON 5
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The Business Analyst is probably the least technical proﬁle, he has a deep
understanding of the various business processes that are in place. He often
performs the role of the middle person between the business folks and the
technicians.

LESSON 5
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The Data and Analytics Manager steers the direction of the data science
team. He consolidates strong and specialized skills in a various arrangement
of advancements (SQL, R, SAS, … ) with the social aptitudes required to deal
with a group.

EXAMPLES
SOME
PHOTO BY JAREDD CRAIG ON UNSPLASH

LESSON 5
THE NY TIMES
https://www.nytimes.com/interactive/2019/11/02/us/politics/trump-twitter-
disinformation.html
54

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