Athens Big Data Meetup
Friday, March 18th 2016
The CUBE – Athens, Greece
From Data Visualization to Visual Analytics
Dr. Andreas S. Maniatis
Commercial Manager / Head of BI
CyberStream LTD
© Copyright 2000-2016 CyberStream LTD
Systems Integrator
Established in 2000
Strong software
engineering skills
Focus on development
of custom & reusable solutions
Partner relationship with
established technology
companies
Involvement in ΒΙ-DW-
Analytics since 1997
CyberStream LTD
The human face of technology …
Business process
automation
Unified
communications
Enterprise
networking
Portals
E-Learning
Digital
Signage
© Copyright 2000-2016 CyberStream LTD
Agenda
PART I
Visualization in Big Data [Business Intelligence, Business Analytics]
PART II
Data Visualization Techniques and Practices
PART III
Visual Analytics in Practice
Part I
Visualization in Big Data [Business Intelligence,
Business Analytics]
© Copyright 2000-2016 CyberStream LTD
Issues to be discussed
•The Big Data hype …
•The many Vs for Big Data
•Some definitions on Visualization and Analytics
•Visualization concepts and Principles
© Copyright 2000-2016 CyberStream LTD
The Data Science Hype Cycle [2014]
Gartner Hype Cycle
http://www.gartner.com/technology/research/methodologies/hype-cycle.jsp
© Copyright 2000-2016 CyberStream LTD
The Data Science Hype Cycle [2015]
Gartner Hype Cycle
http://www.gartner.com/technology/research/methodologies/hype-cycle.jsp
© Copyright 2000-2016 CyberStream LTD
•Factors demanding for usability differentiators (I):
• Flood of data – The “BIG DATA” era:
• Social Networks, Media (Facebook, Twitter, LinkedIn etc.)
• Search engines (Google etc)
• Variety of new data types / streams:
• 3 (4) V’s: Volume, Velocity, Variety, (Veracity)
• More V’s: Viability, Value, Volatility, Virtualization …
• 1 V only: Variety (Damianos Chatziantoniou)
Key usability differentiators for Visual
Analytics (I)
© Copyright 2000-2016 CyberStream LTD
IBM Data Hub
http://www.ibmbigdatahub.com/sites/default/files/infographic_file/4-Vs-of-big-data.jpg
IBM: Big Data Defined by 4 Vs
© Copyright 2000-2016 CyberStream LTD
IBM Data Hub
http://www.ibmbigdatahub.com/infographic/extracting-business-value-4-vs-big-data
IBM: Big Data Defined by 5 Vs
© Copyright 2000-2016 CyberStream LTD
“V” for Visualization
•Why Visualization is the biggest “V” of them all:
• Visualization is the key to making Big Data an integral part
of decision making
• Visualization is the only way to make Big Data accessible
to a large audience
[Storytelling and Narration]
• Visualization is essential to the analysis of Big Data so it
can be of highest value
© Copyright 2000-2016 CyberStream LTD
•Factors demanding for usability differentiators (II):
• Faster cycle times
• New devices (Smartphones, Tablets, Touch and Voice
Activated Displays, Video Walls etc.)
• The introduction of Analytics by itself
• Involve more power users in exploratory data analysis
Key usability differentiators for Visual
Analytics (II)
© Copyright 2000-2016 CyberStream LTD
Biology in Visual Perception
© Copyright 2000-2016 CyberStream LTD
•Seeing 83 percent
•Hearing 11 percent
•Touching 3 ½ percent
•Smelling 1 ½ percent
•Tasting 1 percent
•6th Sense … ?
The importance of senses
Source: Oklahoma State University
http://www.oces.okstate.edu/washita/uploaded_files/4h_Learning_Styles.doc
© Copyright 2000-2016 CyberStream LTD
The importance of context
The Washington Metro Experiment with Joshua Bell
http://www.washingtonpost.com/lifestyle/style/joshua-bell-is-playing-in-the-metro-again-this-time-you-can-plan-to-be-there/2014/09/23/7a699e28-4282-11e4-9a15-137aa0153527_story.html
© Copyright 2000-2016 CyberStream LTD
A Rabbit or a Duck?
© Copyright 2000-2016 CyberStream LTD
What is Data Visualization
Kim Rees - Founding partner of Periscopic, a socially-conscious data visualization firm
https://about.me/krees
© Copyright 2000-2016 CyberStream LTD
The Art of Visualizing Data
© Copyright 2000-2016 CyberStream LTD
Visual Analytics: A Definition
“... the science of analytical reasoning
facilitated by interactive visual interfaces …”
Relative Terms:
• Visual Analysis
• Visual Data Analysis
• Visual Data Mining
Illuminating the Path: Research and Development Agenda for Visual Analytics
James J. Thomas, Kristin A. Cook
IEEE Press, 2005
© Copyright 2000-2016 CyberStream LTD
The Scope of Visual Analytics
Source: http://www.visual-analytics.eu/faq/
© Copyright 2000-2016 CyberStream LTD
Research Fundamentals for Visual
Analytics
Source: http://www.visual-analytics.eu/faq/
© Copyright 2000-2016 CyberStream LTD
1281736875613897654698450698560498286782
9809858453822450985645894509845098096585
9091830208805989595772588875050678904567
8845789809821677654872664908560912949686
1281736875613897654698450698560498286782
980985845382245098564589450984509809656
9091830208805989595772588875050678904567
8845789809821677654872664908560912949686
The Power of Visual Analytics
1281736875613897654698450698560498286782
9809858453822450985645894509845098096565
9091830208805989595772588875050678904567
8845789809821677654872664908560912949686
= 8
= 8
= 8
= 8
32
Part II
Data Visualization Techniques and Practices
© Copyright 2000-2016 CyberStream LTD
Issues to be discussed
• Some History …
• Data Visualization Techniques
• Visualization practices to avoid
• The Visual Analytics Mantra
• Interactive Dynamics for Visual Analysis
© Copyright 2000-2016 CyberStream LTD
Some History (1)
Map from 600 BC - Babylon
© Copyright 2000-2016 CyberStream LTD
Some History (2)
Meet … Jon Snow (1 of 2)
© Copyright 2000-2016 CyberStream LTD
Some History (2)
Meet … John Snow (2 of 2)
© Copyright 2000-2016 CyberStream LTD
Some History (2)
Cholera Outbreak in London (1854)
Sources: “The Guardian” Interactive Map
http://www.theguardian.com/news/datablog/interactive/2013/mar/15/cholera-map-john-snow-recreated
© Copyright 2000-2016 CyberStream LTD
Some History (4)
Napoleon’s March to Moscow (C.J.Minard, 1869)
Sources: http://www.extremepresentation.com
http://annkemery.com/essentials/ | http://labs.juiceanalytics.com
© Copyright 2000-2016 CyberStream LTD
Visualization Practices to Avoid (1)
Can you interpret the Chart? This is better …
0
5
10
15
20
25
30
35
A B C
3D Bar Chart
0
5
10
15
20
25
30
35
40
A B C
Bar Chart
Creating More Effective Graphs
Naomi B. Robbins
Statistical Society of Ottawa, Ottawa – Ontario, December 8th 2009
© Copyright 2000-2016 CyberStream LTD
Visualization Practices to Avoid (2)
Can you sort the data? This is better …
Pie Chart
A B C D E
0
5
10
15
20
25
30
35
40
C E B D A
Bar chart sorting
Creating More Effective Graphs
Naomi B. Robbins
Statistical Society of Ottawa, Ottawa – Ontario, December 8th 2009
© Copyright 2000-2016 CyberStream LTD
Visualization Practices to Avoid (Final)
© Copyright 2000-2016 CyberStream LTD
Visualization Practices to Follow (2)
Gauges? Bullet Graph - This is better …
Gauges: The Black Sheep of Data Visualization
http://www.dundas.com/blog-post/gauges-the-black-sheep-of-data-visualization/
© Copyright 2000-2016 CyberStream LTD
Devour the Pie Chart
© Copyright 2000-2016 CyberStream LTD
Visualization Practices to Follow (Final)
Curious, what's your preference of these 4 ways to show Uber v Taxi wages? Know a better way?
Sometimes ONE Number Tells the Whole Story
If city-specific comparison is not needed, then why over-complicate?
© Copyright 2000-2016 CyberStream LTD
The Visual Analytics Process
Source: http://www.visual-analytics.eu/faq/
Part III
Visual Analytics in Practice
© Copyright 2000-2016 CyberStream LTD
Issues to be discussed
• Requirements for a Data Analytics Platform Architecture
• New methodology for Visual Applications
• Live Visual Analytics with TIBCO Spotfire
• The Data Scientist as a Story Teller
We are surrounded by data, yet most people
still make decisions without it
…visualization-based data discovery tools have far-reaching implications for how
business information is consumed….end-user organizations should adopt use as a way to
improve the success of their BI program. - Gartner, June 2011
“ “
© Copyright 2000-2016 CyberStream LTD
Technical Differentiators
Dimension-Free
Data Exploration
Data Mashup
Predictive &
Event Driven
Contextual
Collaboration
Enterprise-Class
• Visual
• Interactive
• No Constraints
• Combine Data Sources
• No Scripting
• IT Free
• Data at Rest & In Motion
• Open Source & 3rd Party
• “Two Second Advantage”
• Bookmarks
• Guided Apps
• Portals & Social Platforms
• Unmatched
Performance
• Massive
Scalability
• 24x7
Expertise
© Copyright 2000-2016 CyberStream LTD
IT Centric BI
ERP
CRM
SCM
E
T
L
OLAP
Cube
Data
Warehouse
Meta
Data
BI
Platform
Gather
Specifications
Design
Data Models
Source
the data
Load
the data
Construct
BI Metadata
Build
Reports
Publish
To User
Business
User
IT
Developer
If specifications change, repeat the process!
© Copyright 2000-2016 CyberStream LTD
Business User centric Analysis
ERP
CRM
SCM
E
T
L
OLAP
Cube
Data
Warehouse
Meta
Data
BI
Platform
Gather
Specifications
Design
Data Models
Source
the data
Load
the data
Construct
BI Metadata
Build
Reports
Publish
To User
Business
User
IT
Developer
If specifications change, repeat the process!
Connect to
Data Source
… and/or
Multiple
Data Sources
Interact &
Visualize Data
Publish/Share
w/ others
Case Study
Environmental Analytics
© Copyright 2000-2016 CyberStream LTD
Problem Description …
• Best Pilot Data Visualization on Air Quality in Athens
• Sample data from 2 measuring stations
• Want to locate “Outliers” and gain “Insights”
• Build an Interactive Visualization Dashboard
© Copyright 2000-2016 CyberStream LTD
ENVisage:
Exploratory Visual Analytics on Open Access
Environmental Data
the CyberGreen Team
Athens Green Hackathon
14/12/2012 – 16/12/2012
Presentation @ Athinais Cultural Centre
© Copyright 2000-2016 CyberStream LTD
Turning legacy reporting examples …
© Copyright 2000-2016 CyberStream LTD
Project Metrics
70%
Data
Extraction,
Cleaning and
Transforming
30%
Interactive
Visual
Analytics
Application
© Copyright 2000-2016 CyberStream LTD
… into exemplary Visual Analytics paradigms
© Copyright 2000-2016 CyberStream LTD
LIVE DEMO
© Copyright 2000-2016 CyberStream LTD
New Skills for the Data Scientist
• Data Scientist, Analyst or Statistician?
– Technical Skills
– Teamwork Skills
– Communication Skills
– Business Skills (with a little bit of empathy)
– Tool Mastery
• Story Teller?
Institute for Advances Analytics
http://analytics.ncsu.edu/
© Copyright 2000-2016 CyberStream LTD
• Math & Statistics
• Machine Learning
• Statistical Modelling
• Experiment design
• Bayesian Interface
• Supervised Learning: Decision Trees,
Random Forests, Logistic Regression
• Unsupervised Learning: Clustering,
Dimensionality Reduction
• Optimization: Gradient Descent and
Variants
• Programming And Database
• Computer Science Fundamentals
• Scripting Languages (i.e., Python etc.)
• Statistical Computing Packages (i.e., R)
• Databases: SQL and NoSQL
• Relational Algebra
• Parallel Databases and Parallel Query
Processing
• MapReduce Concepts
• Hadoop and Hive/Pig
• Custom reducers
• Experience with xaaS
Modern Data Scientist
• Domain Knowledge & Soft Skills
• Passionate about the Job
• Curious about Data
• Influence without Authority
• Hacker Mindset
• Problem Solver
• Strategic, Proactive, Creative, innovative
and Collaborative
• Communication & Visualization
• Able to Engage with Senior Management
• Story telling skills
• Translate Data-Driven Insights into
Decisions and Actions
• Visual Art Design
• R packages like ggplot and lattice
• Knowledge of any of Visualizations Tools
& Platforms (i.e., TIBCO Spotfire,
Tableau, Flare, d3.js etc.)
© Copyright 2000-2016 CyberStream LTD
A Visual Narration:
Are we Better Off than we Think?
@HansRosling
https://www.youtube.com/watch?v=jbkSRLYSojo
Thank you!
Dr. Andreas S. Maniatis
Commercial Manager / Head of BI
andreas.maniatis@cyberstream.gr
http://www.linkedin.com/in/andreasmaniatis
www.cyberstream.eu

2nd Athens Big Data Meetup - 1st Talk - From Data Visualization to Visual Analytics

  • 1.
    Athens Big DataMeetup Friday, March 18th 2016 The CUBE – Athens, Greece From Data Visualization to Visual Analytics Dr. Andreas S. Maniatis Commercial Manager / Head of BI CyberStream LTD
  • 2.
    © Copyright 2000-2016CyberStream LTD Systems Integrator Established in 2000 Strong software engineering skills Focus on development of custom & reusable solutions Partner relationship with established technology companies Involvement in ΒΙ-DW- Analytics since 1997 CyberStream LTD The human face of technology … Business process automation Unified communications Enterprise networking Portals E-Learning Digital Signage
  • 3.
    © Copyright 2000-2016CyberStream LTD Agenda PART I Visualization in Big Data [Business Intelligence, Business Analytics] PART II Data Visualization Techniques and Practices PART III Visual Analytics in Practice
  • 4.
    Part I Visualization inBig Data [Business Intelligence, Business Analytics]
  • 5.
    © Copyright 2000-2016CyberStream LTD Issues to be discussed •The Big Data hype … •The many Vs for Big Data •Some definitions on Visualization and Analytics •Visualization concepts and Principles
  • 6.
    © Copyright 2000-2016CyberStream LTD The Data Science Hype Cycle [2014] Gartner Hype Cycle http://www.gartner.com/technology/research/methodologies/hype-cycle.jsp
  • 7.
    © Copyright 2000-2016CyberStream LTD The Data Science Hype Cycle [2015] Gartner Hype Cycle http://www.gartner.com/technology/research/methodologies/hype-cycle.jsp
  • 8.
    © Copyright 2000-2016CyberStream LTD •Factors demanding for usability differentiators (I): • Flood of data – The “BIG DATA” era: • Social Networks, Media (Facebook, Twitter, LinkedIn etc.) • Search engines (Google etc) • Variety of new data types / streams: • 3 (4) V’s: Volume, Velocity, Variety, (Veracity) • More V’s: Viability, Value, Volatility, Virtualization … • 1 V only: Variety (Damianos Chatziantoniou) Key usability differentiators for Visual Analytics (I)
  • 9.
    © Copyright 2000-2016CyberStream LTD IBM Data Hub http://www.ibmbigdatahub.com/sites/default/files/infographic_file/4-Vs-of-big-data.jpg IBM: Big Data Defined by 4 Vs
  • 10.
    © Copyright 2000-2016CyberStream LTD IBM Data Hub http://www.ibmbigdatahub.com/infographic/extracting-business-value-4-vs-big-data IBM: Big Data Defined by 5 Vs
  • 11.
    © Copyright 2000-2016CyberStream LTD “V” for Visualization •Why Visualization is the biggest “V” of them all: • Visualization is the key to making Big Data an integral part of decision making • Visualization is the only way to make Big Data accessible to a large audience [Storytelling and Narration] • Visualization is essential to the analysis of Big Data so it can be of highest value
  • 12.
    © Copyright 2000-2016CyberStream LTD •Factors demanding for usability differentiators (II): • Faster cycle times • New devices (Smartphones, Tablets, Touch and Voice Activated Displays, Video Walls etc.) • The introduction of Analytics by itself • Involve more power users in exploratory data analysis Key usability differentiators for Visual Analytics (II)
  • 13.
    © Copyright 2000-2016CyberStream LTD Biology in Visual Perception
  • 14.
    © Copyright 2000-2016CyberStream LTD •Seeing 83 percent •Hearing 11 percent •Touching 3 ½ percent •Smelling 1 ½ percent •Tasting 1 percent •6th Sense … ? The importance of senses Source: Oklahoma State University http://www.oces.okstate.edu/washita/uploaded_files/4h_Learning_Styles.doc
  • 15.
    © Copyright 2000-2016CyberStream LTD The importance of context The Washington Metro Experiment with Joshua Bell http://www.washingtonpost.com/lifestyle/style/joshua-bell-is-playing-in-the-metro-again-this-time-you-can-plan-to-be-there/2014/09/23/7a699e28-4282-11e4-9a15-137aa0153527_story.html
  • 16.
    © Copyright 2000-2016CyberStream LTD A Rabbit or a Duck?
  • 17.
    © Copyright 2000-2016CyberStream LTD What is Data Visualization Kim Rees - Founding partner of Periscopic, a socially-conscious data visualization firm https://about.me/krees
  • 18.
    © Copyright 2000-2016CyberStream LTD The Art of Visualizing Data
  • 19.
    © Copyright 2000-2016CyberStream LTD Visual Analytics: A Definition “... the science of analytical reasoning facilitated by interactive visual interfaces …” Relative Terms: • Visual Analysis • Visual Data Analysis • Visual Data Mining Illuminating the Path: Research and Development Agenda for Visual Analytics James J. Thomas, Kristin A. Cook IEEE Press, 2005
  • 20.
    © Copyright 2000-2016CyberStream LTD The Scope of Visual Analytics Source: http://www.visual-analytics.eu/faq/
  • 21.
    © Copyright 2000-2016CyberStream LTD Research Fundamentals for Visual Analytics Source: http://www.visual-analytics.eu/faq/
  • 22.
    © Copyright 2000-2016CyberStream LTD 1281736875613897654698450698560498286782 9809858453822450985645894509845098096585 9091830208805989595772588875050678904567 8845789809821677654872664908560912949686 1281736875613897654698450698560498286782 980985845382245098564589450984509809656 9091830208805989595772588875050678904567 8845789809821677654872664908560912949686 The Power of Visual Analytics 1281736875613897654698450698560498286782 9809858453822450985645894509845098096565 9091830208805989595772588875050678904567 8845789809821677654872664908560912949686 = 8 = 8 = 8 = 8 32
  • 23.
    Part II Data VisualizationTechniques and Practices
  • 24.
    © Copyright 2000-2016CyberStream LTD Issues to be discussed • Some History … • Data Visualization Techniques • Visualization practices to avoid • The Visual Analytics Mantra • Interactive Dynamics for Visual Analysis
  • 25.
    © Copyright 2000-2016CyberStream LTD Some History (1) Map from 600 BC - Babylon
  • 26.
    © Copyright 2000-2016CyberStream LTD Some History (2) Meet … Jon Snow (1 of 2)
  • 27.
    © Copyright 2000-2016CyberStream LTD Some History (2) Meet … John Snow (2 of 2)
  • 28.
    © Copyright 2000-2016CyberStream LTD Some History (2) Cholera Outbreak in London (1854) Sources: “The Guardian” Interactive Map http://www.theguardian.com/news/datablog/interactive/2013/mar/15/cholera-map-john-snow-recreated
  • 29.
    © Copyright 2000-2016CyberStream LTD Some History (4) Napoleon’s March to Moscow (C.J.Minard, 1869)
  • 30.
  • 31.
    © Copyright 2000-2016CyberStream LTD Visualization Practices to Avoid (1) Can you interpret the Chart? This is better … 0 5 10 15 20 25 30 35 A B C 3D Bar Chart 0 5 10 15 20 25 30 35 40 A B C Bar Chart Creating More Effective Graphs Naomi B. Robbins Statistical Society of Ottawa, Ottawa – Ontario, December 8th 2009
  • 32.
    © Copyright 2000-2016CyberStream LTD Visualization Practices to Avoid (2) Can you sort the data? This is better … Pie Chart A B C D E 0 5 10 15 20 25 30 35 40 C E B D A Bar chart sorting Creating More Effective Graphs Naomi B. Robbins Statistical Society of Ottawa, Ottawa – Ontario, December 8th 2009
  • 33.
    © Copyright 2000-2016CyberStream LTD Visualization Practices to Avoid (Final)
  • 34.
    © Copyright 2000-2016CyberStream LTD Visualization Practices to Follow (2) Gauges? Bullet Graph - This is better … Gauges: The Black Sheep of Data Visualization http://www.dundas.com/blog-post/gauges-the-black-sheep-of-data-visualization/
  • 35.
    © Copyright 2000-2016CyberStream LTD Devour the Pie Chart
  • 36.
    © Copyright 2000-2016CyberStream LTD Visualization Practices to Follow (Final) Curious, what's your preference of these 4 ways to show Uber v Taxi wages? Know a better way? Sometimes ONE Number Tells the Whole Story If city-specific comparison is not needed, then why over-complicate?
  • 37.
    © Copyright 2000-2016CyberStream LTD The Visual Analytics Process Source: http://www.visual-analytics.eu/faq/
  • 38.
  • 39.
    © Copyright 2000-2016CyberStream LTD Issues to be discussed • Requirements for a Data Analytics Platform Architecture • New methodology for Visual Applications • Live Visual Analytics with TIBCO Spotfire • The Data Scientist as a Story Teller
  • 40.
    We are surroundedby data, yet most people still make decisions without it
  • 42.
    …visualization-based data discoverytools have far-reaching implications for how business information is consumed….end-user organizations should adopt use as a way to improve the success of their BI program. - Gartner, June 2011 “ “
  • 43.
    © Copyright 2000-2016CyberStream LTD Technical Differentiators Dimension-Free Data Exploration Data Mashup Predictive & Event Driven Contextual Collaboration Enterprise-Class • Visual • Interactive • No Constraints • Combine Data Sources • No Scripting • IT Free • Data at Rest & In Motion • Open Source & 3rd Party • “Two Second Advantage” • Bookmarks • Guided Apps • Portals & Social Platforms • Unmatched Performance • Massive Scalability • 24x7 Expertise
  • 44.
    © Copyright 2000-2016CyberStream LTD IT Centric BI ERP CRM SCM E T L OLAP Cube Data Warehouse Meta Data BI Platform Gather Specifications Design Data Models Source the data Load the data Construct BI Metadata Build Reports Publish To User Business User IT Developer If specifications change, repeat the process!
  • 45.
    © Copyright 2000-2016CyberStream LTD Business User centric Analysis ERP CRM SCM E T L OLAP Cube Data Warehouse Meta Data BI Platform Gather Specifications Design Data Models Source the data Load the data Construct BI Metadata Build Reports Publish To User Business User IT Developer If specifications change, repeat the process! Connect to Data Source … and/or Multiple Data Sources Interact & Visualize Data Publish/Share w/ others
  • 46.
  • 47.
    © Copyright 2000-2016CyberStream LTD Problem Description … • Best Pilot Data Visualization on Air Quality in Athens • Sample data from 2 measuring stations • Want to locate “Outliers” and gain “Insights” • Build an Interactive Visualization Dashboard
  • 48.
    © Copyright 2000-2016CyberStream LTD ENVisage: Exploratory Visual Analytics on Open Access Environmental Data the CyberGreen Team Athens Green Hackathon 14/12/2012 – 16/12/2012 Presentation @ Athinais Cultural Centre
  • 49.
    © Copyright 2000-2016CyberStream LTD Turning legacy reporting examples …
  • 50.
    © Copyright 2000-2016CyberStream LTD Project Metrics 70% Data Extraction, Cleaning and Transforming 30% Interactive Visual Analytics Application
  • 51.
    © Copyright 2000-2016CyberStream LTD … into exemplary Visual Analytics paradigms
  • 52.
    © Copyright 2000-2016CyberStream LTD LIVE DEMO
  • 53.
    © Copyright 2000-2016CyberStream LTD New Skills for the Data Scientist • Data Scientist, Analyst or Statistician? – Technical Skills – Teamwork Skills – Communication Skills – Business Skills (with a little bit of empathy) – Tool Mastery • Story Teller? Institute for Advances Analytics http://analytics.ncsu.edu/
  • 56.
    © Copyright 2000-2016CyberStream LTD • Math & Statistics • Machine Learning • Statistical Modelling • Experiment design • Bayesian Interface • Supervised Learning: Decision Trees, Random Forests, Logistic Regression • Unsupervised Learning: Clustering, Dimensionality Reduction • Optimization: Gradient Descent and Variants • Programming And Database • Computer Science Fundamentals • Scripting Languages (i.e., Python etc.) • Statistical Computing Packages (i.e., R) • Databases: SQL and NoSQL • Relational Algebra • Parallel Databases and Parallel Query Processing • MapReduce Concepts • Hadoop and Hive/Pig • Custom reducers • Experience with xaaS Modern Data Scientist • Domain Knowledge & Soft Skills • Passionate about the Job • Curious about Data • Influence without Authority • Hacker Mindset • Problem Solver • Strategic, Proactive, Creative, innovative and Collaborative • Communication & Visualization • Able to Engage with Senior Management • Story telling skills • Translate Data-Driven Insights into Decisions and Actions • Visual Art Design • R packages like ggplot and lattice • Knowledge of any of Visualizations Tools & Platforms (i.e., TIBCO Spotfire, Tableau, Flare, d3.js etc.)
  • 57.
    © Copyright 2000-2016CyberStream LTD A Visual Narration: Are we Better Off than we Think? @HansRosling https://www.youtube.com/watch?v=jbkSRLYSojo
  • 58.
    Thank you! Dr. AndreasS. Maniatis Commercial Manager / Head of BI andreas.maniatis@cyberstream.gr http://www.linkedin.com/in/andreasmaniatis www.cyberstream.eu