Lecture 1 – Course Organisation & ICT Trends – October 28th 2024
Overview
- Why data science
- DS vs Business intelligence
- DS ‘pipeline’ → exam question
- Data science process
Why data science
- Worldwide growing interest
- Data science affects many fields
- Exploding demand for data scientists
- More data created than ever before
- Large computing power is easily available
- New programming tools (e.g. python, R, MS Azure)
- Large skills demand meaning more people are looking to jump into the field
What is data science
- Data science is inter-disciplinary
- Uses scientific methods, processes, algorithms and systems to extract knowledge
Data science vs. Business intelligence
Data science pipeline / process
,Activities
- data science
- data munging/wrangling
- storage and governance
- exploration and presentation
- analysis
Course schedule
Final Case Report
- Groups of 4-5
- Select your own data set
- Form your group and sign up on BS
- Based on assignment write a data science report
- Max 10 pages (5000 words)
Examination
- Weekly submission on Fridays
- Written exam (50 MC questions)
- Each component needs a minimum of 5.5
The 4 industrial revolutions
- Industry 1.0: development of the steam engine (1769)
- Industry 2.0: electricity, mass production, division of labour (lopendeband-werk) (1870)
- Industry 3.0: digital revolution (1969), invention of the computer, automated information
processing and the development of programming languages
- Industry 4.0: is leading to novel innovations with autonomous connected smart devices
Developments in technology have also led to agriculture 4.0
A disruptive innovation is an innovation (e.g. mass production of cars) that creates a new market
and value market and eventually disrupts an existing market and value market.
- Digitalization has a disruptive impact and has shaped many disciplines including life sciences
and agriculture
,Malthsian catastrophe = argues that human population is growing exponentially while earth’s
resources are growing at a much slower rate. Hence, in the near future the world has to face with
long periods of hunger. But he didn’t take the exponential growth of food supply into account.
Three waves of computing
- Mainframe = one computer, many people
- PC = one person, one computer
- Ubiquitous computing = one person, many computers
Internet of things (IOT) = physical object + controller, sensors, actuators + internet
- IoT is the network of physical objects embedded with electronics, software, sensors and
network connectivity, which enables these objects to collect and exchange data
Development of smart products
From product to smart product to connected product
Big data
- Are data sets that are so large or complex that traditional data processing is inadequate
- Data that is too big to be processed
- Can be described with the four V’s
The 4 V’s of big data
- Volume: there is a lot of data around
- Variety: (semi)structured and unstructured data (different formats of data)
- Velocity: how fast the data is generated, processed (e.g. how many social media posts per day?)
- Veracity: refers to the quality and reliability of the data (source, biased, incomplete?)
- Value: focusses on extracting meaningful insights from data that create value
, What to do with Big Data – Analytics
- Analytics is the discovery, interpretation, and communication of meaningful patterns in data
- Data: raw facts that have no meaning on their own (text, numbers, symbols)
- Information: data that has been processed within a context to give it meaning
- Knowledge: data or information that have been organised and processed
- Wisdom: advanced insights
Recapitulation
- Industrial revolutions were triggered by innovations in engineering and technology
- The 1st and 2nd IR led to industrialisation which lad a disruptive impact in business domains
- The 3rd IR (digital) led to innovations that helped to automate many humans’ tasks
- The 4th IR or Industry 4.0 is leading to novel innovations with autonomous connected smart
systems and have led to Agriculture 4.0
- Digitalization has a disruptive impact and has shaped many disciplines including the life
sciences and agriculture
- Advancements in ICT have a direct impact on how we store and process data and support our
decision making
Overview
- Why data science
- DS vs Business intelligence
- DS ‘pipeline’ → exam question
- Data science process
Why data science
- Worldwide growing interest
- Data science affects many fields
- Exploding demand for data scientists
- More data created than ever before
- Large computing power is easily available
- New programming tools (e.g. python, R, MS Azure)
- Large skills demand meaning more people are looking to jump into the field
What is data science
- Data science is inter-disciplinary
- Uses scientific methods, processes, algorithms and systems to extract knowledge
Data science vs. Business intelligence
Data science pipeline / process
,Activities
- data science
- data munging/wrangling
- storage and governance
- exploration and presentation
- analysis
Course schedule
Final Case Report
- Groups of 4-5
- Select your own data set
- Form your group and sign up on BS
- Based on assignment write a data science report
- Max 10 pages (5000 words)
Examination
- Weekly submission on Fridays
- Written exam (50 MC questions)
- Each component needs a minimum of 5.5
The 4 industrial revolutions
- Industry 1.0: development of the steam engine (1769)
- Industry 2.0: electricity, mass production, division of labour (lopendeband-werk) (1870)
- Industry 3.0: digital revolution (1969), invention of the computer, automated information
processing and the development of programming languages
- Industry 4.0: is leading to novel innovations with autonomous connected smart devices
Developments in technology have also led to agriculture 4.0
A disruptive innovation is an innovation (e.g. mass production of cars) that creates a new market
and value market and eventually disrupts an existing market and value market.
- Digitalization has a disruptive impact and has shaped many disciplines including life sciences
and agriculture
,Malthsian catastrophe = argues that human population is growing exponentially while earth’s
resources are growing at a much slower rate. Hence, in the near future the world has to face with
long periods of hunger. But he didn’t take the exponential growth of food supply into account.
Three waves of computing
- Mainframe = one computer, many people
- PC = one person, one computer
- Ubiquitous computing = one person, many computers
Internet of things (IOT) = physical object + controller, sensors, actuators + internet
- IoT is the network of physical objects embedded with electronics, software, sensors and
network connectivity, which enables these objects to collect and exchange data
Development of smart products
From product to smart product to connected product
Big data
- Are data sets that are so large or complex that traditional data processing is inadequate
- Data that is too big to be processed
- Can be described with the four V’s
The 4 V’s of big data
- Volume: there is a lot of data around
- Variety: (semi)structured and unstructured data (different formats of data)
- Velocity: how fast the data is generated, processed (e.g. how many social media posts per day?)
- Veracity: refers to the quality and reliability of the data (source, biased, incomplete?)
- Value: focusses on extracting meaningful insights from data that create value
, What to do with Big Data – Analytics
- Analytics is the discovery, interpretation, and communication of meaningful patterns in data
- Data: raw facts that have no meaning on their own (text, numbers, symbols)
- Information: data that has been processed within a context to give it meaning
- Knowledge: data or information that have been organised and processed
- Wisdom: advanced insights
Recapitulation
- Industrial revolutions were triggered by innovations in engineering and technology
- The 1st and 2nd IR led to industrialisation which lad a disruptive impact in business domains
- The 3rd IR (digital) led to innovations that helped to automate many humans’ tasks
- The 4th IR or Industry 4.0 is leading to novel innovations with autonomous connected smart
systems and have led to Agriculture 4.0
- Digitalization has a disruptive impact and has shaped many disciplines including the life
sciences and agriculture
- Advancements in ICT have a direct impact on how we store and process data and support our
decision making
