FIT 1043 Final Notes w1 to w11 - Summary Introduction to
Data Science Concepts 2025 -2026 exam review solution
Monash University
Week 1: Introduction to Data Science
Data Science:
• The extraction of knowledge from data, which is the continuation of the field
data mining and predictive analytics.
• An inter-disciplinary field that uses scientific methods, process, algo, and
system to extract knowledge and insights from structured C unstructured data.
• Related to data mining and big data.
• The ability to take data C: (from Hal Varian)
• Understand
• Process
• Extract value
• Visualize
• Communicate
Big Data:
• Broad term for large/complex data that traditional data processing applications
are inadequate.
• A field that treats ways to analyse, systematically extract info from/deal with
data that are too large to be dealt with by traditional data processing
applications.
• Combination of diff skill
sets
• Diverse skills are needed.
,• Concerned with the development of algo and technq that allow computer to
learn.
• Concerned with building computer programs that can learn, with
computational output
• With underlying theory in stat
,Importance of ML: useful when….
• Human expertise unavailable (E: Martian exploration)
• Human cnt explain expertise/explanation is incomplete, need tuning (E:
speech recognition)
• Many solutions need to be adapted automatically (E: user personalisation)
• Situation changes over time (E: junk email)
• There’re large amount of data (E: discover astronomical objects)
• Human expensive to use for work (E: handwritten zipcode recognition)
Data Science Process:
The process:
1. Pitching ideas: ds projects -> investors/managers
2. Collecting data
3. Integration: data from many sources
4. Interpretation: data described using database schema
5. Governance: caring for data C subjects, manage data standards C format (E:
storage, privacy, sharing, safety, ethics)
6. Engineering: back-end work
7. Wrangling: Inspect C clean data
8. Modelling: propose conceptual/mathematical/functional model, analyse model
with tools, statistics/machine learning works on data
9. Visualisation: to interpret C present result, choose appropriate visualisation
10. Operationalize: put result to work
Standard value chain:
, 1. Collect: get data
2. Wrangle: Data pre-processing, cleaning
3. Analyse: Discovery (learning, visualisation, etc)
4. Present: Argue the case that the results are useful
5. Engineering: Storage and computational resources
6. Governance: Overall management of data
7. Operationalize: put result to work
Relationship of DS to other disciplines:
Data Engineering:
• Build scalable system for storage, processing data.
• Focus on: Hadoop, Databases, Distributed processing, Datalakes, Cloud
computing, GPUs, Data wrangling….
Data Analyst:
• Perform analysis and understand results.
• Focus on: R, ML, computational statistics, Visualisation….
Data management:
• Manage data through lifecycle.
• Focus on: ANDS, Ethics, Privacy, Providence, Curation, Backup, Governance…
Week 2: Data Scientist Role and Skills, Impact of DS G Business Models
with Data
Python’s Role/Importance in DS:
• Easy to learn
• Flexible and multipurpose
• Great libraries
Data Science Concepts 2025 -2026 exam review solution
Monash University
Week 1: Introduction to Data Science
Data Science:
• The extraction of knowledge from data, which is the continuation of the field
data mining and predictive analytics.
• An inter-disciplinary field that uses scientific methods, process, algo, and
system to extract knowledge and insights from structured C unstructured data.
• Related to data mining and big data.
• The ability to take data C: (from Hal Varian)
• Understand
• Process
• Extract value
• Visualize
• Communicate
Big Data:
• Broad term for large/complex data that traditional data processing applications
are inadequate.
• A field that treats ways to analyse, systematically extract info from/deal with
data that are too large to be dealt with by traditional data processing
applications.
• Combination of diff skill
sets
• Diverse skills are needed.
,• Concerned with the development of algo and technq that allow computer to
learn.
• Concerned with building computer programs that can learn, with
computational output
• With underlying theory in stat
,Importance of ML: useful when….
• Human expertise unavailable (E: Martian exploration)
• Human cnt explain expertise/explanation is incomplete, need tuning (E:
speech recognition)
• Many solutions need to be adapted automatically (E: user personalisation)
• Situation changes over time (E: junk email)
• There’re large amount of data (E: discover astronomical objects)
• Human expensive to use for work (E: handwritten zipcode recognition)
Data Science Process:
The process:
1. Pitching ideas: ds projects -> investors/managers
2. Collecting data
3. Integration: data from many sources
4. Interpretation: data described using database schema
5. Governance: caring for data C subjects, manage data standards C format (E:
storage, privacy, sharing, safety, ethics)
6. Engineering: back-end work
7. Wrangling: Inspect C clean data
8. Modelling: propose conceptual/mathematical/functional model, analyse model
with tools, statistics/machine learning works on data
9. Visualisation: to interpret C present result, choose appropriate visualisation
10. Operationalize: put result to work
Standard value chain:
, 1. Collect: get data
2. Wrangle: Data pre-processing, cleaning
3. Analyse: Discovery (learning, visualisation, etc)
4. Present: Argue the case that the results are useful
5. Engineering: Storage and computational resources
6. Governance: Overall management of data
7. Operationalize: put result to work
Relationship of DS to other disciplines:
Data Engineering:
• Build scalable system for storage, processing data.
• Focus on: Hadoop, Databases, Distributed processing, Datalakes, Cloud
computing, GPUs, Data wrangling….
Data Analyst:
• Perform analysis and understand results.
• Focus on: R, ML, computational statistics, Visualisation….
Data management:
• Manage data through lifecycle.
• Focus on: ANDS, Ethics, Privacy, Providence, Curation, Backup, Governance…
Week 2: Data Scientist Role and Skills, Impact of DS G Business Models
with Data
Python’s Role/Importance in DS:
• Easy to learn
• Flexible and multipurpose
• Great libraries
