data science

1. Course : ISYE8015– Selected Topic in Industrial Eng.
Period : June 2020
An Introduction into Data Science
Session I
D6344 – Muhammad Asrol

2. Topic Outline
1. Learning outcomes in this course
2. What is data science
3. Evolutions of data science
4. Frameworks of data science in this course
MUHAMMAD ASROL

3. What will we get?
• This course comprise discussion in data science and its
application in industry. The primary purpose of this course it to
bridging students to the basic and advance applications of data
science technique for industrial management and business.
• In this industry 4.0 era, industry requires an effective and efficient
decision-making process. At this point, data science provides many
technique and insight to process related data in industry to produce
an ideal decision. Using data acquisitions for decision-making in
industry within data science technique may improve the
industrial management.
• This course provides insights and applications of data science in
industries as the advance selected topics in industrial engineering.
To achieved the goal, this course delivers basic concept,
applications, case study and modelling of data science for its
applications in solving industrial problem.

4. Learning Outcomes
1. To apply core theory of selected topics in
industrial engineering disciplines
2. To differentiate selected topics in industrial
engineering disciplines in different settings and
case studies
3. To analyse the most appropriate methods
among selected topics in industrial engineering
disciplines
4. To apply the most appropriate methods to solve
real engineering problems

5. What is Data Science ?
Source: dqlab.id

6. Data Science Skills
Requirement in Indusries

7. What is Data Science ?
Data science is new term to replace formers terms
like Knowledge Discovery in Database and Data
Mining
All of these terms are semiautomated process
which aims to analyze a huge volume of data, find
and recognize pattern to solve and discovery new
knowledge.

8. A data scientist is the adult version of kid who
can’t stop asking ‘why’
Russ Thompson

9. Data Science position
Data
Engineer
Traditional
Research
Machine
Learning
Substantive
Expertise
(marketing)
Programming,
Hacking, Coding
Statistics
DATA
SCIENCE

10. A general framework of Data Science
Communicating
visualizing the
Modeling data using
appropriate algorithm
(KDD)
Questions and
exploring the data

11. Data Science vs Business Intelligence
Data Science
Business Intelligence
Data Source Structured data Unstructured data
Method
Perception
Approach
Tools
Analytical Scientific
Looking backward Looking forward
Statistics and visualization Statistics, machine –
Learning, visualization
Pentaho, Ms. BI, QlickView R, TensorFlow

12. Data science
components
Statistics
Data
Engineering
Advance
Computing
Data
Visualizaion

13. Application of Data Science

14. Application of Data Science
Business Health care Urban living
Insight
Solution
Businesses are using
data science to optimize
their operations and
better meet customer
expectations
Using social data to select
successful retail locations
Electronic health record
and earlier detection are
on the horizon to build
an efficient health care
Medical exams by
bathroom mirrors
More people will live in
cities than rural areas.
Urban informatics
combines data science
assist to facing the growth
Instruments cities

15. Application of Data Science
Christoph M. Flath*, Nikolai Stein (2018)

16. What will we learn here?
1. Data preparing and preprocessing
2. Classification
3. Data association
4. Data clustering
5. Data presentation

17. Evaluation (regular)
Assessment Activity Weight
Learning Outcomes
1 2 3 4 5 6
Individual assignment 20% √ √ √ √ √ √
Group presentation 10% √ √
Team paper section 1 20% √ √ √
Team paper section 2 25% √ √ √ √
Final term exam (UAS) 25% √ √ √

18. Evaluation (Online)
Assessment Activity Weight
Learning Outcomes
1 2 3 4 5 6
Personal assignment 30% √ √ √ √ √ √
Team assignment 20% √ √
Final exam 30% √ √ √
Forum discussion 10% √ √ √ √
Attendance 10% √ √ √

19. Thank you