20200713123139_PPT1-An introduction into Data Science-R1.PPT
- 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
- 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