Design Thinking Notes

 19EID132: DESIGN THINKING
B.Tech - students
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Design is a realization of a concept or idea into a configuration, drawing or a product. Design Thinking is cognitive and practical processes by which design concepts are developed by designers. Innovation is a new idea or a new concept. Product development is the creation of a new or different product that offers new benefits to the end user. This course introduces the design thinking in product innovation.

Course Objectives:

• To familiarize product design process
• To introduce the basics of design thinking
• To bring awareness on idea generation
• To familiarize the role of design thinking in services design

Course outcomes:

After completing this course, the student will be able to

• Innovate new methods in product development.
• Apply design thinking in developing the new designs.
• Select ideas from ideation methods in new product development.
• Use design thinking in developing software products.
• Apply principles of design thinking in service design. 

Unit 1

Introduction to design, characteristics of successful product development, product development process, identification of opportunities, product planning, Innovation in product development.

Learning outcomes:

After completing this unit, the student will be able to

• identify characteristics of successful product development.
• Identify opportunities for new product development.
• plan for new product development

Unit 2

Design Thinking: Introduction, Principles, the process, Innovation in Design Thinking, benefits of Design thinking, design thinking and innovation, case studies.

Learning outcomes:

After completing this unit, the student will be able to

• Explain the principles of Design Thinking.
• Identify the benefits of Design Thinking.
• use innovations in Design Thinking

Unit 3

Idea generation: Introduction, techniques, Conventional methods, Intuitive methods, Brainstorming, Gallery method, Delphi method, Synectics etc, Select ideas from ideation methods, case studies.

Learning outcomes:

After completing this unit, The student will be able to

• Explain the techniques in idea generation.
• Select ideas from ideation methods.
• Identify the methods used in idea generation in some case studies

Unit 4

Design Thinking in Information Technology, Design Thinking in Business process model, Design Thinking for agile software development, virtual collaboration, multi user and multi account interaction, need for communication, TILES toolkit, Cloud implementation.

Learning outcomes:

After completing this unit, the student will be able to

• Use design thinking in business process model.
• Apply design thinking for Agile software development.
• use TILES toolkit

Unit 5

Design thinking for service design: How to design a service, Principles of service design, Benefits of service design, Service blueprint, Design strategy, organization, principles for information design, principles of technology for service design.

Learning outcomes:

After completing this unit, the student will be able to

• Use principles of service design.
• Explain the benefits of service design.
• Apply principles of technology for service design.

Books:

    1.   Pahl, Beitz, Feldhusen, Grote – Engineering Design: a systematic approach, Springer, 2007
2.   Christoph Meinel and Larry Leifer, Design Thinking, Springer, 2011
3.   Aders Riise Maehlum - Extending the TILES Toolkit – from Ideation to Prototyping
4.   http://www.algarytm.com/it-executives-guide-to-design-thinking:e-book.
5.   Marc stickdorn and Jacob Schneider, This is Service Design Thinking, Wiely, 2011

 

 

Data Mining and Data Warehousing

(M.Tech - CSE)

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Course Objectives

•     Understand the basic concepts of data mining.

•     Familiarize with the data mining functionalities.

•     Assess the strengths and weaknesses of various Cluster Analysis techniques.

Course Outcomes (COs)

1.   Implement the data warehouse architecture.

2.   Explain the functionalities of data mining.

3.   Explore the Association Rule Mining techniques.

4.   Identify the association rules and techniques.

5.   Describe the Cluster and Outlier Analysis process.

Module -1 

Data Warehousing - Operational Database Systems vs. Data Warehouses Data Warehouse Architecture concepts of dimensions, facts, cubes, attribute, hierarchies, star and snowflake schema, Multidimensional Data Model – Schemas for Multidimensional Databases  OLAP Operations.

Module -2  

Indexing – OLAP queries & Tools, Introduction to KDD process – Knowledge Discovery from Databases - Need for Data Preprocessing – Data Cleaning.

Module -3 

Introduction - Data Mining Functionalities - Association Rule Mining - Mining Frequent Item sets with and without Candidate Generation - Mining Various Kinds of Association Rules - Constraint-Based Association Mining - Classification vs. Prediction – Data preparation for Classification and Prediction – Classification by Decision Tree Introduction – Bayesian Classification – Rule Based Classification.

Module -4 

Classification by Back Propagation – Support Vector Machines – Associative Classification – Lazy Learners - Other Classification Methods – Prediction – Accuracy and Error Measures – Evaluating the Accuracy of a Classifier or Predictor – Ensemble Methods – Model Section.

Module -  5

Cluster Analysis: - Types of Data in Cluster Analysis – A Categorization of Major Clustering Methods – Partitioning Methods – Hierarchical methods – Density-Based Methods – Grid-Based Methods. Model-Based Clustering Methods – Clustering High- Dimensional Data – Constraint-Based Cluster Analysis – Outlier Analysis.

Text Book

1. Data Mining – Concepts and Techniques, Jiawei Han and Micheline Kamber Second Edition, Elsevier 2006.

Reference Book

1. Jiawei Han & Micheline Kamber, Data Mining – Concepts and Techniques,  2nd Edition, Morgan Kaufman Publishers, Elsevier, 2006.
2. Pang-Ning Tan, Michael Steinbach and Vipin Kumar, Introduction to Data Mining  Pearson education.
3. Arun K Pujari, Data Mining Techniques –University Press.
4. Sam Anahory & Dennis Murray, Data Warehousing in the Real World, Pearson Edn Asia.
5. Margaret H Dunham, Data Mining Introductory and advanced topics ,Pearson Education

17CSDE652 : Mobile Computing (3:0:0)

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Part- A

Unit: 1

Mobile Devices And Systems, Architectures: Mobile phones, Handheld Devices, Operating Systems, Limitations of Mobile Devices. GSM – Services and System Architectures, Radio Interfaces, Protocols, Localization, Calling, Handover, General Packet.

Unit: 2

CDMA based communication and Mobile IP Network Layer: introduction to CDMA – based Systems, IP and Mobile IP Network Layers Packet Delivery and Handover Management, Location Management, Registration, Tunneling and Encapsulation.

Unit: 3

Databases: Database Hoarding Techniques, Data Caching, Client – Server Computing and Adaptation, Transactional Models, Query Processing, Data Recovery Process, Issues relating to Quality of Service.

Unit: 4           

Data Synchronization in Mobile Computing Systems: Synchronization, Synchronization Protocols, SyncML – Synchronization Language for Mobile Computing, Synchronized Multimedia Markup Language (SMIL).

Part- B

Unit: 5

Data Dissemination and Broadcasting Systems: Communication Asymmetry, Classification of Data – Delivery Mechanisms, Data Dissemination Broadcast Models, Digital Audio Broadcasting, Digital video Broadcasting.

Unit: 6           

Getting started with Mobility:  Mobility landscape, Mobile platforms, Mobile apps development, Overview of Android platform, setting up the mobile app development environment along with an emulator, a case study on Mobile app development.

Unit: 7           

Building blocks of mobile apps 1: App user interface designing – mobile UI resources (Layout, UI elements, Draw-able, Menu), Activity- states and life cycle, interaction amongst activities.

Unit: 8           

Building blocks of mobile apps 2: App functionality beyond user interface -  Threads, Async task, Services – states and life cycle, Notifications, Broadcast receivers, Telephony and SMS APIs.

Text Book:

1.      Raj Kamal, Mobile Computing, 2007, Oxford University Press

2.      Mobile Apps Development, Anubhav Pradhan, Anil V Deshpande  , 2014, 1^st edition.

 

Reference Book:

1.   Asoke K. Talkukder, Roopa R Yavaga, Mobile Computing: Technology, Applications and Service Creation, Tata McGraw Hill, 2005.

2.   Reza B’Far, Mobile Computing Principles: Designing and Developing Mobile Applications with UML and XML, 5^th Edition Cambridge University press, 2006.

JAIN (Deemed-To-Be-University)
PhD Core Subject Materials

(Aug 2019 Batch)

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Big Data Analytics

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(All unit PPT)

Syllabus

PART A

UNIT1
What is big data? And why is it Important?: A flood of Mythic “start-up” proportions; Big data is more than Merely big; Why now; A convergence of key trends; Relatively speaking; A wider variety of data; The expanding universe of unstructured data; setting the tone at the top.

UNIT2
Industry Examples of Big Data-I: Digital marketing and non – line world; Don’t abdicate relationships; Is IT losing control of web analytics? Database marketers, pioneers of big data; Big data and the new school of marketing; Consumers have changed. So must marketers;

UNIT3
Industry Examples of Big Data-II: The right approach: cross-channel life cycle marketing; Social and affiliate marketing; Empowering marketing with social intelligence; Fraud and big data; Risk and big data; Credit risk management; Big data and algorithmic trading.

UNIT4
Big Data Technology-I: The elephant in the room: Hadoop parallel world old Vs. new approaches; Data discovery: work the way people’s minds work; Open source technology for big data analytics; The cloud and big data; Predictive analytics moves into the limelight; Software as a service BI. Mobile business intelligence is going mainstream; Ease of mobile application deployment; Crowd sourcing analytics; Inter – and Trans-firewall analytics.

PART B

UNIT5
Big Data Technology-II and Information Management: R&D approach helps adopt new technology; Adding big data technology into the mix; Big data technology terms; Data size. The big data foundation; Big data computing platforms; Big data computation; More on big data storage; Big data computational limitations; Big data emerging technologies.

UNIT6
Business Analytics: The last mile in data analysis; Geospatial intelligence will make your life better; Listening: Is it signal of noise?; Consumption of analytics; From creation to consumption; Visualizing: How to make it consumable; Organizations are using data visualization as a way to take immediate action; Moving from sampling to using all the data; Thinking outside the box; 360o modeling; Need for speed; Let’s get scrappy; What technology is available?; Moving from beyond the tools to analytic applications.

UNIT7
The People part of the equation: Rise of the data scientist; Learning over knowing; Agility; Scale and convergence; Multidisciplinary talent; Innovation; Cost effectiveness; Using deep math, science and computer science; The 90/10 rule and critical thinking; Analytic talent and executive buy-in; Developing decision sciences talent; Setting up the right organizational structure for institutionalizing analytics.

UNIT8
Data Privacy and Ethics: The privacy landscape; The great data grab isn’t new; Preferences, personalization and relationships; Rights and responsibility; Playing in a global sandbox; Conscientious and conscious responsibility;


TEXT BOOKS
1. Michael Minelli, Michele Chambers, Big Data, Big Analytics: Emerging Business Intelligence and Analytic Trends for Today's Businesses Hardcover, 1st edition Wiley C/O series, 2013.

REFERENCE BOOKS
1. Viktor Mayer-Schonberger, Kenneth Neil Cukier: Big Data Are volution that Will Transform, How We Live Work And Think, 1st edition, Hachette India, 2013.