Data Story Telling and VIsualization detailed Syllabus for AI&ML (AN), K scheme PDF has been taken from the MSBTE official website and presented for the diploma students. For Subject Code, Subject Name, Lectures, Tutorial, Practical/Drawing, Credits, Theory (Max & Min) Marks, Practical (Max & Min) Marks, Total Marks, and other information, do visit full semester subjects post given below.
For all other MSBTE AI&ML 3rd Sem K Scheme Syllabus PDF, do visit MSBTE AI&ML 3rd Sem K Scheme Syllabus PDF Subjects. The detailed Syllabus for data story telling and visualization is as follows.
Rationale
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Course Outcomes:
Students will be able to achieve & demonstrate the following COs on completion of course based learning
- Identify the characters in data storytelling.
- Eliminate clutter to grab audience attention.
- Construct Storytelling for the given incident.
- Transform Data to Visuals.
- Create data visualization using many distributions.
Unit I
Introduction to Data StoryTelling 1.1 Concept / Importance of Context 1.2 Exploratory vs. explanatory analysis 1.3 Who – Your audience, You, What – Action, Mechanism, Tone, How, Example 1.4 What is Data Story, make a figure for the generals 1.5 The 3-minute story, Big Idea, Storyboarding. 1.6 Visual effects of Data Story Telling -Choosing an effective visual – Simple text, Tables, Graphs, Points, Bars – Vertical bar chart, Horizontal bar chart Presentations Lecture Using Chalk-Board Case Study Video Demonstrations
Unit II
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Unit III
The process of Storytelling 3.1 Think like a designer-Affordances, Accessibility, Aesthetics, Acceptance 3.2 Dissecting model visuals – line graph, 100% stacked bars 3.3 Lessons in storytelling – The magic of story, Constructing the story, The narrative structure, The power of repetition, Tactics to help ensure that your story is clear 3.4 Pulling it all together for data storytelling 3.5 Final Thoughts – Where to go from here, Building storytelling with data competency in your team or organization Demonstration Presentations Case Study Flipped Classroom
Unit IV
Data Visualization 4.1 Introduction: Ugly, Bad, and Wrong Figures 4.2 Visualizing Data: Mapping Data onto Aesthetics 4.3 Coordinate Systems and Axes 4.4 Directory of Visualizations 4.5 Visualizing Amounts – Bar Plots 4.6 Visualizing Distributions – Histograms and Density Plots. Empirical Cumulative Distribution Functions and Q-Q Plots Hands-on Demonstration Case Study Cooperative Learning
Unit V
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Laboratory Equipment
- Hardware: Personal computer, (i3-i5 preferable), RAM minimum 4 GB onwards All
- Operating system: Windows 7 onwards All
- Software: Python IDE, Video Makers/Editors, Visualization tools All
Suggested Learning Materials
- Cole Nussbaumer Knaflic Storytelling with data – a data visualization guide for business professionals Cole Nussbaumer Knaflic Wiley ISBN: 978-1-119-00225-3
- Claus O. Wilke Fundamentals of Data Visualization O’Reilly ISBN:978-1-492-03108-6.
- Kenneth A Lambert, B.L. Juneja Fundamentals of PYTHON CENGAGE Learning, ISBN:978-81-315- 2903-4
Learning Websites & Portals
For the complete Syllabus, results, class timetable, and many other features kindly download the iStudy App
It is a lightweight, easy to use, no images, and no pdfs platform to make students’s lives easier..
For detail Syllabus of all other subjects of AI&ML, K scheme do visit AI&ML 3rd Sem Syllabus for K scheme.
For all AI&ML results, visit MSBTE AI&ML all semester results direct links.