Unit I
Installation of Jupiter notebook and Spider-install various visualization packages. Summarizing of data-mean, median, mode, midrange, range, IQR, five number summary, boxplot, standard deviation, variance, q-q plot, cosine similarity.
| Course Name | Data Visulization |
| Course Code | 25CSA384 |
| Program | B. Sc. in Physics, Mathematics & Computer Science (with Minor in Artificial Intelligence and Data Science) |
| Semester | CS Lab |
| Campus | Mysuru |
Installation of Jupiter notebook and Spider-install various visualization packages. Summarizing of data-mean, median, mode, midrange, range, IQR, five number summary, boxplot, standard deviation, variance, q-q plot, cosine similarity.
Preparation and cleaning of data-missing value, smoothing, regression, clustering.
Integration of data-chi-square test, Data transformation – normalization
Reduction of data- PCA, histogram, sampling.
Objectives: This course is all about data visualization, the art and science of turning data into readable graphics. We’ll explore how to design and create data visualizations based on data available and tasks to be achieved. This process includes data modelling, data processing, mapping data attributes to graphical attributes, and strategic visual representation based on known properties of visual perception as well as the task.
Course Outcomes
CO1. understand the data summarization using statistical measures.
CO2. Prepare the data for processing using various cleaning procedures.
CO3. Learn to implement the correlation of data and transform data in a standard format.
CO4. explore the implementation of reduction of huge data in to reduced format using various methods.
CO – PO Mappings:
| CO/PO | PO1 | PO2 | PO3 | PO4 | PO5 | PO6 | PO7 | PO8 | PO9 | PO10 | PSO1 | PSO2 | PSO3 | PSO4 |
| CO1 | 3 | 2 | 3 | 3 | 3 | – | 2 | – | 1 | – | – | 2 | 3 | 3 |
| CO2 | 3 | 2 | 3 | 3 | 3 | – | 2 | – | 1 | – | – | 2 | 3 | 3 |
| CO3 | 3 | 3 | 3 | 3 | 3 | – | 1 | 1 | – | – | 1 | 2 | 3 | 3 |
| CO4 | 3 | 2 | 3 | 3 | 3 | 1 | 1 | – | 1 | 1 | 3 | 3 | 3 |
TEXTBOOKS/ REFERENCES:
1. Jiawei Han, Micheline Kamber and Jian Pei, “Data mining concepts and Techniques”, Third Edition, Elsevier Publisher, 2006.
2. K.P.Soman, Shyam Diwakar and V.Ajay, “Insight into data mining Theory and Practice”, Prentice Hall of India, 2006.
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