Introduction to Data Analysis and Visualization (ZD-2401)

Bachelor of Digital Science, Universiti Brunei Darussalam, Digital Science, 2022

Students will learn the latest technologies including Tableau in the field of data analytics.


  • Data pre-processing: data cleaning, handling missing data, graphical methods for identifying outliers, measures of centre and spread
  • Exploratory data analysis: exploring categorical and numeric variables, exploring multivariate relationships
  • Data analytics tool: basic commands, graphics, indexing data, loading data, graphical and numerical summaries
  • Statistical learning: regression versus classification problems, bias-variance trade-off; simple linear regression, multiple linear regression, logistic regression, leave-one-out cross-validation, k-fold cross-validation
  • Data Visualization: Connecting to data, Dimension and Measure, Filtering and Sorting, Aggregation, Calculated Fields, Symbol Map, Trend Lines, Forecasting, Dashboard and Story
  • Case study using visualization tool: apply knowledge on real dataset and create a dashboard to present a story



Lesson 1Connecting to Data
Visualization - Introduction
Lesson 2Filtering and Sorting
Lesson 3Calculated Fields
Symbol Map
Trend Lines
Dashboard and Story
Project Discussion


Lesson 4Descriptive Statistics
Mean Median Mode
Skewed Distribution
Range, Quartiles and Interquartile Range
Variance and Standard Deviation
Lesson 5Introduction to R
Lesson 6Introduction to R cont.. loading data
Statistical Learning
Lesson 7Statistical Learning cont supervised learning
Lesson 8Simple Linear Regression
Multiple Linear Regression
Lesson 9Multiple Linear Regression cont Deciding on Important Variables
Lesson 10Linear Regression Lab
Lesson 11Other Considerations in Regression Model
Lesson 12Linear Regression Lab Contd Interaction Terms
Lesson 13R Commands
Lesson 14Logistic Regression
Logistic Regression Lab
Lesson 15Logistic Regression Lab contd
Lesson 16Resampling lab
Lesson 17dplyr contd additional aesthetics
Lesson 18dplyr contd line graph
Lesson 19Plotly
Lesson 20Plotly cont customizing graphics
Lesson 21Plotly cont background
Lesson 22Shiny
Lesson 23Visualization Best Practices
Lesson 24Visualization Best Practices Contd



  • Coursework: 100%
    • Two Lab Tests (30%)
    • Two Class Tests (30%)
    • Two Class Quizzes (20%)
    • One Project (20%)

Academic Calendar

Jan 4 - Jan 9Fresher’s Week
Jan 10 - Feb 27Classes
Feb 28 - Mar 6Mid Semester Break
Mar 7 - Apr 24Classes
Apr 25 - May 8Revision Week
May 9 - May 22Examination
  • Monday 9:50-11:40 and Tuesday 9:50-11:40