Introduction to Data Analytics (ZD-1201)

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

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


  • Data Pre-processing: Data Cleaning, Handling Missing Data, Graphical Methods for Identifying Outliers, Measures of Centre and Spread, Data Transformation
  • Exploratory Data Analysis: Exploring Categorical and Numeric Variables, Exploring Multivariate Relationships, Deriving New Variables: Flag and Numeric Variables, Using EDA to Investigate Correlated Predictor Variables
  • Univariate and Multivariate Statistical Analysis: Descriptive Statistics Measures, Statistical Approaches to Estimation and Prediction, Statistical Inference, Confidence Interval Estimation of the Mean and Proportion, Hypothesis Testing for the Mean and Proportion, Statistical tests (t-Test, Chi-Square, ANOVA), Simple and Multivariate Regression
  • 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
  • Introduction to Programming environment for data analytics: Data Wrangling: Data Gathering, Data Cleaning, Data Assessing, Clustering, Data Visualization: Univariate, Bivariate and Multivariate Exploration
  • Case Study: Apply knowledge to real world dataset and create slide deck



Lesson 1 (Jan 11, 2022)Connecting to Data
Visualization - Introduction
Lesson 2 (Jan 13, 2022)Filtering and Sorting
Lesson 3 (Jan 18, 2022):Calculated Fields
Symbol Map
Trend Lines
Dashboard and Story
Project Discussion

Python Programming

Lesson 4Python setup
Introduction to Python
Introduction to Pandas
Lesson 5Data Wrangling
Data Gathering
Lesson 6Data Assessing and Cleaning
Univariate Visualisation
Lesson 7Bivariate Visualization
Multivariate Visualization
Slide deck


Lesson 8Descriptive Statistics
Lesson 9Mean Median Mode
Skewed Distribution
Range, Quartiles and Interquartile Range
Variance and Standard Deviation
Lesson 10Outliers
Normal Distribution
Lesson 11Probability Distributions
Lesson 12Sampling Distributions
Lesson 13Sampling Distributions Example
Lesson 14Estimation
Lesson 15Hypothesis Testing
Lesson 16t-Table
Lesson 17Chi-Square Statistic
Lesson 18ANOVA
Lesson 19Simple and Multiple Regression
Lesson 20Clustering


Students’ Projects


  • Examination: 30%
  • Coursework: 70%
    • Two Lab Tests (20%)
    • Six Class Tests (30%)
    • 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
  • Tuesday 7:50-9:40 and Saturday 2:00-4:00