Introduction to Data Analytics (ZD-1201)
Bachelor of Digital Science, Universiti Brunei Darussalam, Digital Science, 2021
Students will learn the latest technologies including Tableau and Python in the field of data analytics.
Contents
- 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
Lectures
- Data Visualization using Tableau
- Introduction to Programming environment for data analytics
- Statistics
Lab/Datasets
Students’ Projects
Assessment
- Examination: 30%
- Coursework: 70%
- Two Lab Tests (20%)
- Two Class Tests (20%)
- Four Class Quizzes (10%)
- Project (20%)