# 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.

## Contents

- 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

# Lectures

## Tableau

Lesson 1 | Connecting to Data Visualization - Introduction |
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Lesson 2 | Filtering and Sorting Aggregation |

Lesson 3 | Calculated Fields Symbol Map Trend Lines Forecasting Dashboard and Story Project Discussion |

## Statistics

Lesson 4 | Descriptive Statistics Mean Median Mode Skewed Distribution Range, Quartiles and Interquartile Range Variance and Standard Deviation |
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Lesson 5 | Introduction to R |

Lesson 6 | Introduction to R cont.. loading data Statistical Learning |

Lesson 7 | Statistical Learning cont supervised learning |

Lesson 8 | Simple Linear Regression Multiple Linear Regression |

Lesson 9 | Multiple Linear Regression cont Deciding on Important Variables |

Lesson 10 | Linear Regression Lab |

Lesson 11 | Other Considerations in Regression Model |

Lesson 12 | Linear Regression Lab Contd Interaction Terms ggplot2 |

Lesson 13 | R Commands Classification |

Lesson 14 | Logistic Regression Logistic Regression Lab |

Lesson 15 | Logistic Regression Lab contd Resampling |

Lesson 16 | Resampling lab dplyr |

Lesson 17 | dplyr contd additional aesthetics |

Lesson 18 | dplyr contd line graph |

Lesson 19 | Plotly |

Lesson 20 | Plotly cont customizing graphics |

Lesson 21 | Plotly cont background |

Lesson 22 | Shiny |

Lesson 23 | Visualization Best Practices |

Lesson 24 | Visualization Best Practices Contd Geospatial |

## Datasets

## Assessment

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

## Recommended Books/Resources

- Tableau
- Statistics

## Academic Calendar

Jan 4 - Jan 9 | Fresherâ€™s Week |
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Jan 10 - Feb 27 | Classes |

Feb 28 - Mar 6 | Mid Semester Break |

Mar 7 - Apr 24 | Classes |

Apr 25 - May 8 | Revision Week |

May 9 - May 22 | Examination |

- Monday 9:50-11:40 and Tuesday 9:50-11:40