BUSI2001 Emerging Technologies, Data and Business Analytics

How BUSI2001 is assessed

The three weights are published on the University's 2026 course page and total 100%. Canvas lists the matching tasks and due dates; course announcements add that extensions will not be granted for the two later assignments. Confirm submission rules and any pass requirements on Canvas.

Current BUSI2001 dates

Dates are as published in the Semester 2 2026 Canvas assessment list. Confirm exact deadlines and submission settings in the live LMS.

What BUSI2001 covers

Ten chapters move from data methods in Excel, Python and Tableau to AI, emerging technology, ethics and security.

The first module builds foundations in data analytics: the four types of analytics, data wrangling in Excel with logical functions and PivotTables, Python on Google Colab for extracting, transforming and loading data, encoding categories, and reading correlation and regression, followed by visualisation and dashboard development in Tableau.

The second module turns to the Fourth Industrial Revolution and the shift towards Industry 5.0, artificial intelligence and human-AI collaboration, and predictive analytics with machine learning models including random forests, Naive Bayes classifiers and logistic regression.

The third module surveys other emerging technologies, including the Internet of Things, edge computing, quantum computing, extended reality, blockchain and polyfunctional robots, before closing with ethics in data analytics and cybersecurity risk assessment.

Generative AI is part of the toolkit rather than a forbidden shortcut.

Workshops have students prompt AI for Excel formulas and Python code, then run, check and interpret the output. That shifts the hard part of the course from typing code to judgement: knowing whether a cleaned column is really clean, whether an average should be a rate, whether a significant p-value matters, and whether an AI-only insight confuses a share with a rate. The later topics add critical lenses.

Students compare the World Economic Forum's inevitability narrative of technological change with critical social science readings, apply the PAPA framework of privacy, accuracy, property and accessibility to cases, and classify security controls as technical, administrative or physical.

This guide follows the Semester 2 2026 topic sequence and the published assessment weights.

Every chapter contains original worked examples with numbers recomputed step by step, computed figures, and answered practice items modelled on the three assessed tasks: analysing a business dataset, reporting on an emerging technology as a team, and reflecting on learning week by week.

Worked example · free

Reading a churn model's confusion matrix

Key terms

Descriptive analytics

Analysis that summarises what has already happened in a dataset, such as totals, averages or counts by group.

Prescriptive analytics

Analysis that recommends an action and weighs its likely consequences for a decision.

Data wrangling

The cleaning, structuring and transforming of raw data so that analysis tools can summarise it correctly.

PivotTable

An Excel tool that groups rows by chosen fields and summarises a value for each group.

ETL process

Extract, transform, load: reading data from a source, changing it deliberately, and writing the result to a new store.

One-hot encoding

Turning a nominal category into separate 0 or 1 columns, one for each level, so no false order is implied.

Calculated field

A Tableau formula that creates a new measure, such as a rate, which recomputes for every slice of a view.

Cognitive offloading

Relying on AI so heavily that one's own critical thinking and checking weaken over time.

Supervised learning

Machine learning that trains on labelled past cases to predict the label of new cases.

Recall

The share of actual positive cases, such as churners, that a classification model correctly identifies.

Edge computing

Processing data close to where it is generated to reduce latency for real-time decisions.

PAPA framework

An ethics framework that examines data practices through privacy, accuracy, property and accessibility.

Multi-factor authentication

Access control that requires evidence from two or more different categories, such as a password and a phone.

BUSI2001 FAQ

Questions students ask in each BUSI2001 chapter

Why delete whole rows instead of single cells?

In the workshop data, a row with a missing or unusable value in any feature cannot support coherent analysis across all features, so the exercise filters each column for blanks, text codes and placeholders and removes the entire row. On the practice sheet this removes 130 rows. Record how many rows you drop, because removal changes the population you describe. Chapter 01 · Data Analytics and Data Wrangling

Why does int(0.75) return zero?

Converting a float to an integer in Python drops the decimal part rather than rounding, so 0.75 becomes 0 and 2.9 becomes 2. This matters when rates are stored as fractions. Use round() when rounding is intended, and check the type of any suspicious value before it reaches a calculation. Chapter 02 · Python Fundamentals for Data Analysis

Why does Tableau show sums by default?

Tableau aggregates measures, and SUM is the default because totals suit many sales questions. For ages, scores or ratings a sum is meaningless, so change the aggregation to average or count. When groups differ in size, compute a rate with a calculated field rather than comparing totals that simply reflect group size. Chapter 03 · Tableau Essentials for Data Visualisation

What makes a dashboard tell a story?

A story dashboard starts from one headline finding and arranges only the sheets that support or qualify it. The lecture advises building around a core idea and refining iteratively, making everything readable for non-analytical audiences. Titles that state findings, consistent definitions and few controls keep the reader on the message. Chapter 04 · Tableau Dashboard Development

What is the difference between the Schwab and Barley perspectives?

The narrative associated with the World Economic Forum and Klaus Schwab frames the Fourth Industrial Revolution as an inevitable, beneficial evolution to prepare for. Barley's critical social science approach examines its broader socio-economic effects and asks who benefits and who carries the costs. A good analysis presents both before judging. Chapter 05 · Fourth Industrial Revolution and Industry 5.0

What is cognitive offloading and why is it risky?

Cognitive offloading is leaning on AI so heavily that your own critical thinking weakens. It is risky because it is gradual and comfortable: each unchecked answer makes the next check feel less necessary. Controls include reconciling AI-drafted numbers to source data and occasionally producing analysis unaided to keep skills sharp. Chapter 06 · AI and Human-AI Collaboration

Why train on one file and test on another?

Scoring a model on the same data it learned from flatters every algorithm, because it may simply have memorised those cases. The churn exercise therefore fits models on a training file and judges them on a separate testing file, which gives an honest estimate of how each will perform on customers it has never seen. Chapter 07 · Predictive Analytics and Machine Learning

When is edge computing better than central processing?

Edge computing suits decisions that cannot wait for data to travel to a distant server, such as shutting down a faulty machine in real time. Reports and analyses that combine data from many sites can run centrally. Most firms use a hybrid of the two and must secure both ends. Chapter 08 · Other Emerging Technologies

Why is Cambridge Analytica used as the privacy example?

The Facebook and Cambridge Analytica case shows personal data collected and shared at scale without explicit consent, then repurposed for political targeting. A recommended reading reports that Meta settled the case for 725 million dollars, illustrating that privacy failures carry legal and reputational costs as well as ethical ones. Chapter 09 · Ethics in Data Analytics

What are the five elements of the cybersecurity critical thinking model?

The model joins five things: how a person thinks, their technical skill, their interpersonal skill, their theoretical knowledge and their broader intellectual ability. Together they let professionals analyse threats, question assumptions and develop strategic defences. Technical knowledge alone is not enough, because many attacks succeed through people and processes rather than technology. Chapter 10 · Cybersecurity Risks and Assessment

How to prepare for the assessments

Treat the course as three linked skills rather than ten separate topics. First, secure the data methods from Module 1: practise cleaning a messy column in Excel, writing nested IF formulas with correct boundaries, building a PivotTable with averages rather than sums, and running the extract, transform and load steps in Colab.

Second, practise interpretation: for every chart, regression or model output, write one sentence stating the finding, one naming the denominator or comparison, and one stating what the result cannot show. Third, practise critical evaluation for Modules 2 and 3 by setting a claim about a technology beside a rival reading and closing with a defensible judgement.

Start the portfolio in the first week and write a short reflection after every workshop, because the two later assignments are built progressively and extensions are not granted. For the group report, agree a thesis and section owners early, and attribute every projection you cite. Before submitting, recompute every number in your work from its source.

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