BUSANA 7005 - Chatbots and Text Analytics for Business

North Terrace Campus - Semester 1 - 2025

In today's data-driven business landscape, mastering the art of leveraging advanced technologies is essential for achieving and sustaining success. The "Chatbots and Text Analytics for Businesses" course is designed to equip students with the skills and knowledge required to harness the power of chatbots, natural language processing (NLP), and text analytics for strategic decision-making, enhanced customer engagement, and overall business growth. The course offers a comprehensive exploration of key concepts and practical applications, bridging theory with hands-on experience. From crafting engaging conversational flows to integrating AI-powered responses, students will learn to design, develop, and deploy chatbots as they gain the expertise needed to create intelligent and user-friendly chatbots that cater to various business needs. Students will uncover how machines understand and interpret human language as they explore the foundations of linguistic analysis, sentiment analysis, and text classification, and discover how these techniques contribute to extracting valuable insights from textual data. Students will be introduced to prompt engineering techniques, equipping them with the skills to formulate effective queries and prompts that yield meaningful results from large language AI models such as ChatGPT. The course extends its focus to Python programming for analytics, empowering students to apply NLP algorithms and create solutions for drawing meaningful insights from text to make better informed business decisions. No prior experience in Python, chatbot development or NLP is required.

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Course Details

Course Code	BUSANA 7005
Course	Chatbots and Text Analytics for Business
Coordinating Unit	Adelaide Business School
Term	Semester 1
Level	Postgraduate Coursework
Location/s	North Terrace Campus
Units	3
Available for Study Abroad and Exchange	Y

Course Staff

No information currently available.

Course Timetable

The full timetable of all activities for this course can be accessed from .

Course Learning Outcomes

On successful completion of this course students will be able to:

Understand the use of chatbots and text analytics in real-life business applications.
Demonstrate basic proficiency in Python programming, including data manipulation, text analytics and API integrations.
Implement functional chatbots and understand the principles of developing scripted (rule-based) and generative (AI-based) chatbots.
Analyse challenges and devise innovative solutions that combine Python programming, chatbot development and text analytics within the business environment.

最新糖心Vlog Graduate Attributes

This course will provide students with an opportunity to develop the Graduate Attribute(s) specified below:

最新糖心Vlog Graduate Attribute	Course Learning Outcome(s)
Attribute 1: Deep discipline knowledge and intellectual breadth Graduates have comprehensive knowledge and understanding of their subject area, the ability to engage with different traditions of thought, and the ability to apply their knowledge in practice including in multi-disciplinary or multi-professional contexts.	1, 2, 3, 4
Attribute 2: Creative and critical thinking, and problem solving Graduates are effective problems-solvers, able to apply critical, creative and evidence-based thinking to conceive innovative responses to future challenges.	3, 4
Attribute 3: Teamwork and communication skills Graduates convey ideas and information effectively to a range of audiences for a variety of purposes and contribute in a positive and collaborative manner to achieving common goals.	4
Attribute 4: Professionalism and leadership readiness Graduates engage in professional behaviour and have the potential to be entrepreneurial and take leadership roles in their chosen occupations or careers and communities.	4
Attribute 7: Digital capabilities Graduates are well prepared for living, learning and working in a digital society.	1, 2, 3, 4

Learning Resources
Required Resources

There is no mandatory texbook for this course as the required resources will vary by teaching period to stay in touch with the latest development in the NLP/chatbot/prompt engineering space.

Recommended Resources
1. Online Textbooks and other Learning Material:
  
  "Natural Language Processing with Python" by Steven Bird, Ewan Klein, and Edward Loper: Offers insights into NLP concepts and techniques using Python.
  
  "Python for Data Analysis" by Wes McKinney: A comprehensive guide to utilizing Python for data manipulation, analysis, and visualization.
  
  “Brex's Prompt Engineering Guide” ()
  
  "The Prompt Engineering Cookbook" ()
  
  “Lil'Log Prompt Engineering” ()
Online Learning
1. Online Platforms:
  
  IBM Watson: A user-friendly platform for designing, building, and deploying intelligent chatbots that engage users in natural language conversations.
  
  Google Colab: A user-friendly free cloud-based interactive development environment (IDE) for Python programming and experimentation.
  
  GitHub: Collaborative version control platform for sharing code, assignments, and projects.
  
  OpenAI Playground: An interactive interface for crafting and testing prompts for AI models.
2. Databases and Datasets:
  
  Kaggle Datasets: A platform offering various datasets for NLP and AI projects.
  
  OpenAI GPT-3.5/4 API: For accessing and experimenting with NLP models.
3. Programming Tools:
  
  Python 3.x: The core programming language for the course, enabling students to develop AI applications.
  
  Anaconda Distribution: Bundled with essential Python libraries for data analysis, providing a convenient environment for development.
Learning & Teaching Activities

Learning & Teaching Modes

No information currently available.

Workload

No information currently available.

Learning Activities Summary

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The 最新糖心Vlog's policy on Assessment for Coursework Programs is based on the following four principles:

Assessment must encourage and reinforce learning.
Assessment must enable robust and fair judgements about student performance.
Assessment practices must be fair and equitable to students and give them the opportunity to demonstrate what they have learned.
Assessment must maintain academic standards.

Assessment Summary

No information currently available.

Assessment Detail

No information currently available.

Submission

No information currently available.

Course Grading

Grades for your performance in this course will be awarded in accordance with the following scheme:

M10 (Coursework Mark Scheme)
Grade	Mark	Description
FNS		Fail No Submission
F	1-49	Fail
P	50-64	Pass
C	65-74	Credit
D	75-84	Distinction
HD	85-100	High Distinction
CN		Continuing
NFE		No Formal Examination
RP		Result Pending

Further details of the grades/results can be obtained from Examinations.

Grade Descriptors are available which provide a general guide to the standard of work that is expected at each grade level. More information at Assessment for Coursework Programs.

Final results for this course will be made available through .

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SELTs are an important source of information to inform individual teaching practice, decisions about teaching duties, and course and program curriculum design. They enable the 最新糖心Vlog to assess how effectively its learning environments and teaching practices facilitate student engagement and learning outcomes. Under the current SELT Policy (http://www.adelaide.edu.au/policies/101/) course SELTs are mandated and must be conducted at the conclusion of each term/semester/trimester for every course offering. Feedback on issues raised through course SELT surveys is made available to enrolled students through various resources (e.g. MyUni). In addition aggregated course SELT data is available.
Student Support
Policies & Guidelines
This section contains links to relevant assessment-related policies and guidelines - all university policies.
Fraud Awareness

Students are reminded that in order to maintain the academic integrity of all programs and courses, the university has a zero-tolerance approach to students offering money or significant value goods or services to any staff member who is involved in their teaching or assessment. Students offering lecturers or tutors or professional staff anything more than a small token of appreciation is totally unacceptable, in any circumstances. Staff members are obliged to report all such incidents to their supervisor/manager, who will refer them for action under the university's student鈥檚 disciplinary procedures.

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Authorised by:	Deputy Vice-Chancellor and Vice-President (Academic)
Maintained by:	Course Outlines Administrator

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