COMM1190 Lectures P1

Week 1: Data, Analytics, and Organisation

Overview

The course COMM1190 at UNSW Sydney focuses on understanding data, analytics, and decision-making within an organizational context. This week, the introduction lays the foundation for the importance of data in driving insights and making informed decisions.

Course Objectives

  • Evaluate and Interpret Data: Students will learn how to assess data critically, ensuring reliability and recognizing biases.

  • Critical Thinking: Develop skills to analyze data critically, identifying trustworthiness and recognizing biases.

  • Mastering Analysis Techniques: Focus on techniques that afford meaningful insights from data.

  • Identifying Trends: Navigate through complex datasets to pinpoint significant trends influencing decision-making.

  • Transform Insights: Learn how to convert data insights into actionable strategic decisions.

  • Communication Skills: Enhance ability to communicate results through effective visualizations and reports.

Course Structure

Lectures

  • Analytics Mindset: Development of a mindset conducive to analytical thinking.

  • Business Analytics Cycle: Introduction to the cyclical nature of business analytics.

  • Analytical Techniques: Overview of various techniques used in data analysis.

  • Research Design: Focus on designing research using experimental methods.

  • Communication with Stakeholders: Importance of effectively communicating insights to stakeholders.

Tutorials

  • Application of Concepts: Hands-on activities applying theoretical constructs learned in lectures.

  • Introduction to R Programming: Basic R programming skills for data analysis.

  • Weekly Activities: Engage in analytical tasks to facilitate learning.

  • Interactive Learning: Promote questions during tutorials to deepen understanding.

Weekly Activities

  • Self-study and Assessments: Students are expected to manage their self-assessment alongside course activities which include live lectures and tutorial sessions.

  • Pre-tutorial Preparation: Engage in pre-tutorial activities for optimal benefit from tutorials.

Assessment Overview

  1. Individual Report Assignment (20%): Focus on descriptive analytics using R, written report required.

  2. Team Report Assignment (30%): Involves both descriptive and predictive analytics, requiring collaboration in written report format.

  3. Final Exam (50%): Conducted as a 3-hour online exam, testing comprehensive understanding of course material.

Data Analytics Fundamentals

Types of Analytics

  • Descriptive Analytics: Historical data analysis to answer "What happened?".

  • Predictive Analytics: Focus on forecasting future trends based on historical data.

  • Prescriptive Analytics: Recommendations for decision-making based on analytical findings.

Understanding Business Context

To successfully implement analytics, understanding the business context including shareholder expectations, technological and legal pressures, and competition is paramount.

Skills Required for Business Analytics Implementation

  • Data Analysis: Proficiency in data analysis tools (Excel, R, SQL).

  • Visualization Techniques: Use tools like Tableau and Power BI for effective data visualization.

  • Problem-Solving: The ability to derive actionable insights to solve business problems.

  • Business Acumen: Understanding of industry dynamics and performance indicators.

  • Communication: Ability to convey complex analytics results in an understandable manner.

  • Machine Learning Knowledge: Familiarity with machine learning algorithms supports enhanced analysis.

Data Types and Quality

  • Data Quality: Ensure completeness, accuracy, and consistency are upheld in data collection.

  • Data Issues: Address issues such as missing values, outliers, and ensure ethical considerations concerning data privacy and manipulation are observed.

Module Wrap-Up

By the end of this week, students will have garnered essential grounding in the dynamics of data analytics within a business realm, coupled with a structured understanding of the practices that lead to effective decision-making.