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Full Description
Data Analysis for Business Students: A Mind-Mapping Approach is an innovative educational resource that guides business students in mastering the essential concepts and methodologies of data analysis. This textbook's unique instructional design incorporates mind maps, a proven cognitive tool to streamline the learning experience, making complex data analysis concepts more digestible. Organised into five parts, the textbook covers an introduction to data analysis, data preparation and exploratory analysis, basic data analytics techniques, advanced data analytics techniques, and data analysis in a digital world. By integrating mind maps with practical applications and case studies, the textbook equips students with the analytical skills required to succeed in the data-rich business world.
This unique pedagogical approach offers three key advantages:
Simplifying complex information: Mind maps systematically organise information, making it easier for students to understand complex data analysis concepts.
Improving memory retention: By linking pieces of information together, mind maps activate long-term memory and support better memory retention.
Increasing student engagement: Mind maps can help students prepare for class activities, enabling a deeper understanding of the subject and allowing instructors to focus more on problem-solving skills and critical thinking.
With extensive online resources, including PowerPoint slides, an instructor's manual, a quiz bank, tutorial questions, instructional videos on creating mind maps, and Python code for performing data analysis that students can access, use, and experiment with, Data Analysis for Business Students: A Mind-Mapping Approach offers a structured and accessible approach for advanced undergraduate and postgraduate business students.
Contents
Part I: Introduction to Data Analysis; Chapter 1: Introducing Data Analysis for Business Students; Chapter 2: Basic Mathematics for Data Analysis; Part II: Data Preparation and Exploratory Analysis; Chapter 3: Data Sources, Types, and Quality; Chapter 4: Descriptive Statistics and Data Visualisation; Part III: Basic Data Analytics Techniques; Chapter 5: Hypothesis Testing; Chapter 6: Correlation; Chapter 7: Regression Analysis; Part IV: Advanced Data Analytics Techniques; Chapter 8: Factor Analysis; Chapter 9: Cluster Analysis; Chapter 10: Structural Equation Modelling; Part V: Data Analysis in a Digital World; Chapter 11: Advanced Techniques in Big Data and Machine Learning; Chapter 12: Ethical Considerations in Data Analysis; Chapter 13: Data Analysis Tools, Software, and Cloud Platforms; Chapter 14: Data Analysis Applications in Business Domains
