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Ross Tinsley
Week 1: The importance of data to your business Week 2: Using data to make your business more robust Week 3: How to improve business productivity through data Week 4: Tools to help you make the most of your data Week 5: Data beyond your own business - data collaboration Most FutureLearn courses run multiple times. Every run of a course has a set start date but you can join it and work through it after it starts. Find out more This course is designed for small and medium enterprises in the tourism industry, such as tour companies, transport providers, and B&Bs.
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Reviews summary
Practical data fundamentals for tourism smes
According to students, this course offers a solid and practical introduction to understanding data for small and medium enterprises in the tourism industry. It is widely appreciated for its accessibility and beginner-friendly approach, breaking down complex concepts into digestible pieces without overwhelming technical jargon. Many learners found the focus on practical applications for their B&Bs, tour companies, and transport businesses to be particularly useful and actionable, with key modules like improving productivity and data tools frequently highlighted. However, some students with prior data literacy mentioned the course felt too superficial or basic, desiring more in-depth 'how-to' guidance or specific industry case studies.
Specific sections on data tools and productivity highly regarded.
"I particularly found the module on 'Tools to help you make the most of your data' very useful."
"I appreciated the emphasis on improving productivity."
"The week on productivity gains was my favourite."
"Week 5 on data collaboration was interesting."
Simplifies data concepts for non-technical learners.
"The instructors explained complex concepts simply. Highly recommend for any tourism business owner looking to leverage data without getting bogged down in technical jargon."
"It wasn't overly technical, which was a plus for me. The content was accessible, and I didn't feel lost even with no prior data background."
"This course is well-designed for its target audience... It simplifies complex ideas and focuses on practical outcomes."
"It helps demystify data for those in the tourism sector."
Directly applicable insights for tourism businesses.
"This course was incredibly insightful for my B&B business. The focus on practical applications for SMEs was exactly what I needed."
"As a small tour operator, I often feel overwhelmed by data. This course broke it down into digestible pieces... examples were relevant to tourism."
"The practical advice on improving business robustness was actionable. This course is a game-changer for small tourism businesses."
"I learned how to identify key data points and use them to attract more guests for my B&B."
May be too basic for those with prior data knowledge.
"Some parts felt a bit basic if I already have some data literacy, but for a true beginner, it's perfect."
"I found this course too superficial. It felt like a long advertisement for the concept of data, rather than teaching concrete skills."
"I was hoping for more advanced techniques or specific industry case studies. It felt a bit too general at times."
"I expected more depth and practical tools. Not worth the time if I have any prior experience or critical thinking skills."
"The 'tools' section was a good overview but didn't go deep enough for me to start using them immediately without further research."
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Understanding Data in the Tourism Industry.
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Learners who complete Understanding Data in the Tourism Industry will develop knowledge and skills
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Reading list
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Provides a comprehensive overview of machine learning, covering topics such as supervised learning, unsupervised learning, and reinforcement learning. Written by leading experts in the field, it valuable resource for students and practitioners who want to gain a deep understanding of machine learning.
Provides a foundational understanding of the fundamental principles of data science and the data-analytic thinking necessary for extracting value from data in a business context. It is highly relevant for undergraduate business analytics programs and working professionals. It serves as a useful reference for understanding the business applications of data analysis and is commonly used as a textbook.
Provides a comprehensive overview of big data analytics, covering topics such as data management, data mining, and data visualization. It valuable resource for students and practitioners who want to gain a better understanding of big data analytics.
Focuses on the crucial skill of communicating insights from data effectively through compelling visualizations. is highly relevant for all levels, emphasizing the importance of clear and impactful data presentation. It is valuable additional reading that complements technical data analysis skills.
Provides a guide to creating effective and aesthetically pleasing data visualizations. It delves into the principles behind good visualization design, helping readers make informed choices about how to represent their data. It valuable reference for anyone creating visualizations, from students to professionals.
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An excellent overview of Bayesian statistics, this book provides a comprehensive introduction to the theory and practice of Bayesian data analysis. The focus on practical applications and real-life examples makes it a great choice for students and practitioners alike.
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A classic text in the field of data mining, this book provides a comprehensive overview of techniques and algorithms used for extracting knowledge from large datasets. Written by leading experts in the field, it valuable resource for students and researchers.
A hands-on guide to data analysis using Python, this book covers a wide range of topics, including data cleaning, transformation, visualization, and modeling. Written by the creator of Pandas, it practical resource for students and professionals in various fields.
A widely-used textbook for undergraduate and graduate-level statistics and data science courses. It provides a comprehensive overview of statistical learning methods with practical applications in R. While it can be challenging, it solidifies understanding of key modeling and prediction techniques. This core textbook for those seeking a deeper understanding.
This online book provides a comprehensive overview of machine learning concepts and techniques. Written by a leading expert in the field, it valuable resource for students and practitioners who want to gain a deep understanding of machine learning.
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A comprehensive introduction to data analysis using R, this book covers a wide range of topics, including data manipulation, visualization, and statistical modeling. Written by leading experts in the field, it valuable resource for students and practitioners.
Provides a comprehensive overview of statistical methods for data analysis, covering topics such as probability distributions, hypothesis testing, and regression analysis. Written by a leading expert in the field, it valuable resource for students and practitioners in various fields.
This comprehensive handbook provides a wide range of topics in data science, including data mining, machine learning, and data visualization. Written by experts in the field, it valuable resource for students and practitioners who want to gain a broad understanding of data science.
Is an excellent starting point for anyone new to data analysis or statistics. It demystifies core statistical concepts without relying heavily on mathematical formulas, making it highly accessible for high school and undergraduate students. It provides a strong foundation in the intuition behind statistical analysis and helps readers understand how data can be used and misused. This is valuable background reading that builds prerequisite knowledge.
A timeless classic that remains highly relevant today. exposes common ways statistics can be manipulated or misinterpreted, fostering a critical eye essential for anyone working with data. It's valuable for all levels, from high school to professional, as it highlights the importance of data integrity and ethical considerations. This serves as crucial additional reading to develop data literacy.
Explores the world of prediction and forecasting across various fields, demonstrating how data analysis and statistical modeling are used in practice. It's particularly engaging for undergraduate and graduate students interested in the application of data analysis in real-world scenarios. It adds breadth by showcasing diverse applications and the challenges involved in making accurate predictions.
Written by the creator of the pandas library, this practical, hands-on guide to manipulating, processing, cleaning, and crunching data in Python. It is essential for anyone using Python for data analysis, from undergraduates to professionals. It serves as an invaluable reference tool and is commonly used as a textbook or supplementary material in data analysis courses focusing on Python.
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Provides a comprehensive introduction to data analysis using R and the tidyverse package collection. It's highly recommended for students and professionals using R, offering a structured approach to data manipulation, visualization, and modeling. It functions well as a textbook and a practical reference.
Builds data science tools and algorithms from the ground up using Python, providing a deeper understanding of the underlying mechanics. It's suitable for those with some programming experience and a desire to understand how data analysis techniques work internally. It helps solidify understanding by revealing the foundational code.
A classic text in the field of statistical learning, this book covers a wide range of topics, including linear and nonlinear regression, classification, unsupervised learning, and model selection. It comprehensive resource for students and practitioners in various fields.
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