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Data Science: How to Plan Projects, Research and Reflect · LearnSpace
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Data Science: How to Plan Projects, Research and Reflect

Курс от University of Leeds
Средний≈ 7.7 чАнглийский
О курсеНавыкиПрограммаПреподаватели

О курсе

This course guides you through the key steps involved in planning and delivering a successful data science project. You will learn how to define your project aims, build a clear project plan, and select methods that support strong, evidence-based work. The course also introduces core academic skills, including how to research a topic, complete a literature review, and evaluate information critically. Reflection is a central part of the process, and you will learn how to reflect on your decisions and communicate your findings with clarity and confidence. Through practical activities and real examples, you will build the skills needed to approach data science projects in a structured and thoughtful way. By the end of the course, you will be ready to plan, research and reflect effectively on your own data science work.

Навыки, которые вы освоите

Analytical SkillsTechnical CommunicationData ScienceRecord KeepingCritical ThinkingGeneral Science and ResearchPersonal Development

Программа курса

2 модулей · 39 учебных материалов

01Week 1: Reflective writing and project planning23 материалов

Welcome to Reflective writing and project planning for data science

About this courseЧтениеHow to study this courseЧтениеShort course authorЧтение

Lesson 1: A simplified approach to reflective practice

1.1 Overview Чтение

Учитесь у экспертов

Jennifer Sexton

Преподаватель курса

Data Science: How to Plan Projects, Research and Reflect
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Обучение на Coursera

≈ 7.7 ч

2 модулей

Язык: Английский

Часть программы вашего университета
1.2 What is reflective writing and why am I being asked to do it? Чтение
1.3 Why should data scientists write reflectively? Чтение
1.4 A simplified model for reflective writing Чтение
1.5 Shifting from reflective thinking to reflective writing Чтение
1.6 Next steps Чтение

Lesson 2: Adapting and applying the Gibbs' model

2.1 A model for reflective writing – an introduction to Gibbs' modelЧтение2.2 The reflective templateЧтение2.3 When should I reflect?Чтение2.4 Barriers to writing reflectivelyЧтение2.5 Reflective activity: Examples of student reflectionsЧтение2.6 Reflective activity: Tutor feedback on student reflectionsЧтение

Lesson 3: The data science research process and using CRISP-DM

3.1 Introducing CRISP-DMЧтение3.2 Large projects with diverse teamsЧтение3.3 Day-to-day tasksЧтение3.4 How do data scientists collaborate?Чтение3.5 How can I use reflective writing to guide my research?Чтение3.6 Reflection for different purposes within the research project contextЧтение3.7 Summary MCQ on CRISP-DMЗаданиеContinuing your studies in Data ScienceЧтение
02Week 2: Literature searches, critiquing papers and academic reading16 материалов

Lesson 4: Literature searching and record keeping

4.1 Overview of Week 2Чтение4.2 Deciding where to searchЧтение4.3 Search toolsВидео4.4 Developing a search strategyЧтение4.5 Next steps Чтение

Lesson 5: Academic reading

5.1 Reading with a question in mind: SQ3RЧтение5.2 Matching task: Using questions to guide a literature searchЧтение5.3 Reading to answer specific questionsЧтение5.4 How should I take notes?Чтение5.5 Software for note takingЧтение5.6 What makes a good research question?Чтение5.7 Next stepsЧтение5.8 Annotated course bibliographyЧтениеContinuing your studies in Data ScienceЧтение

Lesson 6: Final activity

6.1 Activity brief: Reflective assignmentЧтениеCourse SummaryЧтение