Data Analysis 2: Introduction to Python

This is a free course of approximately 1.5 hours for citizen science practitioners and researchers, that provides you with a basic introduction to citizen science and requires no prior knowledge of the subject. The course will help you to: define citizen science, understand the history and significance of citizen science and how it has evolved into the modern-day practice we see today, understand the different typologies that have been created to explain the varying levels of public participation in citizen science. By the end of this course you will better understand the key characteristics of citizen science. At the end of each section there is a short quiz or activity, and a ten question quiz at the end of the course. If you score 50% or more in the final quiz then you will earn a course completion badge. Click here to enrol in this course and use enrolment key INTROTOCITSCI to do so. Once you enrol you will be able to take quizzes and receive a badge upon successful course completion.

This is a free course of approximately 1.5 hours for citizen science practitioners and researchers, that provides you with a basic introduction to citizen science and requires no prior knowledge of the subject.The course will help you to:define citizen science,understand the history and significance of citizen science and how it has evolved into the modern-day practice we see today,understand the different typologies that have been created to explain the varying levels of public participation in citizen science. By the end of this course you will better understand the key characteristics of citizen science.At the end of each section there is a short quiz or activity, and a ten question quiz at the end of the course. If you score 50% or more in the final quiz then you will earn a course completion badge.Click here to enrol in this course and use enrolment key INTROTOCITSCI to do so. Once you enrol you will be able to take quizzes and receive a badge upon successful course completion.

Dies ist eine speziell für die Universität Mainz angepasste Version der "Introduction to Citizien Science".

🔴 Course description:  Welcome to the “Introduction to Pandas” course. This free course, designed for beginners, takes approximately 2 hours to complete and provides a comprehensive introduction to Pandas, a powerful library for data manipulation and analysis in Python. While no prior knowledge of Pandas is required, it is recommended that you have completed the previous courses in the “Data Analysis and Visualization” learning path. This course will help you to: Series and DataFrames: Understand the basics of Pandas Series and DataFrames, how to create them, and see practical examples of their use in data analysis. Indexing, selecting, and assigning: Learn different methods of indexing, selecting, and assigning data in Pandas, with practical examples to illustrate these techniques. Basic functions: Explore essential functions for data manipulation and analysis, including handling missing data, applying operations, and summarizing data. Grouping: Learn how to group data using the groupby() function, and explore techniques for aggregating and transforming grouped data. Combining: Understand different methods for merging, joining, and concatenating data, and see practical examples of combining DataFrames and Series. Exercises: Test your knowledge and skills with practical exercises designed to reinforce your understanding and provide hands-on experience with Pandas. Final Test - Exercises in real environments: Apply everything you’ve learned in real-world scenarios, working with external databases and tackling complex data analysis challenges. By the end of this course, you will have a solid understanding of Pandas and its capabilities, preparing you for more advanced topics in data analysis and visualization. This course is part of the “Data Analysis and Visualization” learning path, which includes the following courses: Introduction to Jupyter Notebooks Introduction to Python Introduction to NumPy Introduction to Pandas Data visualization in Python These courses are designed to be taken in sequence to build a solid foundation in data analysis and visualization. 💡Access information While the ECS Academy aims to make all its courses accessible to guests (no registration required), the courses belonging to the Data Analysis and Visualization learning path require users to be registered. This is due to their specific technological setup linked to each individual account. Self-enrollment: To self- enroll first create your account on the citizenscience.eu platform - then on the ECS Academy (it is a two step process). Once you have created your account on the ECS Academy you will be able to enroll without a key.

🔴 Course description:  Welcome to the “Introduction to NumPy” course. This free course is designed for beginners, takes approximately 90 minutes to complete and provides a comprehensive introduction to NumPy, a powerful library for numerical computing in Python. While no prior knowledge of NumPy is required, it is recommended that you have completed the previous courses in the “Data Analysis and Visualization” learning path. This course will help you to: Introduction: Learn how to install and import NumPy, understand the basics of NumPy arrays, and explore the core concepts of array creation and basic operations. Dimensions and structure: Understand array shapes, work with multi-dimensional arrays, and learn techniques for indexing, slicing, and reshaping arrays. Transformations and conversions: Discover how to convert data types, perform array flattening, and combine arrays using stacking and concatenation. Attributes: Learn about array attributes such as shape, size, and data type, and understand how to use these attributes to gain insights into your data. Operations with arrays: Perform mathematical and statistical operations on NumPy arrays, including array arithmetic, broadcasting, and using common functions like sum, mean, and standard deviation. Exercises: Apply your knowledge through practical exercises designed to reinforce your understanding and provide hands-on experience with NumPy. By the end of this course, you will have a solid understanding of NumPy and its capabilities, preparing you for more advanced topics in data analysis and visualization. This course is part of the “Data Analysis and Visualization” learning path, which includes the following courses: Introduction to Jupyter Notebooks Introduction to Python Introduction to NumPy Introduction to Pandas Data visualization in Python These courses are designed to be taken in sequence to build a solid foundation in data analysis and visualization. 💡Access information While the ECS Academy aims to make all its courses accessible to guests (no registration required), the courses belonging to the Data Analysis and Visualization learning path require users to be registered. This is due to their specific technological setup linked to each individual account. Self-enrollment: To self- enroll first create your account on the citizenscience.eu platform - then on the ECS Academy (it is a two step process). Once you have created your account on the ECS Academy you will be able to enroll without a key.

🔴 Course description:  Welcome to the “Introduction to Jupyter Notebook” course. This free course, designed for beginners, takes approximately 45 mins to complete and provides a foundational understanding of Jupyter Notebook. No prior knowledge of the subject is required. This course will help you to: Understand what Jupyter Notebook is and its key features and benefits. Navigate the Jupyter Notebook interface with ease. Use notebook cells effectively, including writing and executing code and using Markdown for documentation. Perform basic operations, including running and debugging code and using keyboard shortcuts. Collaborate and share notebooks efficiently, leveraging best practices. Organise your notebooks and troubleshoot common issues.  By the end of this course, you will be well-prepared to interact with Jupyter Notebooks confidently, setting a strong foundation for more advanced data analysis and visualisation courses. This course is part of the “Data Analysis and Visualization” learning path, which includes the following courses: Introduction to Jupyter Notebooks Introduction to Python Introduction to NumPy Introduction to Pandas Data visualization in Python These courses are designed to be taken in sequence to build a solid foundation in data analysis and visualization. 💡Access information While the ECS Academy aims to make all its courses accessible to guests (no registration required), the courses belonging to the Data Analysis and Visualization learning path require users to be registered. This is due to their specific technological setup — including the use of Jupyter Notebooks for example, linked to each individual account. Self-enrollment: To self- enroll first create your account on the citizenscience.eu platform - then on the ECS Academy (it is a two step process). Once you have created your account on the ECS Academy you will be able to enroll without a key.

🔴 Course description:  Welcome to the “Data Visualization in Python” course. This free course is designed for beginners, takes approximately 3 hours to complete and provides a comprehensive introduction to creating visualizations using various Python libraries. While no prior knowledge of these libraries is required (Matplotlib, GGPlot, Seaborn and Bokeh), it is recommended that you have completed the previous courses in the “Data Analysis and Visualization” learning path. This course covers: Matplotlib: Learn the fundamentals of Matplotlib, including installation, basic plotting techniques, and customization options to create a wide range of static visualizations. GGPLOT: Explore GGPLOT, a library that allows you to create complex and aesthetically pleasing plots using its grammar of graphics, enhancing your ability to produce sophisticated visual representations. Seaborn: Understand Seaborn, a statistical data visualization library built on top of Matplotlib, to create informative and attractive statistical plots. Bokeh: Discover Bokeh, a library for creating interactive and web-ready plots, enabling you to develop interactive visualizations that can be easily shared online. Final Test: Apply everything you’ve learned in real-world scenarios, facing exercises that simulate complex data analysis tasks with access to external databases. By the end of this course, you will have a strong understanding of various data visualization libraries in Python and be well-prepared to create both static and interactive visualizations for your data analysis projects. This course is part of the “Data Analysis and Visualization” learning path, which includes the following courses: Introduction to Jupyter Notebooks Introduction to Python Introduction to NumPy Introduction to Pandas Data visualization in Python These courses are designed to be taken in sequence to build a solid foundation in data analysis and visualization. 💡Access information While the ECS Academy aims to make all its courses accessible to guests (no registration required), the courses belonging to the Data Analysis and Visualization learning path require users to be registered. This is due to their specific technological setup linked to each individual account. Self-enrollment: To self- enroll first create your account on the citizenscience.eu platform - then on the ECS Academy (it is a two step process). Once you have created your account on the ECS Academy you will be able to enroll without a key.

This course will help you understand the ethical and organizational challenges of designing and implementing participatory activities with different stakeholders and particularly citizens in an ethical and meaningful way. By the end of the course, you should have a good understanding of the questions and considerations to take into account when planning, implementing and evaluating such an activity. The contents of this course build on the learnings of the PRO-Ethics project, and especially its main output, the Ethics Framework and Guidelines: A guide for research funding organizations implementing participatory activities.

🔴 Course description:  This is a free course resource that was developed as a guide for designing your own course on the topics discussed. It includes resources such as detailed session/workshop outlines and PowerPoint slides. It could also be used as a course in itself, though was not specifically set up as such. The course resources are designed to facilitate the adoption of citizen science (CS) within Research Performing Organisations (RPOs) focusing on four key topic areas:  Research & Methodology Education & Awareness Support Infrastructure & Resources Policy & Assessment In addition to these four sections, a Train-the-Trainer section enables you to run your own citizen science-related training courses for institutional staff based on the four key topic areas in this course resource. The intended audiences for this course resource include training providers for RPOs, but also researchers at all career stages, research support staff and management, students and volunteer managers and facilitators could benefit from relevant modules.  Many others may find particular aspects or modules of this course resource interesting, and we encourage everyone to browse the content and engage with it. Intended audiences for modules within each area are specified on the Welcome and Introduction pages under each area. By the end of this course, the learner will:  Understand the fundamentals of CS, how to develop and expand research projects using CS methodologies, understand CS data management, and know how to engage and manage volunteers.  Understand the importance of CS training and awareness-raising, and develop practical skills in stakeholder analysis, designing training programs, engaging schools and communities, and crafting purposeful communication and engagement strategies tailored to diverse audiences. Understand the critical role of institutional support and infrastructures within RPOs, including the role of Institutional Contact Points (CS-ICP), the opportunities for CS funding, and the ethical considerations of CS projects. Understand the critical significance of developing strategic approaches and policies for CS within RPOs, investigate broader assessment models to effectively evaluate and reward CS contributions, and establish a connection between CS and Responsible Research and Innovation (RRI) principles and processes. Be able to conduct CS-related training courses for institutional staff based on the four key topic areas, taking into consideration stakeholders and audiences, training design, and challenges and opportunities for implementing and embedding the training. 💡Access information This course allows 1) guest access (no registration required) and 2) self-enrollment as a user.  Type of access Using guest access allows you to see course content freely but does not enable you to save your learning progress or earn a badge. Self-enrollment as a user allows you to see the course content, save your learning progress and earn a badge. How to access Guest access: enter the key ‘TIME4CS’ into option “Guest access” below. Self-enrollment: To self- enroll first create your account on the citizenscience.eu platform - then on the ECS Academy (it is a two step process). Once you have created your account on the ECS Academy you will be able to enroll without a key.

Muki Haklay

This course is reserved for network members of the ECS Academy network of educators and trainers. To know more about and to register to the network please visit the webpage dedicated to the ECS Academy network of educators and trainers of the following page: https://moodle.citizenscience.eu/local/edwiserpagebuilder/page.php?id=2

Lucy Robinson Lucy Robinson

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Welcome to this short training course, which we hope you will find inspiring, energising and informative. The course is specifically aimed at museum staff, or those working closely with museums and similar institutions like science centres. It has been developed by citizen science practitioners at museums in the UK and Germany, with support from colleagues across Europe and beyond. Over the course of two hours, we will share examples and case studies of citizen science projects at museums, highlight museum-specific considerations, challenges and opportunities when developing a project, and give you tools and strategies to develop your own citizen science research project. The six sections introduce you to:A brief history of citizen science in museumsResearch opportunities through a citizen science approachEnhancing outcomes for citizen science participantsReal-world impact: Societal, conservation and policy outcomes of citizen scienceEthical considerationsOur goal is for you to:Reflect upon the long tradition of museums as an interface between research and society, professional societies and individual lay researchers, as well as in the form of (joint and open) data collection and archiving. Understand the benefits of a citizen science approach, for yourself and your organisation, for academic research, for the people who take part, and for the environment. Be able to capitalise upon and appreciate the opportunities and challenges at Museums as host organisations for citizen scienceBe aware of ethical considerations, and strategies to work towards equitable and inclusive citizen science.Understand the tools and resources available to support you as you develop or participate in a citizen science scheme.