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This course will introduce students to the basics of data visualization, with a focus on creating reports and charts using the matplotlib library. The course will begin with a design and information literacy perspective, covering what makes a good and bad visualization, as well as how statistics are reflected in visualizations. The second week will focus on the technology used to create visualizations in python, matplotlib, and will introduce users to best practices for creating basic charts and how to implement design decisions within this framework. The third week will be a tutorial on the functionality available in matplotlib, demonstrating a variety of basic statistical charts, helping students identify when each method is good for a particular problem. The course will conclude with a discussion of other forms of data structuring and visualization. This course should be taken after Introduction to Data Science with Python and before the other Applied Data Science with Python courses: Applied Machine Learning with Python, Applied Text Mining with Python, and Applied Social Media Analysis with Python.

Preparing for graduate school in the United States can be nerve-wracking. Many international students have questions about what these programs are and what resources they can use to succeed. This course will help prospective and incoming international students practice their communication skills, identify tools that can help them succeed academically, and gain confidence as they prepare for advanced degrees. In addition to improving your English language skills, you will have the opportunity to hear from international students who have completed graduate studies in the United States about how they adapted to campus life, utilized resources, and built a community on their new campus. Throughout the course, students from around the world can connect with other students to explore course topics in depth, discuss perspectives, and network.

This course introduces students to applied machine learning, focusing more on the techniques and methods than on the statistics behind the methods. The course begins with a discussion of how machine learning differs from descriptive statistics and an introduction to the scikit learn toolkit via a tutorial. The issue of data dimensionality is addressed, the problem of clustering data is solved, and how to estimate those clusters is discussed. Supervised approaches to building predictive models are described, and students will be able to apply scikit learn's predictive modeling techniques while understanding the issues associated with generalizability of the data (e.g., cross-validation, overfitting). The course concludes with a discussion of more advanced techniques such as ensemble building and the practical limitations of predictive models. By the end of the course, students will be able to identify the difference between supervised (classification) and unsupervised (clustering) analysis techniques, determine which technique to apply for a particular dataset and need, design features to meet those needs, and write python code to perform the analysis. This course should be taken after Introduction to Data Science with Python and Applied Plotting, Graphing, and Presenting Data with Python, and before Applied Text Analysis with Python and Applied Social Analytics with Python.

Are you concerned about climate change? Would you like to learn how to address and respond to this issue? If so, this course is for you. Act on Climate: Steps to Individual, Community, and Political Action is designed to help students understand, address, and respond to climate change as individuals and in partnership with their communities and political leaders. The course focuses on how to translate learning into practical action on climate change in areas such as food, energy, transportation, and the built environment (cities). This course was co-designed and taught by Michaela Zint, professor of environmental education and communication, and students at the University of Michigan. The course features a variety of academic climate change experts and professional leaders. Upon completion of this course, you will be able to: 1) Identify individual, community, and political actions you can take to effectively address and respond to climate change. 2) Describe how insights from the social sciences can be used to create change at the individual, community, and political levels. 3) Feel empowered to further influence how you, your community, and political leaders address and respond to climate change. Use #UMichActonClimate on social media to share your actions and connect with other students.

This course is open to professionals who want to learn more about higher education leadership for a changing demographic or who are interested in developing their own leadership skills. The idea that people can be taught to lead is not without skepticism. Reasonable people, even some academics in the field, point to important traits, skills, and qualities that are seen in many notable leaders and argue that certain qualities needed for effective leadership are not easily transferred. To accept this premise and conclude that some people are born leaders and that others should not be expected to lead at all is to fall into the wrong end of the logical spectrum. We believe that leadership can be taught if it is nurtured, that most people have a range of strengths on which to build their own philosophy and approach to leadership, and that leadership development programs, if done well, can be transformative for organizers and participants. Course Objectives: Understand the historical narrative of institutions and the institutional structures that have created instances of inclusion and exclusion in colleges and universities. Describe how transformational leadership is applied to diversity, equity, and inclusion in a complex and contentious environment. Compare legislative, governance, and public accountability and expectations in relation to contemporary issues in higher education. Analyze the strategic diversity planning process and documents of a selected institution. Recommend innovations and opportunities for transformational change at various levels of the ecological model, using the strategic diversity planning process as a tool.

This course introduces the basics of text mining and text manipulation. The course starts with an understanding of how text is processed by python, the structure of text for both machines and humans, and an overview of the nltk framework for text manipulation. The second week covers the general needs of text manipulation, including regular expressions (text searching), text cleaning, and preparing text for use in machine learning processes. In the third week, we apply basic natural language processing techniques to text and demonstrate how text classification is performed. The final week covers more advanced methods for discovering topics in documents and grouping them by similarity (topic modeling). This course should be taken after: Introduction to Data Science in Python, Applied Plotting, Graphing, and Presenting Data in Python, and Applied Machine Learning in Python.

This course introduces network analysis through tutorials using the NetworkX library. The course begins with an understanding of what network analysis is and the motivations for modeling phenomena as networks. The second week introduces the concepts of network connectivity and stability. The third week looks at ways to measure the importance or centrality of a node in a network. The final week looks at the evolution of networks over time, as well as models of network creation and the problem of link prediction. This course should be taken after: Introduction to Data Science in Python, Applied Plotting, Graphing, and Presenting Data in Python, and Applied Machine Learning in Python.

In this course, we will look at the object-oriented patterns available in PHP. You will learn how to connect to MySQL using the Portable Data Objects (PDO) library and execute SQL commands in PHP. We will also look at how PHP uses cookies and manages session data. You will learn how PHP avoids double-hosting of data, how flash messages are implemented, and how to use the session to log users into web applications. We will then create our first "complete" application, which will have several screens for creating, reading, updating, and deleting (CRUD) our data. This will bring together all the previous concepts and will be the basis for all subsequent web applications. Students are assumed to have completed the Building Web Applications course in this specialization.

In this course, you will go through the steps of installing a text editor, installing MAMP or XAMPP (or equivalent), and creating a MySql database. You will learn about single-table queries and basic SQL syntax, as well as designing databases with multiple tables, foreign keys, and JOINs. Finally, you will learn how to model many-to-many relationships, such as those needed to represent users, roles, and courses.

What are the ethical considerations surrounding consumer privacy and control and big data, especially in the wake of recent major data breaches? This course provides a foundation for analyzing these issues as you explore the ethical implications of collecting, managing, and protecting big data. Explore the broader impact of the field of data science on modern society and the principles of fairness, accountability, and transparency as you gain a deeper understanding of the importance of a common set of ethical values. You will explore the need for voluntary disclosure when using metadata for underlying algorithms and/or complex AI systems, as well as best practices for responsible data governance, the significance of the Fair Information Practices Act, and laws relating to the “right to be forgotten.” This course will help you answer questions such as who owns data, how we value privacy, how to obtain informed consent, and what it means to be fair. This course will be useful for data scientists and anyone starting to use or expanding their use of data. No specific previous knowledge is required.

In this course, you will be introduced to the dictionary data structure and user-defined functions. You will learn about local and global variables, passing optional and keyword parameters, named functions, and lambda expressions. You will also learn about Python's sorted function and how to control the sort order by passing another function as input. In your final project, you will read simulated social media data from a file, calculate sentiment scores, and write .csv files. The course covers Chapters 10-16 of the Python Fundamentals textbook, which is an optional and free companion text to this course. This course is a good choice if you have already taken the Python Fundamentals course and want to gain additional fundamental knowledge of the Python language. The two courses together are designed for those new to Python programming, those who need a refresher on the basics of Python, or those who have some familiarity with Python programming but want to gain a deeper understanding and vocabulary for describing and reasoning about programs. This course is a continuation of the Python Fundamentals course (Course 1 of the Python 3 Programming Specialization), and is the second of five courses in the specialization.

This course teaches you how to retrieve and process data from services on the web. It introduces Python list comprehensions and provides practice retrieving and processing deeply nested data. You'll also learn how to use Python's requests module to interact with REST APIs and what to look for in the documentation for those APIs. As a final project, you'll create a "tag recommender" for the photo-sharing site flickr. This course is a good fit if you've already completed Python Fundamentals and Python Functions, Files, and Dictionaries (Courses 1 and 2 of the Python 3 Programming Specialization). If you already know the basics of Python but want practice retrieving and processing complex nested data from web services, you can also benefit from this course without taking the previous two. This is the third of five courses in the Python 3 Programming Specialization.