In 2020 the world will generate 50 times the amount of data as in 2011. And 75 times the number of information sources (IDC, 2011). Being able to use this data provides huge opportunities and to turn these opportunities into reality, people need to use data to solve problems.

This Specialization, in collaboration with Tableau, is intended for newcomers to data visualization with no prior experience using Tableau. We leverage Tableau's library of resources to demonstrate best practices for data visualization and data storytelling. You will view examples from real-world business cases and journalistic examples from leading media companies.

WHAT YOU WILL LEARN

- Examine, navigate, and learn to use the various features of Tableau

- Create and design visualizations and dashboards for your intended audience

- Assess the quality of the data and perform exploratory analysis

- Combine the data to and follow the best practices to present your story

By the end of this specialization, you will be able to generate powerful reports and dashboards that will help people make decisions and take action based on their business data. You will use Tableau to create high-impact visualizations of common data analyses to help you see and understand your data. You will apply predicative analytics to improve business decision-making. The Specialization culminates in a Capstone Project in which you will use sample data to create visualizations, dashboards, and data models to prepare a presentation to the executive leadership of a fictional company.

The Data Science and Machine Learning for Asset Management Specialization has been designed to deliver a broad and comprehensive introduction to modern methods in Investment Management, with a particular emphasis on the use of data science and machine learning techniques to improve investment decisions. 

By the end of this specialization, you will have acquired the tools required for making sound investment decisions, with an emphasis not only on the foundational theory and underlying concepts, but also on practical applications and implementation. Instead of merely explaining the science, we help you build on that foundation in a practical manner, with an emphasis on the hands-on implementation of those ideas in the Python programming language through a series of dedicated lab sessions.

Machine Learning is one of the most in-demand skills for jobs related to modern AI applications, a field in which hiring has grown 74% annually for the last four years (LinkedIn). This Professional Certificate from IBM is intended for anyone interested in developing skills and experience to pursue a career in Machine Learning and leverage the main types of Machine Learning: Unsupervised Learning, Supervised Learning, Deep Learning, and Reinforcement Learning. It also complements your learning with special topics, including Time Series Analysis and Survival Analysis.

This program consists of 6 courses providing you with solid theoretical understanding and considerable practice of the main algorithms, uses, and best practices related to Machine Learning . You will follow along and code your own projects using some of the most relevant open source frameworks and libraries. Although it is recommended that you have some background in Python programming, statistics, and linear algebra, this intermediate series is suitable for anyone who has some computer skills, interest in leveraging data, and a passion for self-learning. We start small, provide a solid theoretical background and code-along labs and demos, and build up to more complex topics.

In addition to earning a Professional Certificate from Coursera, you will also receive a digital Badge from IBM recognizing your proficiency in Machine Learning. This Professional Certificate has a strong emphasis on developing the skills that help you advance a career in Machine Learning. All the courses include a series of hands-on labs and final projects that help you focus on a specific project that interests you. Throughout this Professional Certificate, you will gain exposure to a series of tools, libraries, cloud services, datasets, algorithms, assignments and projects that will provide you with practical skills with applicability to Machine Learning jobs. These skills include: Tools: Jupyter Notebooks and Watson Studio Libraries: Pandas, NumPy, Matplotlib, Seaborn, ipython-sql, Scikit-learn, ScipPy, Keras, and TensorFlow.

In this Specialization, you will learn to analyze and visualize data in R and create reproducible data analysis reports, demonstrate a conceptual understanding of the unified nature of statistical inference, perform frequentist and Bayesian statistical inference and modeling to understand natural phenomena and make data-based decisions, communicate statistical results correctly, effectively, and in context without relying on statistical jargon, critique data-based claims and evaluated data-based decisions, and wrangle and visualize data with R packages for data analysis.

You will produce a portfolio of data analysis projects from the Specialization that demonstrates mastery of statistical data analysis from exploratory analysis to inference to modeling, suitable for applying for statistical analysis or data scientist positions.

WHAT YOU WILL LEARN

- Analyze and visualize data

- Fit, examine, and utilize regression models to examine relationships between multiple variables

- Perform hypothesis tests, interpret statistical results (e.g., p-values), and report the results of your analysis to clients

- Install and use R and RStudio

In this specialization, you will learn essential leadership skills, including how to inspire and motivate individuals, manage talent, influence without authority, and lead teams. In this specialization, you will not only learn from Michigan faculty. You will also learn directly from exceptional leaders including Jeff Brodsky, Global Head of HR for Morgan Stanley, and John Beilein, Head Coach of the University of Michigan Men’s Basketball Team. We will share with you our research on how to lead people and teams effectively, and work with you to apply these insights to your own teams and leadership. In every course, you will have an opportunity to apply new leadership skills by working through a series of practical leadership assignments. In addition, the capstone will enable you to work on live leadership challenges faced by senior leaders from premier Fortune 500 firms and receive their feedback on your ideas and solutions.

Top students completing the Specialization will be eligible to receive one or more of the following: office hours with your faculty, one credit toward Michigan Ross’ Distinguished Leader certificate, a waived application fee to Michigan Ross graduate programs, or a LinkedIn recommendation/endorsement by Ross faculty.

WHAT YOU WILL LEARN

- Create and communicate your vision as a leader

- Use the Michigan Model of Leadership to define organizational goals

- Manage talent to improve employee performance, development and retention

- Create a high-quality team charter that enhances team performance

This Specialization starts out by teaching basic concepts in Python and ramps up to more complex subjects such as object-oriented programming and data structures in Java. By the time learners complete this series of four courses, they will be able to write fully-functional programs in both Python and Java, two of the most well-known and frequently used programming languages in the world today.

Introduction to Programming with Python and Java is for students and professionals who have minimal or no prior programming exposure. It’s for motivated learners who have experience with rigorous coursework, and are looking to gain a competitive edge in advancing their careers. It’s for folks who are thinking about applying to the University of Pennsylvania’s online Master of Computer and Information Technology degree and want to sample some of the lecture videos and content from the first course in the program. We hope this Specialization is for you.

Topics in this Specialization include language syntax, style, programming techniques, and coding conventions. Learn about best practices and good code design, code testing and test-driven development, code debugging, code and program documentation, and computational thinking.

WHAT YOU WILL LEARN

- Describe core Python programming concepts, including how to configure tools for Python code and write fully functional programs using data structures

- Apply core principles of object-oriented programming and Java to write fully functional programs using classes and methods, and perform unit testing

- Examine core data science techniques and concepts using Python, including using data analysis libraries and developing data visualization skills

- Understand Java inheritance and apply techniques for parsing text in files, using advanced data structures to store information, and debugging code

Learners will write fully-functional Python programs, including the implementation of an online banking system and a data analysis project analyzing movies and ratings from IMDB. Learners will apply Python programming, file I/O, data analysis, and visualization, using both PyCharm and Jupyter Notebook.

Learners will also write fully-functional Java programs, including a text file parser that reads, writes, and analyzes text files. Learners will apply Java programming, object-oriented principles, data structures, file I/O, unit testing, and code debugging, and using Eclipse.

Do you have people reporting to you that need managing? Or perhaps you want to consider a career in human resources? Or freshen up your HR knowledge?

This specialization provides a robust introduction to the key principles, policies, and practices of human resource management. The specialization begins with a foundational course that considers alternative approaches to managing human resources, provides a background to the U.S. legal context in which employees are hired, fired, rewarded, and managed, and outlines the different reasons that people are motivated to work. The remaining three courses tackle three core areas that all managers should understand: hiring employees, evaluating their performance, and rewarding them. Throughout the courses, an accessible, scientific approach is embraced such that best practices and practical tips are informed by research, but presented in accessible, applied ways.

Upon completing the specialization, learners will have a deeper understanding of what works in the workplace, including a toolkit of best practices for hiring, managing, and rewarding employees.

The specialization will be valuable for managers and entrepreneurs taking on these responsibilities as well as anyone else interested in the fundamental principles of human resource management. The Capstone Project will provide an opportunity to apply this knowledge to a real situation, including your own organization or work unit if desired.

WHAT YOU WILL LEARN

- Understanding alternative approaches to managing human resources and appreciating the diversity of factors that motivate workers

- Avoiding key mistakes in (mis)managing human resources

- Applying best practices for hiring and rewarding employees, and for managing employee performance

The projects apply the knowledge learned to specific work groups of your choosing (e.g., your own work team). You will learn how to proactively identify key challenges around staffing, performance management, and compensation and then use this to construct strategies and plans for addressing these challenges in the context of your chosen work group.

Computer science legend Donald Knuth once said “I don’t understand things unless I try to program them.” We also believe that the best way to learn an algorithm is to program it. However, many excellent books and online courses on algorithms, that excel in introducing algorithmic ideas, have not yet succeeded in teaching you how to implement algorithms, the crucial computer science skill that you have to master at your next job interview. We tried to fill this gap by forming a diverse team of instructors that includes world-leading experts in theoretical and applied algorithms at UCSD (Daniel Kane, Alexander Kulikov, and Pavel Pevzner) and a former software engineer at Google (Neil Rhodes).

This unique combination of skills makes this Specialization different from other excellent MOOCs on algorithms that are all developed by theoretical computer scientists. While these MOOCs focus on theory, our Specialization is a mix of algorithmic theory/practice/applications with software engineering. You will learn algorithms by implementing nearly 100 coding problems in a programming language of your choice. To the best of knowledge, no other online course in Algorithms comes close to offering you a wealth of programming challenges (and puzzles!) that you may face at your next job interview. We invested over 3000 hours into designing our challenges as an alternative to multiple-choice questions that you usually find in MOOCs.

WHAT YOU WILL LEARN

- Play with 50 algorithmic puzzles on your smartphone to develop your algorithmic intuition!  Apply algorithmic techniques (greedy algorithms, binary search, dynamic programming, etc.) and data structures (stacks, queues, trees, graphs, etc.) to solve 100 programming challenges that often appear in interviews at high-tech companies. Get instant feedback on whether your solution is correct.

- Learn exactly the same material as undergraduate students in “Algorithms 101” at top universities and more! We are excited that students from various parts of the world are now studying our online materials in the Algorithms 101 classes at their universities. Here is a quote from the website of Professor Sauleh Eetemadi from Iran University of Science and Technology: “After examining syllabus and course material from top universities including Stanford, Princeton, and MIT we have chosen to follow the Data Structures and Algorithms Specialization from UCSD...due to excellent course material and its practical approach.”

- Apply the newly learned algorithms to solve real-world challenges: navigating in a Big Network or assembling a genome of a deadly pathogen from millions of short substrings of its DNA.

- If you decide to venture beyond Algorithms 101, try to solve more complex programming challenges (flows in networks, linear programming, streaming algorithms, etc.) and complete an equivalent of a graduate course in algorithms!

The specialization contains two real-world projects: Big Networks and Genome Assembly. You will analyze both road networks and social networks and will learn how to compute the shortest route between New York and San Francisco 1000 times faster than the shortest path algorithms you learn in the standard Algorithms 101 course! Afterwards, you will learn how to assemble genomes from millions of short fragments of DNA and how assembly algorithms fuel recent developments in personalized medicine.

Learn to provide psychological first aid to people in an emergency by employing the RAPID model: Reflective listening, Assessment of needs, Prioritization, Intervention, and Disposition.

Utilizing the RAPID model (Reflective listening, Assessment of needs, Prioritization, Intervention, and Disposition), this specialized course provides perspectives on injuries and trauma that are beyond those physical in nature. The RAPID model is readily applicable to public health settings, the workplace, the military, faith-based organizations, mass disaster venues, and even the demands of more commonplace critical events, e.g., dealing with the psychological aftermath of accidents, and robberies, suicide, homicide, or community violence. In addition, the RAPID model has been found effective in promoting personal and community resilience.

Participants will increase their abilities to:

- Discuss key concepts related to PFA

- Listen reflectively

- Differentiate benign, non-incapacitating psychological/ behavioral crisis reactions from more severe, potentially incapacitating, crisis reactions

- Prioritize (triage) psychological/ behavioral crisis reactions

- Mitigate acute distress and dysfunction, as appropriate

- Recognize when to facilitate access to further mental health support

- Practice self-care

Developed in collaboration with Johns Hopkins Open Education Lab.

Around the world, we find ourselves facing global epidemics of obesity, Type 2 Diabetes and other predominantly diet-related diseases. To address these public health crises, we urgently need to explore innovative strategies for promoting healthful eating. There is strong evidence that global increases in the consumption of heavily processed foods, coupled with cultural shifts away from the preparation of food in the home, have contributed to high rates of preventable, chronic disease. In this course, learners will be given the information and practical skills they need to begin optimizing the way they eat. This course will shift the focus away from reductionist discussions about nutrients and move, instead, towards practical discussions about real food and the environment in which we consume it. By the end of this course, learners should have the tools they need to distinguish between foods that will support their health and those that threaten it. In addition, we will present a compelling rationale for a return to simple home cooking, an integral part of our efforts to live longer, healthier lives.