This is a self-paced lab that takes place in the Google Cloud console. In this lab you explore the basics of Go (golang) by compiling and testing a Go app interactively, deploying it to App Engine, then access data in BigQuery and Firestore.

Introduction: Cancer datasets are plentiful, complicated, and hold information that may be critical for the next research advancements. In order to use these data to their full potential, researchers are dependent on the specialized data tools that are continually being published and developed. Bioinformatics tools can often be unfriendly to their users, who often have little to no background in programming (Bolchini et al. 2008). The usability and quality of the documentation of a tool can be a major factor in how efficiently a researcher is able to obtain useful findings for the next steps of their research. Increasing the usability and quality of documentation for a tool is not only helpful for the researcher users, but also for the developers themselves – the many hours of work put into the product will have a higher impact if the tool is usable by the target user community. 70% of bioinformatics tools surveyed by Duck et al. (2016) were not reused beyond their introductory publication. Even the most well-programmed tool will be overlooked by the user community if there is little to no user-friendly documentation or if they were not designed with the user in mind. Target Audience: The course is intended for cancer informatics tool developers, particularly those creating tools as a part of the Informatics Technology Cancer Research. Learning Objectives: 1. Understanding why usability and documentation is vital 2. Identifying your user community 3. Building documentation and tutorials to maximize the usability of developed tools 4. Obtaining feedback from your users Curriculum: This course will demonstrate how to: Understanding why usability and documentation is vital, Identifying your user community, Building documentation and tutorials to maximize the usability of developed tools, Obtaining feedback from your users The course includes a hands-on exercises with templates for building documentation and tutorials for cancer informatics tools. Individuals who take this course are encouraged to use these templates as they follow along with the course material to help increase the usability of their informatics tool. This course is part of a series of courses for the Informatics Technology for Cancer Research (ITCR) called the Informatics Technology for Cancer Research Education Resource. This material was created by the ITCR Training Network (ITN) which is a collaborative effort of researchers around the United States to support cancer informatics and data science training through resources, technology, and events. This initiative is funded by the following grant: National Cancer Institute (NCI) UE5 CA254170. Our courses feature tools developed by ITCR Investigators and make it easier for principal investigators, scientists, and analysts to integrate cancer informatics into their workflows. Please see our website at www.itcrtraining.org for more information.

This course covers the essential information that every serious programmer needs to know about algorithms and data structures, with emphasis on applications and scientific performance analysis of Java implementations. Part I covers elementary data structures, sorting, and searching algorithms. Part II focuses on graph- and string-processing algorithms. All the features of this course are available for free. It does not offer a certificate upon completion.

In this course you will learn how to apply the functional programming style in the design of larger Scala applications. You'll get to know important new functional programming concepts, from lazy evaluation to structuring your libraries using monads. We'll work on larger and more involved examples, from state space exploration to random testing to discrete circuit simulators. You’ll also learn some best practices on how to write good Scala code in the real world. Finally, you will learn how to leverage the ability of the compiler to infer values from types. Several parts of this course deal with the question how functional programming interacts with mutable state. We will explore the consequences of combining functions and state. We will also look at purely functional alternatives to mutable state, using infinite data structures or functional reactive programming. Recommended background: You should have at least one year programming experience. Proficiency with Java or C# is ideal, but experience with other languages such as C/C++, Python, Javascript or Ruby is also sufficient. You should have some familiarity with using the command line. This course is intended to be taken after Functional Programming Principles in Scala: https://www.coursera.org/learn/progfun1.

Welcome to IBM COBOL – Basic Testing and Debugging! By enrolling in this course, you are taking a big step in increasing your knowledge and hands on experience with IBM COBOL testing and debugging topics and technologies. In this course, you will describe common errors associated with working with COBOL. You will list file status codes. You will also describe the process of debugging COBOL code. So let’s get started!

How do we give instructions to a computer? Isn't programming hard? Not really! Whether it's giving someone directions to a nearby store or writing out some dance moves we frequently exhibit aspects of computational thinking in our everyday lives! This class teaches the first key concepts of programming -- sequences of instructions and basic counted repetition of instructions. For each concept, we'll start by helping you connect real-world experiences you are already familiar with to the programming concept you are about to learn. Next, through a cognitively scaffolded process we'll engage you in developing your fluency with problem solving with sequences and repeated instructions in a way that keeps frustration at a minimum. Along the way you will learn about the common challenges or "bugs" students have with these concepts as well as ways to help them find and fix those concepts. You'll also be guided in running classroom discussions to help students develop deeper understanding of these concepts. Finally, you'll learn about a recommended pedagogical practice, Pair Programming, and find out why research recommends teaching block-based programming first.

In this 1-hour long project-based course, you will gain hands-on experience with one of the hottest web development framework, React. If you have some experience with HTML, CSS, JavaScript and want to start developing with React, this course is for you! By the end of this course You will understand the fundamental of React and be able to create and build a website with React with ease.

Note: You should complete all the other courses in this Specialization before beginning this course. This six-week long Project course of the Data Mining Specialization will allow you to apply the learned algorithms and techniques for data mining from the previous courses in the Specialization, including Pattern Discovery, Clustering, Text Retrieval, Text Mining, and Visualization, to solve interesting real-world data mining challenges. Specifically, you will work on a restaurant review data set from Yelp and use all the knowledge and skills you’ve learned from the previous courses to mine this data set to discover interesting and useful knowledge. The design of the Project emphasizes: 1) simulating the workflow of a data miner in a real job setting; 2) integrating different mining techniques covered in multiple individual courses; 3) experimenting with different ways to solve a problem to deepen your understanding of techniques; and 4) allowing you to propose and explore your own ideas creatively. The goal of the Project is to analyze and mine a large Yelp review data set to discover useful knowledge to help people make decisions in dining. The project will include the following outputs: 1. Opinion visualization: explore and visualize the review content to understand what people have said in those reviews. 2. Cuisine map construction: mine the data set to understand the landscape of different types of cuisines and their similarities. 3. Discovery of popular dishes for a cuisine: mine the data set to discover the common/popular dishes of a particular cuisine. 4. Recommendation of restaurants to help people decide where to dine: mine the data set to rank restaurants for a specific dish and predict the hygiene condition of a restaurant. From the perspective of users, a cuisine map can help them understand what cuisines are there and see the big picture of all kinds of cuisines and their relations. Once they decide what cuisine to try, they would be interested in knowing what the popular dishes of that cuisine are and decide what dishes to have. Finally, they will need to choose a restaurant. Thus, recommending restaurants based on a particular dish would be useful. Moreover, predicting the hygiene condition of a restaurant would also be helpful. By working on these tasks, you will gain experience with a typical workflow in data mining that includes data preprocessing, data exploration, data analysis, improvement of analysis methods, and presentation of results. You will have an opportunity to combine multiple algorithms from different courses to complete a relatively complicated mining task and experiment with different ways to solve a problem to understand the best way to solve it. We will suggest specific approaches, but you are highly encouraged to explore your own ideas since open exploration is, by design, a goal of the Project. You are required to submit a brief report for each of the tasks for peer grading. A final consolidated report is also required, which will be peer-graded.

If you are relatively new to the world of cloud computing, you might have come across a term JSON and not know what it is exactly. JSON stands for JavaScript Object Notation and is widely used to exchange information between clients and servers. Many No-SQL databases also uses the format to store data. In this 1-hour long project-based course, you will learn about the format and structure of JSON, accept and process JSON in your code, and transmit JSON to other processes. No prior experience with JSON is required but basic to intermediate HTML and JavaScript is required.

This is the fourth and final course in the Computational Thinking with JavaScript specialisation. It is an added-value unit and entirely builds on what you will have learned in the first three units and enables you to synthesise and develop that knowledge. The course supports you to develop your own larger application, using a combination of the technologies you have seen already, including HTML, JavaScript and various libraries, while also encouraging you to reach out to incorporate new libraries. You are taken through the software development process step-by-step, with guidance along the way. You have plenty of opportunity to interact with your peers on the course, and to review their work at periodic intervals.