This course distills for you expert knowledge and skills mastered by professionals in Health Big Data Science and Bioinformatics. You will learn exciting facts about the human body biology and chemistry, genetics, and medicine that will be intertwined with the science of Big Data and skills to harness the avalanche of data openly available at your fingertips and which we are just starting to make sense of. We’ll investigate the different steps required to master Big Data analytics on real datasets, including Next Generation Sequencing data, in a healthcare and biological context, from preparing data for analysis to completing the analysis, interpreting the results, visualizing them, and sharing the results. Needless to say, when you master these high-demand skills, you will be well positioned to apply for or move to positions in biomedical data analytics and bioinformatics. No matter what your skill levels are in biomedical or technical areas, you will gain highly valuable new or sharpened skills that will make you stand-out as a professional and want to dive even deeper in biomedical Big Data. It is my hope that this course will spark your interest in the vast possibilities offered by publicly available Big Data to better understand, prevent, and treat diseases.

This course will introduce you to Linux, a powerful operating system used by most professional developers! Why add Linux to your C programming skills? Most people use Linux without knowing it! Whether you use a smartphone, search the web, or use an ATM, each time Linux is involved somewhere in the background. It is the most used operating system for embedded devices and high-performance servers. It is also the most common operating system used by developers to create software applications. In this course, you will learn the history of Linux and how its open source community was able to create today’s most advanced operating system. You will navigate the file system, use fundamental Linux commands and master the Linux command line interface. These are essential skills for every developer. You will also be able to produce software written in C using the industry-standard tools on Linux. We are excited to introduce you to Linux and guide you along your path to becoming a skilled user of this powerful operating system! At the end of this short course, you will reach the sixth milestone of the C Programming with Linux Specialization, unlocking the door to a career in computer engineering. Your job Outlook: - Programmers, developers, engineers, managers, and related industries within scientific computing and data science; - Embedded systems such as transportation, utility networks, and aerospace; - Robotics industry and manufacturing; - IoT (Internet of Things) used in smart homes, automation, and wearables. - IEEE, the world’s largest technical professional organization for the advancement of technology, ranks C as one of the top programming languages of 2017 in demand by employers. (Source: IEEE Spectrum) This course has received financial support from the Patrick & Lina Drahi Foundation.

Spatial (map) is considered as a core infrastructure of modern IT world, which is substantiated by business transactions of major IT companies such as Apple, Google, Microsoft, Amazon, Intel, and Uber, and even motor companies such as Audi, BMW, and Mercedes. Consequently, they are bound to hire more and more spatial data scientists. Based on such business trend, this course is designed to present a firm understanding of spatial data science to the learners, who would have a basic knowledge of data science and data analysis, and eventually to make their expertise differentiated from other nominal data scientists and data analysts. Additionally, this course could make learners realize the value of spatial big data and the power of open source software's to deal with spatial data science problems. This course will start with defining spatial data science and answering why spatial is special from three different perspectives - business, technology, and data in the first week. In the second week, four disciplines related to spatial data science - GIS, DBMS, Data Analytics, and Big Data Systems, and the related open source software's - QGIS, PostgreSQL, PostGIS, R, and Hadoop tools are introduced together. During the third, fourth, and fifth weeks, you will learn the four disciplines one by one from the principle to applications. In the final week, five real world problems and the corresponding solutions are presented with step-by-step procedures in environment of open source software's.

This course is for users who want to learn how to write SAS programs to access, explore, prepare, and analyze data. It is the entry point to learning SAS programming for data science, machine learning, and artificial intelligence. It is a prerequisite to many other SAS courses. By the end of this course, you will know how to use SAS Studio to write and submit SAS programs that access SAS, Microsoft Excel, and text data. You will know how to explore and validate data, prepare data by subsetting rows and computing new columns, analyze and report on data, export data and results to other formats, use SQL in SAS to query and join tables. Prerequisites: Learners should have experience using computer software. Specifically, you should be able to understand file structures and system commands on your operating systems and access data files on your operating systems. No prior SAS experience is needed.

By the end of this project you will use the Python Collections Counter, the CSV package's DictReader, and the Collections UserList to read student test data and find the most common test scores. The Python Collection classes are convenience classes that make it easier to process data and extend capabilities of existing classes. The CSV package's DictReader is convenient for reading columnar data. The UserList allows the developer to add functionality to the List, for example to check types. The Counter class is useful for counting common occurrences in arrays and other structures. Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.

In this course, you’ll be learning various supervised ML algorithms and prediction tasks applied to different data. You’ll learn when to use which model and why, and how to improve the model performances. We will cover models such as linear and logistic regression, KNN, Decision trees and ensembling methods such as Random Forest and Boosting, kernel methods such as SVM. Prior coding or scripting knowledge is required. We will be utilizing Python extensively throughout the course. In this course, you will need to have a solid foundation in Python or sufficient previous experience coding with other programming languages to pick up Python quickly. We will be learning how to use data science libraries like NumPy, pandas, matplotlib, statsmodels, and sklearn. The course is designed for programmers beginning to work with those libraries. Prior experience with those libraries would be helpful but not necessary. College-level math skills, including Calculus and Linear Algebra, are required. Our hope for this course is that the math will be understandable but not intimidating. This course can be taken for academic credit as part of CU Boulder’s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder’s departments of Applied Mathematics, Computer Science, Information Science, and others. With performance-based admissions and no application process, the MS-DS is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics. Learn more about the MS-DS program at https://www.coursera.org/degrees/master-of-science-data-science-boulder.

In this one hour long project-based course, you will tackle a real-world problem in robotics. We will be simulating a robot that can move around in an unknown environment, and have it discover its own location using only a terrain map and an elevation sensor. We will encounter some of the classic challenges that make robotics difficult: noisy sensor data, and imprecise movement. We will tackle these challenges with an artificial intelligence technique called a particle filter. By the end of this project, you will have coded a particle filter from scratch using Python and numpy. Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.

Digital Signal Processing is the branch of engineering that, in the space of just a few decades, has enabled unprecedented levels of interpersonal communication and of on-demand entertainment. By reworking the principles of electronics, telecommunication and computer science into a unifying paradigm, DSP is a the heart of the digital revolution that brought us CDs, DVDs, MP3 players, mobile phones and countless other devices. The goal, for students of this course, will be to learn the fundamentals of Digital Signal Processing from the ground up. Starting from the basic definition of a discrete-time signal, we will work our way through Fourier analysis, filter design, sampling, interpolation and quantization to build a DSP toolset complete enough to analyze a practical communication system in detail. Hands-on examples and demonstration will be routinely used to close the gap between theory and practice. To make the best of this class, it is recommended that you are proficient in basic calculus and linear algebra; several programming examples will be provided in the form of Python notebooks but you can use your favorite programming language to test the algorithms described in the course.

This course teaches a calculus that enables precise quantitative predictions of large combinatorial structures. In addition, this course covers generating functions and real asymptotics and then introduces the symbolic method in the context of applications in the analysis of algorithms and basic structures such as permutations, trees, strings, words, and mappings. All the features of this course are available for free. It does not offer a certificate upon completion.

In previous courses in the Specialization, we have discussed how to sequence and compare genomes. This course will cover advanced topics in finding mutations lurking within DNA and proteins. In the first half of the course, we would like to ask how an individual's genome differs from the "reference genome" of the species. Our goal is to take small fragments of DNA from the individual and "map" them to the reference genome. We will see that the combinatorial pattern matching algorithms solving this problem are elegant and extremely efficient, requiring a surprisingly small amount of runtime and memory. In the second half of the course, we will learn how to identify the function of a protein even if it has been bombarded by so many mutations compared to similar proteins with known functions that it has become barely recognizable. This is the case, for example, in HIV studies, since the virus often mutates so quickly that researchers can struggle to study it. The approach we will use is based on a powerful machine learning tool called a hidden Markov model. Finally, you will learn how to apply popular bioinformatics software tools applying hidden Markov models to compare a protein against a related family of proteins.