UC San Diego is an academic powerhouse and economic engine, recognized as one of the top 10 public universities by U.S. News and World Report. Innovation is central to who we are and what we do. Here, students learn that knowledge isn't just acquired in the classroom—life is their laboratory.
Class Central TipsLearn How to Sign up to Coursera courses for free1600+ Coursera Courses That Are Still Completely FreeIn a very competitive workplace, demonstrating your ability to turn challenges into opportunities is an important to way stand out to hiring managers and to your existing management/leadership.In this course, you will learn how to utilize the knowledge and skills needed to leverage left- and right-brain thinking, analyze problems, spur creativity, and implement innovative ideas for your workplace. Using the power of design thinking and Creative Problem Solving models you will work toward data-driven solutions to workplace challenges.You will engage in an active process of identifying/defining a specific challenge,generatinga plan to address the challenge, collecting and analyzing data/information in order to sell your solution to your leadership, implementing your data-driven solution, and evaluating success of the solution.Course Outcomes: 1. UnderstandDesign, out-of-the box, whole brain thinking, Your workplace strengths2. ApplyQualitative and scientific research method for workplace problems, Setting priorities and analyzing for outcomes3. ApplyCreative problem solving and creative thinking, using creative techniques to overcome challenges and bring about innovation4. CreateAction plan, Learn to implement innovative ideas and present ideasPlease Note: This course is scheduled to close new learner enrollment on November 15th, 2021, and fully close on May 15th, 2022. All graded assignments, including peer reviews, must be completed by May 15th in order to be accepted for Certificate credit.
Class Central TipsLearn How to Sign up to Coursera courses for free1600+ Coursera Courses That Are Still Completely FreeIn this course you’ll focus on how “smart” devices have changed how we interact with others in personal ways, impacting how we stay connected in our increasingly mobile society. This will be done through a series of paired teaching sections, exploring a specific “Impact of Computing” in your typical day and the “Technologies and Computing Concepts” that enable that impact, all at a K12-appropriate level. This course is part of a larger Specialization through which you’ll learn impacts of computing concepts you need to know, organized into 5 distinct digital “worlds”, as well as learn pedagogical techniques and evaluate lesson plans and resources to utilize in your classroom. By the end, you’ll be prepared to teach pre-college learners to be both savvy and effective participants in their digital world.In this particular digital world (relationships), you’ll explore the following Impacts & Technology pairs --Impacts (Keep me connected in a mobile society):, personal relationships, facebook, circle of friendsTechnology and Computing Concepts: algorithms, software engineering evolution, heuristics, computer runtime, big O notation, P vs NPImpacts (Making geography-based connections): findings friends, maps, geolocationTechnology and Computing Concepts: data and binary,image encoding, pixels, how color pickers work, filters, blurs In the pedagogy section for this course, in which best practices for teaching computing concepts are explored, you’ll learn about the current CSTA K-12 CS Standards and practice using them to review and apply to lesson plans, as well as how to apply the ICAP framework to connect your students’ engagement to active learning outcomes, such as through peer instruction. In terms of CSTA K-12 computer science standards, we’ll primarily cover learning objectives within the “impacts of computing” concept, while also including some within the “networks and the Internet” concepts and the “data and analysis” concept.Practices we cover include “fostering and inclusive computing culture”, “recognizing and defining computational problems”, and “communicating about computing”.
Class Central TipsLearn How to Sign up to Coursera courses for free1600+ Coursera Courses That Are Still Completely FreeIn a very competitive workplace, establishing, growing, and leveraging your professional community can make all the difference as you search for your initial work opportunity, improve satisfaction at your current job, or find your next work adventure.In this course, you will learn how to tap into your existing personal and professional networks to find your tribe and then leverage your professional community for your benefit (and sometimes the benefit of your employer!).Given that job satisfaction and job opportunities, are often integrally intertwined with “doing good,” you will engage in an introspective exercise evaluating your interests and values so you can incorporate those ideals into your work. Finally, you will understand how service experiences can be extremely valuable in growing your professional community and contributing to company success.
Class Central TipsLearn How to Sign up to Coursera courses for free1600+ Coursera Courses That Are Still Completely FreeWelcome to our course on Object Oriented Programming in Java using data visualization. People come to this course with many different goals -- and we are really excited to work with all of you! Some of you want to be professional software developers, others want to improve your programming skills to implement that cool personal project that you’ve been thinking about, while others of you might not yet know why you’re here and are trying to figure out what this course is all about.This is an intermediate Java course. We recommend this course to learners who have previous experience in software development or a background in computer science.Our goal is that by the end of this course each and every one of you feels empowered to create a Java program that’s more advanced than any you have created in the past and that is personally interesting to you. In achieving this goal you will also learn the fundamentals of Object Oriented Programming, how to leverage the power of existing libraries, how to build graphical user interfaces, and how to use some core algorithms for searching and sorting data. And this course is project-based, so we’ll dive right into the project immediately!We are excited to be offering a unique course structure, designed to support learners of different backgrounds in succeeding at their own pace. The first module explains how this will work and if this course is right for you. We also recommend taking a few minutes to explore the course site. A good place to start is the navigation bar on the left. Click Course Content to see what material we’ll cover each week, as well preview the assignments you’ll need to complete to pass the course. Click Discussions to see forums where you can discuss the course material with fellow students taking the class. Be sure to introduce yourself to everyone in the Meet and Greet forum.This course should take about 6 weeks to complete. You can check out the recommended course schedule below to see a quick overview of the lessons and assignments you’ll complete each week.We’re excited you’re here learning with us. Let’s get started!
Are you interested in analyzing biological datasets but don’t have a strong computational background? Do you want to focus on the biology and learn how to use modern best-practice pipelines that use existing tools? This introductory course, geared towards non-computational biologists, will introduce a specific biological problem each week centered around next generation sequencing and teach you how to use Illumina’s BaseSpace platform to run workflows conveniently and in a user-friendly manner.You will learn current best-practice workflows for Genome Assembly, Variant Calling, Trio Analysis, and Differential Expression Analysis as well as the types of biological problems that motivate them.
Step into the area of more complex problems and learn advanced algorithms to help solve them.This course, part of the Algorithms and Data Structures MicroMasters program, discusses inherently hard problems that you will come across in the real-world that do not have a known provably efficient algorithm, known as NP-Complete problems.You will practice solving large instances of some of these problems despite their hardness using very efficient specialized software and algorithmic techniques including:SAT-solversApproximate algorithmsSpecial cases of NP-hard problemsHeuristic algorithms
Class Central TipsLearn How to Sign up to Coursera courses for free1600+ Coursera Courses That Are Still Completely FreeDrive better business decisions with an overview of how big data is organized, analyzed, and interpreted. Apply your insights to real-world problems and questions.*********Do you need to understand big data and how it will impact your business? This Specialization is for you. You will gain an understanding of what insights big data can provide through hands-on experience with the tools and systems used by big data scientists and engineers. Previous programming experience is not required! You will be guided through the basics of using Hadoop with MapReduce, Spark, Pig and Hive. By following along with provided code, you will experience how one can perform predictive modeling and leverage graph analytics to model problems. This specialization will prepare you to ask the right questions about data, communicate effectively with data scientists, and do basic exploration of large, complex datasets. In the final Capstone Project, developed in partnership with data software company Splunk, you’ll apply the skills you learned to do basic analyses of big data. 3 reviews6 weeks long,18 hours worth of materialView detailsAt the end of the course, you will be able to:*Retrieve data from example database and big data management systems *Describe the connections between data management operations and the big data processing patterns needed to utilize them in large-scale analytical applications*Identify when a big data problem needs data integration*Execute simple big data integration and processing on Hadoop and Spark platformsThis course is for those new to data science.Completion of Intro to Big Data is recommended.No prior programming experience is needed, although the ability to install applications and utilize a virtual machine is necessary to complete the hands-on assignments.Refer to the specialization technical requirements for complete hardware and software specifications.Hardware Requirements: (A) Quad Core Processor (VT-x or AMD-V support recommended), 64-bit; (B) 8 GB RAM; (C) 20 GB disk free. How to find your hardware information: (Windows): Open System by clicking the Start button, right-clicking Computer, and then clicking Properties; (Mac): Open Overview by clicking on the Apple menu and clicking “About This Mac.” Most computers with 8 GB RAM purchased in the last 3 years will meet the minimum requirements.You will need a high speed internet connection because you will be downloading files up to 4 Gb in size. Software Requirements: This course relies on several open-source software tools, including Apache Hadoop. All required software can be downloaded and installed free of charge (except for data charges from your internet provider). Software requirements include: Windows 7+, Mac OS X 10.10+, Ubuntu 14.04+ or CentOS 6+ VirtualBox 5+.
Class Central TipsLearn How to Sign up to Coursera courses for free1600+ Coursera Courses That Are Still Completely Free2% That’s the estimate of how many high school students in all of California took a Computer Science class in 2015. And yet, computers and data are everywhere. Just consider a typical 24 hours in your life … how many different computer devices do you use? We all live in multiple digital worlds that are changing rapidly with new apps, devices, and data analyses offering a constant stream of innovations and technology integrations for our lives.As it's an integral part of our lives, we’re working towards computer science for all - making it possible for every student, every future member of society, to understand computing and technology. To do so, we need teachers. Teachers prepared to both teach computational concepts and use best practices so kids enjoy and see they can be successful in computer science. This is where you (and this Specialization) come in!In this Specialization you will both learn about the impacts of computing in our world and how to teach these impacts to K-12 students. We offer both the technical knowledge and also the pedagogical approaches for teaching these concepts. Along the way you’ll engage with freely available materials you can use in your own classroom, as well as learn from teachers currently teaching these concepts in their classrooms.In short - in this Specialization we'll teach you the computing concepts you need to know and then help you explore and evaluate lesson plans and resources to prepare you for your classroom.
Class Central TipsLearn How to Sign up to Coursera courses for free1600+ Coursera Courses That Are Still Completely Freeمقدمة عن البيانات الضخمةهل أنت مهتم بزيادة معرفتك بأبرز سمات البيانات الضخمة؟ هذه الدورة التدريبية مخصصة للمستجدين في علوم البيانات والمهتمين بفهم أسباب ظهور عصر البيانات الضخمة. فهي مخصصة لمن يريدون الإلمام بالمصطلحات والمفاهيم الأساسية الخاصة بمشكلات البيانات الضخمة وتطبيقاتها وأنظمتها. إنها لمن يريدون البدء في التفكير بشأن الطريقة التي يمكن أن تفيدهم البيانات الضخمة بها في عملهم أو مسيرتهم المهنية. حيث تتعرض مقدمة عن أحد أكثر أطر العمل الشائعة ألا وهو Hadoop، والذي زاد من سهولة تحليل البيانات الضخمة وإمكانية الوصول إليها، فقد زاد من احتمالية تطوير البيانات الضخمة لعالمنا!وفي نهاية الدورة التدريبية، ستتمكن مما يلي:*وصف أبرز سمات البيانات الضخمة بما في ذلك الأمثلة على مشكلات البيانات الضخمة على أرض الواقع التي تتضمن ثلاثة مصادر أساسية للبيانات الضخمة وهي الأفراد والمؤسسات وأدوات الاستشعار.* شرح خصائص البيانات الضخمة التي تبدأ بالحرف V مثل (volume (الحجم)، وvelocity (السرعة)، وvariety (التنوع)، وveracity (الصحة)، وvalence (التكافؤ)، وvalue (القيمة)) ولماذا تؤثر كل خاصية من تلك الخصائص في جمع البيانات ومتابعتها وتخزينها وتحليلها والإبلاغ عنها* الاستفادة بقيمة البيانات الضخمة عن طريق استخدام عملية مكونة من 5 خطوات لهيكلة تحليلك. * تحديد المشكلات التي تندرج تحت البيانات الضخمة والتي لا تندرج تحتها، والقدرة على إعادة تشكيل مشكلات البيانات الضخمة مثل مسائل علوم البيانات.* تقديم تفسير للمكونات الهندسية والنماذج البرمجية التي تستخدم في التحليل القابل للتوسيع للبيانات الضخمة.* تلخيص ميزات المكونات الأساسية لمكدس Hadoop وقيمتها بما في ذلك مورد YARN ونظام إدارة الوظائف، ونظام ملفات HDFS، ونموذج برمجة MapReduce.* تثبيت البرامج وتشغيلها باستخدام إطار عمل Hadoop!هذه الدورة التدريبية موجهة للمستجدين في علوم البيانات.لا يلزم توافر خبرة برمجية مسبقة، على الرغم من ضرورة توافر القدرة على تثبيت التطبيقات واستخدام الأجهزة الظاهرية لإنجاز الواجبات العملية.متطلبات الأجهزة:(أ) معالج رباعي النواة (يوصى بمعالج يدعم ميزة VT-x أو AMD-V)، 64 بت؛ (ب) ذاكرة وصول عشوائي بحجم 8 جيجابايت؛ (ج) مساحة خالية بحجم 20 جيجابايت. طريقة العثور على معلومات الأجهزة: (نظام Windows): افتح النظام عن طريق الضغط على زر Start (بدء التشغيل)، وانقر بزر الفأرة الأيمن على أيقونة Computer (جهاز الكمبيوتر)، ثم انقر على Properties (خصائص)؛ (نظام Mac): افتح Overview (نظرة عامة) عن طريق الضغط على قائمة Apple والنقر على "About This Mac." سيتوفر الحد الأدنى من المتطلبات في معظم أجهزة الكمبيوتر ذات الذاكرة العشوائية سعة 8 جيجابايت والتي تم شراؤها في آخر 3 أعوام. وستحتاج إلى سرعة اتصال عالية بالإنترنت لأنك ستقوم بتنزيل ملفات يصل حجمها إلى 4 جيجابايت.المتطلبات البرمجية: تعتمد هذه الدورة التدريبية على العديد من الأدوات البرمجية مفتوحة المصدر، ومنها Apache Hadoop. ويمكن تنزيل جميع البرامج المطلوبة وتثبيتها مجانًا.تتضمن المتطلبات البرمجية ما يلي: Windows 7+ أو Mac OS X 10.10+ أو Ubuntu 14.04+ أو CentOS 6+ VirtualBox 5+.
In the first half of this course, we'll investigate DNA replication, and ask the question, where in the genome does DNA replication begin? You will learn how to answer this question for many bacteria using straightforward algorithms to look for hidden messages in the genome.In the second half of the course, we'll examine a different biological question, and ask which DNA patterns play the role of molecular clocks. The cells in your body manage to maintain a circadian rhythm, but how is this achieved on the level of DNA? Once again, we will see that by knowing which hidden messages to look for, we can start to understand the amazingly complex language of DNA. Perhaps surprisingly, we will apply randomized algorithms to solve problems.Finally, you will get your hands dirty and apply existing software tools to find recurring biological motifs within genes that are responsible for helping Mycobacterium tuberculosis go "dormant" within a host for many years before causing an active infection.This course begins a series of classes illustrating the power of computing in modern biology.
Virtual reality (VR) is one of the hottest emerging technologies in the entertainment industry today. Millions of VR devices have been sold in the US alone, but most software developers have no formal training on the technology. This Professional Certificate program will teach you what VR devices exist, how VR technology works, and how to write software, often called VR experiences, for it.You will learn effective 3D interaction techniques to use VR applications, how to write VR applications in WebVR and Unity 3D, and what features make a VR application successful. You will also learn the required mathematics for successful VR applications and how computer graphics are rendered onto a screen. The final course in this program will allow you to apply the material learned in the previous courses to create your own VR app.This program will provide you with a strong foundation to develop VR apps in all areas VR is used, including entertainment and gaming. This VR program also provides a solid foundation for people who want to develop augmented reality (AR) applications.
Class Central TipsLearn How to Sign up to Coursera courses for free1600+ Coursera Courses That Are Still Completely FreeThe Code Free Data Science class is designed for learners seeking to gain or expand their knowledge in the area of Data Science.Participants will receive the basic training in effective predictive analytic approaches accompanying the growing discipline of Data Science without any programming requirements.Machine Learning methods will be presented by utilizing the KNIME Analytics Platform to discover patterns and relationships in data. Predicting future trends and behaviors allows for proactive, data-driven decisions.During the class learners will acquire new skills to apply predictive algorithms to real data, evaluate, validate and interpret the results without any pre requisites for any kind of programming.Participants will gain the essential skills to design, build, verify and test predictive models.You Will Learn•How to design Data Science workflows without any programming involved•Essential Data Science skills to design, build, test and evaluate predictive models•Data Manipulation, preparation and Classification and clustering methods•Ways to apply Data Science algorithms to real data and evaluate and interpret the results
Class Central TipsLearn How to Sign up to Coursera courses for free1600+ Coursera Courses That Are Still Completely FreeYou’ve hit a major milestone as a computer scientist and are becoming a capable programmer. You now know how to solve problems, write algorithms, and analyze solutions; and you have a wealth of tools (like data structures) at your disposal.You may now be ready for an internship or (possibly) an entry-level software engineering job.But can you land the internship/job?It depends in part on how well you can solve new technical problems and communicate during interviews.How can you get better at this?Practice!With the support of Google’s recruiting and engineering teams we’ve provided tips, examples, and practice opportunities in this course that may help you with a number of tech companies.We’ll assist you to organize into teams to practice.Lastly, we’ll give you basic job search advice, and tips for succeeding once you’re on the job.
Class Central TipsLearn How to Sign up to Coursera courses for free1600+ Coursera Courses That Are Still Completely FreeIn this course we will learn about Recommender Systems (which we will study for the Capstone project), and also look at deployment issues for data products. By the end of this course, you should be able to implement a working recommender system (e.g. to predict ratings, or generate lists of related products), and you should understand the tools and techniques required to deploy such a working system on real-world, large-scale datasets.This course is the final course in the Python Data Products for Predictive Analytics Specialization, building on the previous three courses (Basic Data Processing and Visualization, Design Thinking and Predictive Analytics for Data Products, and Meaningful Predictive Modeling). At each step in the specialization, you will gain hands-on experience in data manipulation and building your skills, eventually culminating in a capstone project encompassing all the concepts taught in the specialization.
Class Central TipsLearn How to Sign up to Coursera courses for free1600+ Coursera Courses That Are Still Completely FreeThe sequencing of the human genome at the start of this century fueled a computational revolution in biology. As a result, modern biology produces as many new algorithms as any other fundamental realm of science.Once we have sequenced a genome, it may look like an incomprehensible string of the nucleotides A, C, G, and T. Yet hidden in these four letters is a secret language. In this course, we will start understanding this language by using computer programming. What makes this course distinct is that we assume that you have never programmed before.While learning Python from the ground up, we will write algorithms to determine where a bacterium starts replicating its genome, a problem with applications in genetic engineering. We will also use programming to learn how a cell knows what time of day it is and how the bacterium causing tuberculosis can hide from antibiotics.This course offers a much gentler-paced alternative to Finding Hidden Messages in DNA, the first course in the Bioinformatics Specialization. After completing it, we hope you will be well prepared to jump into the full Specialization!