Take Online Courses from Prestigious Institutions at Coursera
Name: Coursera (Visit Coursera)
Type: Free Online Course Directory
Best Website For: Online Courses from Prestigious Organizations
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Coursera comes highly-rated and is featured in multiple "best website" lists. The site is the host of high-quality free online courses in many different subjects.
U.S News & World Report has named Arizona State University the most innovative school in America for three years in a row. Now on Coursera, their Master of Computer Science pushes the envelope in computing and covers cutting-edge topics like AI, cybersecurity, and big data. Ranked in the Top 5 for graduate employment by The Wall Street Journal, a Master of Computer Science from ASU gives you the edge you need to launch a new career or move ahead in your current organization. Earn credit toward your master’s by trying one of the degree courses below. If you are accepted to the program, you’ll get credit for courses you’ve completed.
This course introduces the world of database systems. It provides the foundation that will enable learners to master skills in data modeling and information, as well as extract information using existing database management systems. The following main topics are covered: database design/modeling, data storage and indexing, query processing/optimization, transaction management, database security, and data analytics.
The increased capabilities of a collection of logically interrelated databases distributed over a computer network enable scalable data processing. This course addresses the components of these systems, covering the following main topics: distributed database architectures, distributed data storage and indexing, distributed and parallel query processing/optimization, and concurrency control in distributed Parallel Database Systems.
This course answers the questions, “What is data visualization and What is the power of visualization?” It also introduces core concepts such as dataset elements, data warehouses and exploratory querying, and combinations of visual variables for graphic usefulness, as well as the types of statistical graphs, tools that are essential to exploratory data analysis.
Covering the tools and techniques of both multivariate and geographical analysis, this course provides hands-on experience in visualizing data that represents multiple variables. This course will use statistical techniques and software to develop and analyze geographical knowledge.
Learn more about ASU’s Master of Computer Science.
The post New Computer Science Courses from America’s Most Innovative University appeared first on Coursera Blog.
By: Alan Hickey, Vinod Bakthavachalam, Alexandra Urban and Talia Kolodny
The challenge of expanding access to education is central to creating an equitable and thriving society. Yet, overcoming structural inequalities demands more than mere access. Research shows a significant gender gap in both enrollment and achievement when it comes to scientific and technical education. This gap also occurs in the online learning world — sometimes, it’s even wider. We would like to suggest a few strategies that promote equal opportunity and help diverse communities of learners succeed.
In business, data science, and computer science courses on Coursera, male learners are 5% more likely to complete courses when compared to female learners. Coursera’s data shows that the gap between male and female learners widens even more for learners who score poorly on their first assignment.
Leveraging data and machine learning for solutions
Using machine learning tools, we’ve found that targeted review material for struggling learners (a video in this case) significantly boosted exam completion rates and continuation rates for female learners. The review material had no effect for male’s performance. The results suggested that small behavioral interventions can positively impact learning outcomes specifically for females, thus reducing gaps between genders. To learn more about this experiment, stay tuned for an upcoming post on Medium.
What you can do
- Provide learners with actionable feedback: research shows that immediate and actionable feedback on assessments can support better learning outcomes. Feedback supports all learner’s success, but females can benefit even more, thus reducing gender gaps. As mentioned above, females are more likely than males to succeed after reviewing relevant content when struggling. There are several ways to offer effective feedback on Coursera. One is embedding a short video in each of the answer options in a quiz. Using videos in online quiz feedback shows a significant positive impact on learners’ progression rates.
- Encourage learners to try again: the Coursera platform is designed around the concept of mastery learning. Mistakes are an important part of the learning process. Believing one can learn anything and embracing failures as milestones in that process are crucial for success. Therefore, encouraging learners to grow from feedback and try again until achieving mastery, can help foster a growth mindset.
- Walk through common pitfalls: in your course materials and assessment feedback, highlight common mistakes and explain them. This is an additional channel that learners can use to process difficult concepts.
- Intentional data analysis can surface targeted areas for improvement to benefit learners and especially underrepresented groups.
- Small interventions can go a long way to support a more inclusive learning experience.
- Encouraging learners to develop a growth mindset with effective learning design strategies can make all the difference.
As educators, there’s so much you can do to reduce the gender gap and foster an inclusive learning environment. We look forward to partnering with you to explore innovative tools that help learners around the globe.
The post A Growth Mindset Can Reduce the Gender Gap in STEM appeared first on Coursera Blog.
In Gender and Sexuality: Diversity and Inclusion in the Workplace, instructor Julie Beaulieu covers questions like, “What is gender?” and “What do we mean by LGBTQIA+?” She also talks about how identity shapes experience in culture and the workplace. Hear from Julie on what you’ll learn in the course and how Pride Month is an opportunity to support inclusion and equality in the workplace and the LGBTQIA+ community.
Why did you choose to bring this course to Coursera?
I was excited for the opportunity to think about how to teach topics in gender and sexuality to a community of thinkers from diverse personal and professional backgrounds. I am also very passionate about the topic so I was grateful to have the opportunity to introduce learners to theories of gender and sexuality.
Who should be taking this course?
The course is designed for learners from any background or experience level. It’s specifically designed for people who are interested in LGBTQIA+ diversity, but it would be useful as well for people who’ve never really thought much about the topics. Ideas and theories about gender and sexuality are pretty central to our lives—all of us are impacted by it, not just LGBTQIA+ folks—so I hope that people who may have had little interest in the past take the class and actually grow a bit more curious about how we all move through the world we share.
What do you hope learners will walk away with after taking this course?
The most important thing is to realize that our ideas about gender, sexuality, intimacy, and embodiment are deeply shaped through the social, political, and historical aspects of our lives. Knowing this allows us to recognize difference as both predictable and interesting. I hope people come away from the course with an appreciation for what makes us different, as opposed to rejection or fear of difference.
Can you briefly talk about what LGBTQIA+ stands for?
The initialism LGBTQIA+ refers to lesbian, gay, bisexual, trans, queer (or questioning), intersex, asexual (or ally), and plus (which leaves space for folks who are part of the community but in ways that are not listed). LGBTQIA+ people experience some of the same forms of oppression; cultures of resistance and community form as a response to this oppression and form communities for education, support, and consciousness-raising, which is in part what LGBTQIA+ reflects.
Can you discuss how Pride Month is a good opportunity for the support of racial diversity, equality, and inclusion in the LGBTQIA+ community?
The history of pride in a U.S. context provides a rich history of people from different class and race experiences fighting for rights and justice for diverse communities; much of the early movement was spearheaded by historically-minoritized people. The large pride celebrations that we see around the world shows promise for change, but history teaches us that progress is highly uneven; pride is a time to reflect on the work we have left to do, much of which involves working on race and class justice in LGBTQIA+ communities. It’s a celebration of course, but if that celebration excludes LGBTQIA+ people of color then it isn’t effective for all. Ideally, pride brings LGBTQIA+ together, helps us learn more about each other, and see beyond our own experiences.
The post Making the Workplace Inclusive by Understanding Gender and Sexuality appeared first on Coursera Blog.
Mohamed Sarwat is a professor of computer science at Arizona State University. He recently participated in a Quora session where he answered questions on Computer Science trends and how to navigate higher education when pursuing a career in CS. Read our highlights from the session below:
Not necessarily! However, I have to say that holding a computer science degree is becoming a requirement. I would also go one step further and claim that the tech titans such as Google, Facebook, and Amazon prefer to hire individuals who hold a graduate degree in computer science.
Some areas where I think individuals who don’t hold the formal CS degree might struggle with are data science and AI. The reason is that such areas require that the individual masters several sub-areas, which are not easy to learn on your own without an advanced degree. For example, a data scientist needs to:
(1) be proficient in one or more high level programming languages like C/C++ and Java as well as major scripting languages such as Python and R.
(2) be able to manage, maintain, and retrieve data stored in large-scale data databases. Hence, learning SQL, as the most widely-used query language, is also becoming a must.
(3) be able to test various hypotheses on data. She cannot do that without a solid understanding of statistical terminologies such as Mean, Standard Deviation, P-value, and the Null Hypothesis…
(4) be able to build a machine learning model on the input data to predict new unseen patterns. Some of these skills can be acquired through online courses. However, I recommend that you enroll in online courses that are part of a planned degree that is offered by an R1 research institute (e.g., MIT, UIUC, ASU). For instance, the Master of Computer Science offered by ASU both on campus and online via Coursera has a big data systems concentration, in which student learn subjects like SQL, distributed and parallel data processing, statistical machine learning, and data visualization.
Jobs in technology evolve at a staggering rate. The software engineering process companies followed ten years ago is quite different to what software companies use these days. The required skills have also radically changed. For instance, in the past, a programmer who did not know C/C++ would struggle to find a job in a major tech company. Currently, you can be hired as a programmer in a software company to just develop scripts using languages like Python, Matlab or R (statistical Language).
Job titles like Data Scientist and Big Data Engineer did not exist ten years ago. Recently, I have witnessed even job postings for Machine Learning Engineers and AI Engineers. In other words, tech companies these days hire individuals who possess more than just programming skills. A data scientist, besides being a good programmer, needs to have good knowledge of statistics and machine learning algorithms. In the future, I would not be surprised if we had job titles like Autonomous Car App Developer, Blockchain Developer, Smart City App Developer… That is why I would emphasize the importance of continuing to learn cutting edge technologies so that you can keep up with the pace of change. For instance, The Master of Computer Science offered by ASU both on campus and online via Coursera offer data science, artificial intelligence, and big data courses that keep up with such cutting edge technologies.
There are a plethora of computer science courses offered online either on Coursera, Youtube, or other online learning tools. However, I recommend that you enroll in online courses that are part of a planned degree that is offered by an R1 research institute.
For instance, I would recommend the Arizona State University Online Master in Computer Science degree, which is offered through Coursera. When you enroll in a formal degree program, you typically have a more well-designed curriculum, which can make it significantly easier to both absorb and fully understand the material. In addition to that, you’ll complete your coursework with an MCS under your belt; that credential definitely helps with the job hunting process.
Software engineering and programming jobs represent the typical career path for a computer science major. Nonetheless, careers in computer science evolve at a staggering rate. Job titles like Data Scientist and Big Data Engineer did not exist ten years ago. I can still see growth in data science jobs in the upcoming decade. To be prepared for such jobs, a data scientist needs to master four main skills (consider the pie chart below):
Recently, I have witnessed even job postings for Machine Learning Engineers and AI Engineers. In the future, I would not be surprised if we had job titles like Autonomous Car App Developer, Blockchain Engineer, Smart City App Developer…That is why I would emphasize the importance of continuing to learn cutting edge technologies so that you can keep up with the pace of change. Mastering all such skills cannot be easily achieved without academic guidance. Hence, pursuing a Master’s degree in data science / big data as part of a prestigious computer science program (such as the one at MIT, ASU, and Stanford) may make the learning journey easier.
Apply for your Master of Computer Science from Arizona State University or start earning credit toward your degree with one of the courses below:
- Core Database Concepts
- Distributed Database Systems
- Introduction to Data Exploration and Visualization
- Multivariate and Geographical Data Analysis
The post Navigating Technology Trends with a Master of Computer Science appeared first on Coursera Blog.
Selling is a critical skill that will accelerate your success at work and in life and is not reserved solely for salespeople. We had the opportunity to speak with sales expert Craig Wortmann, the instructor of the new Specialization, The Art of Sales: Mastering the Selling Process Specialization and clinical professor at the Kellogg School of Management. Craig has been a salesperson and entrepreneur for more than 20 years and is founder and CEO of Sales Engine Inc. Read on to find out why everyone, no matter their field or age, should “Master the Art of Sales”:
Who should take this Specialization?
If you’re an entrepreneur, a career changer, or a professional salesperson like I used to be, this Specialization will give you the tools you need to get tactical use of selling.
I know some people shy away from the word “selling” and that’s okay, call it whatever you’d like. You’re still trying to influence an outcome – whether it’s selling a product, or doing well in a job interview, honing these skills will benefit anyone and everyone.
Let’s say you’re part of a company and you would never identify yourself with the word sales. You’re still going into meetings and presenting ideas and strategies to a group of people. Presenting ideas and public speaking is a form of selling, we just call it presentation or communication skills. If you’re a mom or a dad, if you’re getting your first job or your 15th job, if you’re in a company large or small, all of these circumstances require you to be good at selling yourself and your ideas.
Do you have any tips for someone who is shy or introverted?
Introverts are incredibly powerful salespeople. It’s often thought that extroverts are champion salespeople and that’s definitely true, but introverts are just as capable. The question for introverts is: What are your inherent strengths? Introverts are good at asking questions and listening. Guess what the two necessities for high performing sales are? Asking questions and listening! To all the introverts in the world: double down on your strengths and try to identify your weak points so you can work on them in this course.
What are the characteristics of a high performer in sales?
Discipline. Incredibly disciplined. When you give a presentation you’re precise, articulate, and your point is crystal clear. High performers have the knowledge, skill, and discipline to show up, stand out and break through in a room. That’s the definition of a high performer.
What do you hope learners will walk away with after taking this course?
After taking this course, I hope learners walk away with confidence and credibility. I hope they walk away making better choices about how to show up, standout, and breakthrough because that’s really what the course is about. I want people to think “you know what? I’m going to approach this differently than I did before. I have the knowledge, skill, and discipline and I’m going to be better, say things differently.” I think learning how to be a high performer in sales really changes people’s lives and outcomes.
Enroll in The Art of Sales: Mastering the Selling Process Specialization today.
SMBs are the backbone of any economy; in America, they account for 99.7% of all US businesses. However, the impact of technology and shift to digital are changing the way these businesses are run. Every company needs to continuously reskill and adapt their workforce to stay competitive, but SMBs often lack the massive budget and organizational resources to address such needs. In fact, according to Bank of America’s 2015 Small Business Owner Report, small business owners’ #1 priority for using loan capital is training and developing existing staff due to difficulty in locating new, qualified candidates.
At Coursera, we’re excited by our mission to transform lives through learning, and we recognize that making our platform available to employers is key to expanding our reach and impact. This is why we launched Coursera for Business in August 2016: to give major employers across the world access to a scalable platform that delivers world-class content and an exceptional learning experience. We launched with large enterprises in mind – those who need to upskill and retrain their globally-distributed workforces – but soon realized this challenge is one that all companies are facing, regardless of their size.
Today, we’re excited to announce that we’re extending the reach of Coursera for Business to provide small and medium-sized organizations and teams with the fast, flexible, self-serve deep skilling options they need to keep their workforce competitive – at a price that makes sense.
Through our new offering, smaller companies like LWCC, Louisiana-based workers’ compensation carrier, and Finland-based Angry Birds franchise creator, Rovio, are able to upskill their employees in everything from data science and machine learning to leadership and writing, with access to our full catalog of 2,600+ courses from the world’s best universities.
Similar to our enterprise customers, these small businesses will now be able to:
- Curate several learning programs for different groups of employees according to their company’s specific needs. Whether the team is interested in learning Google Cloud content, or data science from top universities or simply wants to develop advanced Excel skills, Admins can now easily curate programs for individuals and teams.
- Track their employees’ progress with an easy-to-use dashboard. They can send reminders to learners to join a program, enroll in courses and a nudge to complete assignments – all from the platform.
- Easily purchase what they need via credit card or PayPal, and add new learners to their ongoing programs.
- Enable employees to earn university credentials that they can proudly showcase
- Learn on their own schedule, with Coursera’s robust mobile platform.
All these can be done by business owners and team leads themselves, making it simple for companies who do not have dedicated resources to manage the platform with ease.
With this new offering, Coursera for Business will help smaller organizations retrain their workforces and effectively prepare their businesses for the future. Coursera for Business has become a critical partner for large enterprise and small businesses alike as they execute on their workforce transformation goals.
You can learn more about Coursera for Business for enterprises and SMBs, and inquire about using Coursera for Business at your organization, on this page.
The post Coursera for Business Is Now Available to Small and Medium-Sized Businesses appeared first on Coursera Blog.
The iMSA is ranked in the top three accounting programs in the US and is taught by some of the best faculty in the world. Through the iMSA, you’ll get hands-on practical experience and learn cutting-edge analytics that will help you become a standout candidate to potential employers.
In a recent webinar titled, “Staying ahead of the curve: How analytics and technology are changing the accounting profession,” Brooke Elliott, the head of accountancy at the University of Illinois at Urbana-Champaign, talks about why the iMSA is at the forefront of analytics in accounting. Read our highlights and watch the full webinar below:
The new accountant will be a data-driven decision maker – 15:19
Companies want access to data because data-driven companies make better decisions. Highly data-driven decisions-makers can look back when needed and use predictive and prescriptive analysis to model the future. And demand for analytics talent is growing.
Data-savvy accountants are wanted by employers – 5:11
Accountants of the future are expected to have data science and analytical skills, and the iMSA ensures that students are well prepared.
Technology is disrupting traditional accounting – 16:02
From Ernst and Young’s April 2018 announcement about blockchain audit technology to leaders from Deloitte and Capital One who visited Illinois as part of the Lyceum series– all of the major accounting and audit firms are responding to rapid changes impacting the accounting industry as a result of technology.
Learning to develop an analytical mindset – 18:44
You’ll learn to ask the right questions, extract, transform, and load relevant data. You’ll apply appropriate data analytics and interpret and share results in a digestible way. Unlike other programs, analytics are a main focus in the iMSA and all courses help you develop your data evaluation techniques.
Sign up for the Data Analytics Foundations for Accountancy I course from the iMSA and get started today. You’ll learn Python, statistical data analysis, and the basics of data visualization. And if you are accepted into the program, you’ll get credit for the course.
Learn more and apply for the Master of Science in Accountancy.
Machine Learning (ML) is one of the fastest growing fields today. Financial Institutions continue to implement ML solutions to understand how markets work, access data, and forecast trends. In the new Machine Learning and Reinforcement Learning Finance Specialization from New York University, you’ll learn the algorithms and tools needed to predict financial markets and how to use ML to solve problems.
Whether you’re a data scientist who wants to break into the finance industry or someone who currently works in finance, this Specialization will help you gain the knowledge and skills you need to develop a strong foundation. Instructor Igor Halperin, who has worked at both JP Morgan Chase and Bloomberg LP, gives us a sneak peek into what you’ll learn below:
How did you become interested in machine learning and finance?
I started in theoretical physics and eventually, that led me to my first job in finance as a quantitative researcher focused on models of option pricing and credit risk modeling. I started using various methods of non-parametric statistics for my models, and then slowly migrated into machine learning methods. I self-educated which is why I wanted to help create this Specialization because I know what it’s like to learn on your own.
Who should take this Specialization?
This course is designed for students of finance or those who already work in finance who want to further their career by growing their skillset. Before taking this Specialization, learners should have a decent understanding of basic math including calculus and linear algebra, basic probability theory and statistics, and some programming skills in Python. People who already know Machine Learning and/or Reinforcement Learning and want to learn about their applications in finance may also benefit from taking this Specialization.
Could this Specialization also benefit a data scientist who wants to work in finance?
Absolutely. Data scientists use many tools from Machine Learning, they just need to learn about Finance in order to understand how to use them in this domain.
Why is it important for someone working in finance to understand machine learning?
Machine Learning gives you more tools in comparison to traditional financial models. When you know both, your abilities quadruple.
How would this specialization give learners a leg up in their careers?
Machine Learning in finance is booming. This Specialization will help people learn on their own and allow them to use their new skills the next day, on the job. It will help those seeking quantitative roles prepare for the next chapter of their career.
Quantitative finance means you’re doing something directly related to machine learning. You’re either using ML as a tool for a specific problem or developing new financial models.
Enroll in the Machine Learning and Reinforcement Learning in Finance Specialization from NYU today.
May was a great month for new content on Coursera for Business with 51 new courses and 7 specializations. Here are the top 4 we’re most excited about:
- Blockchain (SUNY) – Gain the foundational knowledge to do more than trade and mine cryptocurrencies. The Blockchain specialization will teach you to create new nodes on a blockchain, design your own smart contracts, and develop distributed applications.
- Machine Learning and Reinforcement Learning in Finance (NYU) – A first on Coursera for industry-focused applications of machine learning, this Specialization aims to re-skill and up-skill workers in the rapidly changing finance industry.
- Applied Data Science (IBM) – This Specialization works towards a Certificate in Applied Data Science from IBM with fundamental programming tools using Python.
- The Art of Sales: Mastering the Selling Process (Northwestern) – We’re happy to announce the relaunch of our popular Sales specialization through Northwestern Kellogg School of Management as an upgrade from the popular and well-rated Sales course from the University of Chicago.
Here are the rest of the new courses and specializations:
- 10,000 Women: Assessing Your Business Growth Potential, Goldman Sachs
- Accounting for Decision Making: Fast Track Finance, University of Michigan
- Design strategy: Design thinking for business strategy and entrepreneurship, The University of Sydney
- Anticipating Your Next Battle, in Business and Beyond, HEC Paris
- Organizing for Innovation, HEC Paris
- How to Get Skilled: Introduction to Individual Skills Management (Project-Centered Course), The State University of New York
- New Product Development – develop your own new product, Technion – Israel Institute of Technology
- Understanding Modern Finance, American Institute of Business and Economics
Computer Science Courses
- Mobile VR App Development with Unity, Unity
- Solving Algorithms for Discrete Optimization, The Chinese University of Hong Kong and The University of Melbourne
- 用 Python 做商管程式設計（三）, National Taiwan University
Computer Science Specializations
- Modern Robotics: Mechanics, Planning, and Control, Northwestern University
- Unity Expert Gameplay Programmer Certification Preparation, Unity
- Программирование на Python, Moscow Institute of Physics and Technology
Data Science Courses
Data Science Specializations
- Advanced Machine Learning, National Research University Higher School of Economics
- Data Systems, Arizona State University
- Introduction to Applied Data Science, IBM
- Machine Learning with TensorFlow on Google Cloud Platform, Google Cloud
- Spanish for Successful Communication in Healthcare Settings, Rice University
Personal Development Courses
- Career Decisions: From Insight to Impact, Wesleyan University
- Sleep Deprivation: Habits, Solutions, and Strategies, University of Michigan
Blockchain is a revolutionary new technology and demand for blockchain talent is off the charts. In this groundbreaking new Blockchain Specialization taught by blockchain expert Dr. Bina Ramamurthy from the University at Buffalo, you can learn the basics and build foundational knowledge that will help you innovate with the next frontier in technology. We’re excited to introduce you to Dr. Ramamurthy as she shares what you can learn in this Specialization.
What motivated you to create this Specialization?
This Specialization of four courses is about the emerging technology blockchain. The blockchain ecosystem is advancing and expanding at an unprecedented pace. We need to educate people at all levels – developers, researchers, users and decision makers – to enable them to innovate and be informed users of the technology. The Blockchain Thinklab at the University at Buffalo wanted to educate developers and quickly ramp up their skills to program using a blockchain technology.
What is blockchain?
Blockchain is a technology like the Internet. Similar to how the Internet gave rise to applications such as World Wide Web (web browsers, servers) and email, blockchain is a protocol that enables a trust layer. This trust layer allows for peer-to-peer transactions among people who are beyond the boundaries of trust. The basic tenets of a blockchain that makes this trust layer possible are:
- a distributed ledger technology (DLT)
- a decentralized system where you/participant hold your assets, and
- disintermediation by validation and verification by the blockchain protocol – DDD
Who should be taking this Specialization, but maybe isn’t?
We encourage anybody curious about the blockchain technology to join these courses, even if they are not developers. You never know, this may be the technology that takes you from a casual observer to an involved developer of the technology! We are excited to share our knowledge to create a robust advancement of the field.
What can people expect to learn from this Specialization?
People can learn the basics of blockchain starting from its genesis in Bitcoin cryptocurrency. They will also learn about the robust foundation of the blockchain technology that has arisen from the research in cryptography and secure hashing. They will learn about Ethereum blockchain and about the development of smart contracts for solving problems on the blockchain. They will learn about the development of decentralized applications (Dapp) on Ethereum test blockchain.
Learners will be able to write applications that require automatic validation and recording of transactions on the distributed ledger of the blockchain; and also applications that require provenance and governance including rules, regulations and policies. For example, in a recent blockchain hackathon in Buffalo, students from our university completed Dapps for efficient third-party verification for a financial institution, supply chain management applications from diverse domains like food and chemical industries, and a patient-centered healthcare application that lets a patient control their health data assets.
Innovate with the next frontier of technology in the new Blockchain Specialization.
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