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Hamideh Iraj

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  • 70 reviews
  • 60 completed
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A very bad course. Course contents are good but almost all of the instructors speak annoying English. Even watching the courses and following the instructors seems impossible.
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This course is a high-level introduction to linear and logistic regression modeling using SAS and python. You will not become an expert in any of the algorithms but you will get a general idea of what are the inputs and outputs of regression models. If you are following the specialization, the course will complement your skills. In addition this series has some interesting features which I mentioned in my review of "Data Management and Visualization" course.
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This was a concise course about machine learning that covered Decision Trees, Random Forests, Lasso Regression and K-means Cluster Analysis in SAS or Python. Off course, this course is not enough for learning machine learning. However, you will get a general idea of some supervised and unsupervised methods in a project-based course. For learning about my idea about the specialization in general, see my review for Data Management and Visualization course.
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I took this course to practice data analysis in python. This is a concise and nice course that covers ANOVA, chi-squared and correlation tests in SAS or Python. For learning about my idea about the specialization in general, see my review for Data Management and Visualization course.
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I audited this course as a beginner in algorithms. I never studied algorithms in university. I watched the videos and did the homework (quiz) for this course and skipped projects and applications because of my time limitation. I really enjoyed the lectures taught by Dr. Nakhleh. He has a special talent in describing concepts in a simple way. Homework was also good, a little bit challenging for me but a good learning experience. Watching this course gave me the motivation to spend more time to learn algorithms. I wish Dr. Nakhleh could teach more MOOCs on Coursera.
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I was very happy when I heard about the course because it was my first MOOC in text-mining. The course contents are too vague and the instructor is not suitable for teaching. His voice is monotonous and boring and he is very difficult to follow. The course lacks context and appetite wetting and you don't know what are you going to do with these algorithms. You as a student cannot see the big picture.The programming assignment was fun but did not help learning the course contents. I will complete the course just because the topic of this course is very important and it is difficult to learn this topic from books, either. A video-based MOOC is anyhow better than studying this subject from books.
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This course is my first MOOC in machine learning that goes beyond introductory level and explores clustering algorithms in 4 weeks. This is generally a low-quality course. The syllabus is interesting technically but spiritless due to the lack of context and use cases to give a meaning to algorithms. The lectures are deadly boring. The instructor obviously lacks teaching and communication skills and most of the time he is reading from the slides. This course helps you to know only the titles of available clustering algorithms . You will not learn any of them. It only warms you up to study the subjects later. I am taking this course just to get an idea about clustering algorithms and self-study in demand. It is worth spending about two hours a week for watching each week videos and doing the quizzes in one our or so. This is the first run of the course and it has many ways to improve.
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A very good introduction to Linux command line. If you are a beginner, do not hesitate to take this course.
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This course is good in general but it has some drawbacks: 1- If this is your first exposure to Linux, this course may not be a good choice. You will have many questions unanswered. 2- This course lacks a story line that you can easily follow. If you know the basics, that might not be the case but if you are completely new to Linux, it may be bothering. I used this course after taking a 24 hour physical class and it was OK. Another point is that I hate edx platform. I cannot work easily with that. Batch downloading of course material was not available and I spent a lot of time downloading videos from YouTube channel. Also keeping course pages took too much time. The exam was almost like a joke. I could get 29/30 before studying the course just by searching the answers on the internet.
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Up to now, It is the second best course of data specialization series (after developing data products). I am somehow accustomed to the ways these 3 professors teach and found a way to learn. In comparison to data analysis course, it is completely redesigned, very much improved and many tips are added to the course and the use of only one package (caret) brings the tool usage into order. Remember that it is a 4 week course and you cannot expect to learn the wide variety of concepts of Machine Learning and it cannot replace machine Learning by Andrew Ng which is far better in concepts. My recommendation is taking (or auditing) the Andrew Ng's course (you have to work with MATLAB or Octave which I did not like) and this course as a complementary to learn how to work with some R packages and how to map ML concept to R programming language. Probably a good idea is to extend this course to two or three four-week courses on the series. students expect to learn more about the core topic of the series.
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I passed computing for data analysis 6 months ago. At the time it was a difficult course for me. but I passed it anyway .The programming assignments did not match the course content and I was somehow wrestling with the code! Now I am taking R Programming. I am doing all the quizzes and exercises. I am still learning and removing the bad experience in my mind as well. In comparison to "computing for data analysis" it is an improved version. Some difficult parts has been removed and some parts added. It is more integrated now. I recommend every beginner to take it after 1-The Data Scientist’s Toolbox and 2-Getting and Cleaning Data so that you have some experience with R before the course. And off course the more you experience R, the easier the course would be.
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This course is about new ways of thinking about business. I took it because it improves general understanding of business; what is never thought in schools and colleges It is not that much suitable for MOOC because it should be based on face to face discussion which is narrowed down to forums and I 'm not motivated enough to participate!!! Anyhow I think it is worth watching. It takes less than an hour per week and quizzes are easy. I 'm taking it somehow for fun. just listening to speeches arouses my mind to think.
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I really love this course. I am familiar with programming and this course is an opportunity for me to learn python. It is really well-designed: max of 7 videos per week, short and easy to understand videos ,suitable for an introductory course. Exercises and assignments are all you need to practice to learn any week contents. Only those who want to learn programming from the very beginning should check to see whether they are easy with this or need a more elaborated course.
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I generally agree with Greg. It is a very light weight course : an absolute beginner guide to R .Having passed "computing for data analysis" and "data analysis" on coursera , I already knew almost all the material and completed all in less than two or three hours. I think the mission of this course is to assimilate students who want to take other courses such as R Programming. Before the data science specialization announcement, computing for data analysis (Now R Programming) was the course for beginners and many were complaining that it was not easy. I think this course plus Getting and Cleaning Data acts as a warm-up to make R Programming easier. To wrap up, Although being easy I consider the course necessary for the entire specialization program. But it does not worth 50 dollars.
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I audited this course. I mean I just watched the videos and did not get into quizzes or assignments. The course was not bad as general knowledge but I am wondering it is a real course with explicit learning objectives.
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I audited this course. It was informative and interesting for me as a non-american. It gave me insights not only about poverty, but also about social science studies. The course was enriched with both stories and statistics. Recommended.
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I audited this course. The course introduces ways of making yourself happier. You may know many of the titles: gratitude, compassion,...beforehand. However, while watching the videos you can focus on your own life to see what you have done to make yourself happier. It is generally a good course. Recommended. Even watching the videos makes you happier because you are trying to implement the ideas in your life subconsciously. The instructors are nice and kind and they really match what they are teaching. I don't give it a 5 because it is a little bit naive. The course does not raise serious challenges of being happy.
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This is an introductory course of average quality. Somehow boring but not too bad. The quizzes are relevant to the course and let you get your hands dirty in bayesian statistics.
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I watched the video parts of course. The course was eye-opening. As a beginner,I learned a lot about women health and human rights issues. Highly Recommended.
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I audited this course after passing DASI (Data Analysis and Statistical Inference) on Coursera. It was not good at all. Dr. Mine Çetinkaya-Rundel is a good speaker but I couldn't follow the other instructors.
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I audited this course. It is a Super Course and a role model. Very innovative and unorthodox. No lecture, No PowerPoint. You will learn through a series of conversations in the beautiful university and fabulous interviews with thought leaders about interesting topics.I learned and enjoyed a lot. Highly Recommended!
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I did not like the course for two reasons 1- The introduction of descriptive,predictive and prescriptive analytics was mixed with marketing so the amount of time that could be dedicated to marketing was dedicated to introducing analytics. 2- This course was more general knowledge and E-Commerce rather than marketing. I wanted the course to be more specific: How marketing theories help in building predictive models and What a marketing analyst knows that other data analysts don't know. If you are an absolute beginner in analytics, give it a try. Otherwise it may be too simple for you.
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I audited the course. This is a high-level non-technical course. You will get a general idea about topics in people analytics. For example, you will learn about what to consider when evaluating people, how to assess a measure, and also you will learn that we predict how well the job applicants will perform and when they quit. Recommended.
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This was a short nice course: 30 minutes of lecture each week with short quizzes and assignments. The professor is cool and explains the contents simply and intuitively. The course has a free and open textbook. In addition, the professor puts some extra material such as interview which you can enjoy. Recommended for warm-up.
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This was a short nice course: 60 minutes of lecture each week with short quizzes and assignments. The professor is cool and explains the contents simply and intuitively. The course has a free and open textbook. In addition, the professor puts some extra material such as interview which you can enjoy. Recommended for warm-up.
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This was a short nice course: 30 minutes of lecture each week with short quizzes and assignments. The professor is cool and explains the contents simply and intuitively. The course has a free and open textbook. In addition, the professor puts some extra material such as interview which you can enjoy. Recommended for warm-up.
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I audited this course. As my first course in writing, It was was very nice and informative. In my country, such a course does not exist in universities. I had a manuscript in hand and did the editing step by step with the course. I wish I had taken this course earlier. The course has two drawbacks: First, It was a little bit long and Second, Dr. Sainani talks too fast. The first four weeks of the course is suitable for professional writing,too.
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A very nice course. The instructor is really dexterous in explaining the contents seamlessly and easily. To understand the course better, you can take data analysis and statistical inference by Duke university as a prerequisite.
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I audited this course. It was an average one. The contents were new and interesting for me but the course was too fragmented. A one-hour documentary could explain the contents better.
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This course is the first in Data Analysis and Interpretation Specialization on coursera. The specialization is really promising .The instructors did a great job of designing nice engaging courses.They carefully alleviated the problems of existing courses. Instead of PowerPoint, they used films to teach subjects. Instead of one voice, you hear the voice of different people. The use of Anaconda allows the student to get rid of installation and package management troubles at the very beginning. Instead of uploading your code and document, you post assignments on your blog. This minimizes the trouble of uploading and technical issues. In addition, you can show your posts to other people later. Last but not least, there are no quizzes to take up your time. You do four short assignments and it is done. In a nutshell, this is a carefully designed specialization. Highly recommended.
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This was a nice course offered by lovely professors Dr. Hastie and Dr Tibshirani. I liked the lectures. Concepts were explained simply and intuitively. The use of the two professors helped make the course a little bit more engaging. The second professor (The one who was not lecturing) contributed by raising questions and asking for explanations. However, I did not like quizzes. They were far from the course and usually tricky which lead to disappointment and frustration. (Generally I prefer project-based courses) I also enjoyed their effort for making the lectures meaningful by the telling the stories of how the technique was born, by whom and which society (statisticians or computer scientists). This was very valuable and made a lot of sense for me. Just remember that this is not an introductory course. Take it when you have a basic understanding of statistics and machine learning.
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This was a very nice and concise course about the work and life of Martin Luther King, Jr. The instructor talked about major events in Dr. King's movement and referred to some letters and documents for further study and research. Recommended for those interested.
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This course is about linear models and matrix algebra for data analysis. For example, how you can find linear model coefficients using matrices. The course contents are practical. Although watching the videos and doing assignments does not take too much time, the student should spend time thinking about course contents to understand them. In general, it is recommended. It seems to be the first course in math for data science courses.
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This course was my first exposure to logic and I am very satisfied that I completed the course. Although the course was very long (twice a six-week MOOC) and sometimes boring, it was worth taking for a beginner like me. The sense of humor of Dr. Armstrong and loose timing of deadlines helped make up for its weak points. Watching the first three weeks of the course are highly recommended for GRE general exam. It gives your mind the structure and terminology to talk about arguments.
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It was a very interesting course for me. I have never been to Louvre nor have been familiar with European art. It gave me Louvre literacy! I mean I had a journey to the Louvre visiting a couple of paintings in the course and the instructor explanations helped me get a general idea how the Louvre was built and how France gained importance in European art as the Louvre was developing.
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I audited this course as a beginner in algorithms. I never studied algorithms in university. I watched the videos and did the homework (quiz) for this course and skipped projects and applications because of my time limitation. I really enjoyed the lectures taught by Dr. Nakhleh. He has a special talent in describing concepts in a simple way. Homework was also good, a little bit challenging for me but a good learning experience. Watching this course gave me the motivation to spend more time to learn algorithms. I wish Dr. Nakhleh could teach more MOOCs on Coursera.
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I audited this course. It was about Systematic Review and Meta-Analysis in public health. Although I have no expertise in public health , the course provided useful insights. The contents were quite good, comprehensive and elaborated but were not presented well. It was boring with too much repetition of concepts. It is a MOOC and people can always refer to materials later so there is no need for too much repetition.The course instructors can improve the course by better presenting the materials.
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It was an introductory course on data visualization.The professor focused on concepts of visualization rather than using tools. It was generally a nice course, well designed and well structured. I got some ideas for visualizing my research dataset. The only drawback was that it was too theoretic. Adding more challenge and fun will make the course better.
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It was a fabulous, informative and engaging course. Before taking the course, I did know absolutely nothing about politics in USA. I learned about presidency in the United States and much more. Highly recommended.
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I audited this course to get some high quality resources for my academic paper on implementing flipped classroom. The instructors collected a lot of useful information for guiding educators turning a traditional class into a flipped class. I used the material in writing my paper.
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I audited the course. Downloaded all the videos and watched them at my own pace. I think the course topic is a very important one, not only for US, but also for any country. If you are living and working in the US, you should learn about the environment surrounding you. I enjoyed the course and learned a lot, although I am not living in the US. I think the course name is rather vague. It can be improved to represent the contents in a better way and attract more students.
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This is a very good conceptual course about logistic regression with videos not exceeding two hours per week. The instructor has mastery over the subject and he explains the details of the algorithm. This course teaches stata which is the reason I am not doing the assignments because I am not going to spend time learning a new tool. I wish the course was teaching R. In that case it would be a five star one. If you are practicing epidemiology , it is a must study course for you.
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I am auditing this course now. It is a really nice and pleasant tour through the history of the US. For me as an outsider, it is learning about the US, american people, their struggles and victories by stories and getting general knowledge.
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I first learned about this course in Johns Hopkins Data Science Specialization courses. Students talked about this course in a very positive manner. I became very curious about this course and I decided to take it. I am really satisfied with what I have learned and I feel very good about statistics now. It completely removed my bad memories of statistics. This course was nicely organized with very flexible schedule (no need for late days), phenomenal use of real world examples and beautiful slides (Sometimes I could not take my eyes off the slides). The exercises were carefully designed and I could learn simply by watching videos and doing exercises. (I did nothing special) If you are new to statistics, do not lose this course.
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I took a look at course material to see what it is covering and I watched the videos on demand. I think this course is more concise and interesting in comparison with Big Data in Education offered by Coursera. So I recommend taking it before studying Big Data in Education.
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I was looking for a high level course on educational data mining and learning analytics when I found this course and it was exactly what I wanted. I wanted to explore the area of educational data mining and learning analytics to see how can I enter it. For learning educational data mining and learning analytics i recommend the following sequence: 1- Educational Big Data and Learning Analytics on Udemy 2- LINK5.10x: Data, Analytics and Learning on Edx 3- big Data in Education on Coursera As the sequence number goes up, the course becomes more elaborated and deep and as it goes down the course is of higher level and concise.
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I skimmed the course slides to get a general understanding of educational data mining and decide whether to pursue it or not. Course content advantages: 1- Being right to the point and informative 2- Referring to and explaining related articles (a research facilitator) Course content disadvantages: 1- Lacking a high level view of educational data mining 2- Does not include which type of questions are asked in educational data mining and learning analytics. The examples are inadequate and they are too much distributed across weeks and does not give a general understanding of educational data mining and learning analytics 3- Lack of information about what kinds of datasets are available in education and how to integrate them for research
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This is a brilliant course and I encourage every university student or professor to take it. It is about things academic people need to do to promote their brand on the internet in the age of social media. They are very nice ideas available so do not hesitate to take this 4 week course.
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This was a nice high level introduction to web scraping. The whole course took only half an hour. If you are a beginner, this will help you get a general understanding of web scraping.
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I audited the course and watched almost all the videos. This is not a real course. Just an ad for the book. It is a shame for MIT university to present this sort of material. However, the idea of this course is interesting and worth watching.
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I read the reviews before starting the course and just did the exercises and course project with my prior knowledge. I think this course and statistical inference need to be rewritten. They are the weakest in the series.
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This course is on the acceptable borderline. The quizzes were quite easy, not challenging. The same for the two course projects. The drawbacks are: 1\. The course does not evaluate your understanding of weeks 3 and 4. No motivation to watch them carefully. Only weeks 1 and 2 have quizzes. 2\. Doing two course projects is irritating. You have to do the main project and peer assessment in four weeks. So you have to keep an eye on deadlines for four weeks. To wrap up, I preferred the course with full quiz coverage, more practice on the three plotting systems and one course project.
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It was probably the best course on data specialization series. It is a tool- oriented demo like course about ways to create data products including R packages, shiny,R Studio Presenter,slidify and some other tools.  you can get a general overview of the products and learn those you need. For the course project you should do a shiny project and Rstudio Presenter or slidify on your choice. I do not agree with Richard in the idea that it is easy to learn them from web. It takes too much time and effort to select and gather  these stuff from the web and I appreciate the effort done by the instructors. It is not brilliant but I learned and enjoyed.
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I audited the course. It was exciting and inspiring. The idea of TED was not new to me .I had already downloaded some TED talks and enjoyed.  However, It was a pleasant return to it. The course had a nice idea: to integrate TED into classrooms. I really fell in love with the course idea in the very beginning. In the course contents, there were some useful references to popular TED talks.
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I audited the course for less than two hours to see if there is anything new to me. The course talks about the very basics of reading- writing- listening and speaking skills that play a role in success in universities. Also understanding the topic, searching and other skills are covered. I think this is suitable for first or second year undergrads who know very little about studying at university. Other students in most cases have learned these things by experience. In my opinion the skills are general and anyone can learn them wherever he/she is studying whether in an English speaking university or not ,whether he/she speaks English as their first language or not.
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This course had nothing new to me. I did not watch any video and just did the quizzes and assignments. I passed data analysis from John Hopkins University before and I knew almost all the course material. I love coursera courses mainly because of challenging assignments and learning through that . but this course had not this characteristic.
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This was obviously a weak course in data science specialization. I am not going to repeat prior posts comments. However, the professors are going to improve the course. I hope it will reach an acceptable level. I completed the first two weeks quizzes just with my knowledge and course slides and not watching videos. For the third and fourth quizzes I used slides only .I am expecting to get 75 out of 100 but frankly saying I did not learn anything. Just passed it. I can spend my time on Duke's Data Analysis and Statistical Inference which was highly recommended on coursera forums. If you are going to complete data science specialization track , you will have another alternative from John Hopkins University. Go and check it out.
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Having passed "computing for data analysis" and "data analysis" both by John Hopkins university , the course was quite easy for me. It is recommended as the third course on data science specialization. However, I think it is better to pass it before R Programming. The course content seems simple but very important esp subsetting. Many of the actions on data preparation in R is done by subsetting. So generally I recommend this course.
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Generally saying these are good seminars that turned out to introduce me the world of HCI and I really loved watching them. I watched the following videos: Research Meets Web 2.0: Augmented Social Cognition Sheds Light on Coordination, Trust, Wikipedia, and Social Tagging (October 19, 2007)-very practical and informative Data Modeling and Conceptual Sketching in the Design Process at Microsoft (November 9, 2007) -not practical Google Design in Practice: the Challenge of Simplicity - good,practical- suitable for beginners Automating & Customizing the Web With Keyword Programming (May 16, 2008): twined with Koala: End User Programming on the Web (December 8, 2006)- very good
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Generally saying these are good seminars that turned out to introduce me the world of HCI and I really loved watching them. I liked the following video Koala: End User Programming on the Web
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Generally saying these are good seminars that turned out to introduce me the world of HCI and I really loved watching them. I watched the following videos: New Models for Browsing (September 26, 2008) -good Aurora: Envisioning the Future of the Web (October 17, 2009) - not practical Information Foraging Theory (October 24, 2008) - too theoritical Building Theories: People's Interaction with Computers (October 31, 2008) - not practical SearchTogether and CoSearch: New Tools for Enabling Collaborative Web Search (November 7, 2008) - good,practical
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The course title was interesting but it had nothing to say. I was expecting to watch a course about Google business models or something cool about Google but it was a mixture of aimless speech and interviews.
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I dropped the course because it was too long and had no concentration. Also it was distracting rather than integrating. Also the quizzes were terribly bad and made no sense to me.
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It is the best course I have ever taken on MOOCs. You might not believe, but I enjoy it more than watching a movie. Because Dr Ng is excellent in his job. speaks very fluently, explains very simply so that you can completely make sense of the material and you have a good feeling inside. Programming assignments are really hard but if you can code everything from scratch in MATLAB or octave which is the goal of this course, you can fully understand what is going on, not being dependent on software programs to do it for you. I recomment it to everyone interested in machine learning. Even if you are not doing assignments, watch videos, learn and enjoy.
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It was a course with good syllabus but not well taught. The instructor was explaining too hard, unable to make it easier and also too fast so that he himself could not keep up. Only a phd can fully understand what he says. only MAP REDUCE introduction was good. Generally this course is wide not deep and I think it is not worth the time you dedicate on the whole course. you may download and use it by subject. It might help in this way.
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It was my first experience on Coursera and it was quite good.I audited the course. The instructor uses a lot of ways to teach: storytelling, playing, videos and so on. It is not a comprehensive course but a good start. The instructor speaks slowly so anyone with any level of english can learn from this course..
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A long boring course with 20 lectures each week. too much for a MOOC. not suitable for those in computer science . it is all about library and information science. I do not know what the instructor thinks about MOOC. I think he is wrong about it. people want smart concise courses. The instructor was explaining 3 to 4 times what is necessary to undestand some idea.
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It is a 4 week course for introducing R. It is a controversial course and it has a lot of both opponents and proponents. Just look at the coursera forum for this course. video contents are really nice elevating your knowledge but assignments are hard and far away from the taught material. it is what people often complain about. I recommend it when you have a little knowledge and skill about R and you can improve your knowledge and skill with this course otherwise it would be difficult and you may get disappointed.
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This course is about data analysis from statistics point of view. it would be ideal for students of statistics who are familiar with statistics concepts and want to review and learn R. for me, the video lectures were rather difficult. Quizzes are difficult and the two assignments were stressfull but I learned well how to write a data analysis report. for those who have time limitations I do not recommend this course. it is not optimal at all. but for those who have time it is a learning opportunity.
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I did not take the course officially. I watched some videos.but I am going to take it as soon as it will be open again. I really love this course.It was amazing and the content was stated intuitively (really making sense of data) which I really admired. The only drawback was that on the beginning weeks it was too easy and this was a little irritating. This was the course I learned R from and I am sure I will use both course contents and R over and over again.