Overview of some MOOC Coursera in Computer Science

Most likely, if you went to Habr and read this article, then at least once in your life you heard about MOOC courses.

But if you still didn’t hear it, then MOOC (it is customary to say “torment” in Russian) means “Massive Open Online Course” - a massive open online course. This is a real phenomenon in the education of the 21st century. The New York Times newspaper even called 2012 “the year of MOOC” in connection with the appearance of 3 “whales” in the distance education market - Coursera, Udacity and EdX. A lot of articles are devoted to MOOCs, someone sees in them the future of education , someone, on the contrary, is a threat . They are also trying to predict the “traditional” and “distance” components of future education.

However, in this article I will not discuss the prospects for the development of distance education, but I will talk about my experience of acquaintance with courses on the Coursera platform . These courses will be useful to students studying applied mathematics and computer science, especially data analysis. Much of what these courses gave me, as I later realized, is the knowledge that any self-respecting data researcher should have (so I prefer to translate the profession of Data Scientist).

I give not only a description of the course, but also approximate labor costs and a subjective assessment of complexity on a 10-point scale.

  1. Machine Learning (Machine Learning, Stanford University). 4-5 h / wk Difficulty: 7.
    Just a brilliant course, sample. For 10 weeks, participants are developing their own programs for spam filtering, image compression, handwriting recognition and movie recommendations. The working language is Octave, essentially the same Matlab, only free.

  2. Data analysis (Data Analysis, Johns Hopkins University)6-7 h / w Difficulty: 8.
    In this course, peer-reviewed home projects are implemented. You write not only code for solving the problem, but also a full-fledged article. Participants like you are blindly evaluating. I think this is useful for developing skills in expressing thoughts in English and intelligible presentation of research results. A very interesting project to predict human behavior (sitting, lying, running, etc.) based on the readings of the accelerometer and gyroscope in a mobile phone.
    Now, in addition to this intensive course, Johns Hopkins University also offers a specialization of 9 courses in data analysis. Working language - R.
  3. Statistics (Statistics One, Princeton). 3-4 h / wk Difficulty: 5.
    One of the most popular courses on Coursera. Everything is explained very accessible. Cons - certificates are not issued (although, of course, knowledge is more important), there are a lot of glitches in the test verification system. Working language - R.
  4. Algorithms: Design and Analysis , part 1 (Algorithms: Design and Analysis, part 1, Stanford). 8-10 h / w Difficulty: 9.
    Must-Know. Algorithms for sorting, analysis of graph structures, complexity of algorithms, paradigms in creating algorithms, and much more are considered. The most difficult tasks of all the courses that I took. Among other things, it was necessary to program in any language the algorithms of the minimum section in the graph and the search for strongly connected components, as well as Dijkstra's algorithm for finding the shortest path in a weighted graph. Any working language.

  5. Analysis of social networks (Social Network Analysis, University of Michigan). 3-4 h / wk Difficulty: 5 (if without additional tasks).
    We consider the basic properties of social networks, their types, as well as tasks in which social networks naturally arise, for example, predicting the spread of infection or diffusion. An unexpected example is a network of food ingredients that helps find clusters of similar recipes and interchangeable foods. Demonstrates social network analysis tools Gephi and Netlogo.

  6. Introduction to the R language for data analysis (Computing for Data Analysis, Johns Hopkins University). 3-4 h / wk Difficulty: 3.
    A very simple course, cannot be considered as independent. Rather, the introduction to the course "Data Analysis", and then for people without programming skills (if they even read Habr).
  7. Introduction to Python (Rice University) 1-2 hours / week. Difficulty: 3.
    An entertaining course for people without programming skills. Probably, the credo of its creators is "Every self-respecting programmer should play his own arkanoid." The course makes sense to go with a 12-year-old son. The basics of the Python language are given, during the course you need to program such games as Guess the Number, Pong, Memory, 21, and finally, Asteroids.

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