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"In data science, two of the best specializations taught by the Coursera platform are from The JHU and IBM. There are many comparative assessments to choose which would be the best choice according to the profile of the postulant. It's probably worth doing both. In any case, I ratify jhu's data science expertise, in the sense that it is quite rich, deepens very little explored topics in regular undergraduate courses, and is directed specifically to statistics and big data. It assumes that the student has a good basis of mathematics and statistics of higher level and good knowledge base in R. I say only good base, because the R is quite vast, and the specific classes of programming in R are sequenced, practically from scratch, but of well accelerated progression. In some more specific topics, I had to complement the knowledge with very objective and punctual parallel courses, such as the themes of the DataCamp platform, which serves well to unlock some punctual subject. StackOverFlow's help and feedback alsocomes as a great help at all levels of learning, including professor Roger Peng's first lesson: knowing how and where to seek help to move forward, that is, the first major lesson in data science is the humility of being an astronaut in a virtually infinite universe,and expanding every day. This is the most fascinating of Data Science, Biostatistics and R: the themes never run out and become concatenated in the face of the phenomena that surround us daily. From there, the sequence of 10 courses represents a long way (not so long for some) of development, feedbacks, evaluations and model building in R. Undoubtedly an excellent specialization, which is worth the investment, especially time. The final part of the specialization represents the last steps, but the steepest of the journey. In the latter, in particular, metaphorically you are confronted with yourself, a feeling of having been blindfolded in the middle of a dense dark forest, and now need to find your way back using what you have learned so far. For all specialization graduates it is a stage of relief, rather than celebration. The percentage of evasion of specialization from the first course is very large. In a master's and doctorate, I believe it is a specialization of great support, for the publication of studies and analysis of field data, in order to reach assertive conclusions from hypothesis tests. Upon completion of the specialization, JHU encourages the publication of the Certificate of Completion on LinkedIn and, with this, you receive an invitation from Professor Brian Caffo, after a brief verification of authenticity (around 1 week), to join a private Data Science group moderated by him, where there are excellent networking opportunities with other scientists,partnerships, job opportunities and project development. &#xa0;\n"
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