Vital Skills for Data Science introduces students to several areas that every data scientist should be familiar with. Each of the topics is a field in itself. This specialization provides a "taste" of each of these areas which will allow the student to determine if any of these areas is something they want to explore further. In this specialization, students will learn about different applications of data science and how to apply the steps in a data science process to real life data. They will be introduced to the ethical questions every data scientist should be aware of when doing an analysis. The field of cybersecurity makes the data scientist aware of how to protect their data from loss. This specialization can be taken for academic credit as part of CU Boulder’s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder’s departments of Applied Mathematics, Computer Science, Information Science, and others. With performance-based admissions and no application process, the MS-DS is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics. Learn more about the MS-DS program at https://www.coursera.org/degrees/master-of-science-data-science-boulder.https://www.coursera.org/degrees/master-of-science-data-science-boulder. Logo image by JJ Ying on Unsplash
Exercises and lectures were hands-on and informative. I enjoyed the practical advice on data science as a profession.
The only reason to not give 5 stars is the need for an audit option where i can learn the concepts without needing to turn in assignments - i'm not looking for a grade. But the course is awesome!
Learned many aspects of Cybersecurity. Opened my mind into how to make sure not become a cyber victim.
Very Lively interaction from the mentor. Simplified explanations
Very practical overview of the field. Does require knowledge of R to do 2 simple projects if you take it for credit.
Very good course. I learned a lot from this course.
The homeworks were interesting and allowed us to learn and apply it in a creative manner
Not your basic data visualization course - very in depth and interesting with concepts that are fresh and new. Professor is very thorough and understandable.
Good course for exercise R programming, data analysis and communication skills.
I learned a lot about ethical issues and computer Science. Good lectures, good reading material, but a whole lot of writing
It is a very good and enjoyable course. I have learnt a lot of things related to cyber security.
It is very good course. Thank you very much professor.
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It looked easy, but it isn't easy for anyone who is new to R programming or programming in general. If one is taking this course as part of an MSDS Degree, please familiarize yourself with some R programming before working on projects.
The class fails to teach most of what it asks for the assignments. In the requirements, only a basic understanding of R was noted, while in reality most of the assignments require you to have an, at least, intermediate understanding of R and how different R libraries work. What's worst is that none is this is really taught, but really it just feels as a very shallow, incomplete, guidethrough of 10% of the whole process. Would not recommend this course for someone who is not following the MSDS degree. You will not learn anything other than opening an R markdown.
Parts of this course were useful to get an idea of what to expect from a career in data science- particularly the interviews with data science professionals.
However, for whatever reason it's difficult to actually get assignments graded (and therefore complete the course) and the final assignment involves uploading a video of yourself speaking - I have no interest in having a video of myself floating around this website.
In my opinion the course is a little bit desorganize. The first modules consist of only videos about Data Science and then the 3rd starts with downloading Rstudio and making a data analysis in R. I thought that was a big jump and I felt a bit lost at first. Also, on peer-to-peer review, I reviewed a work from 2 years ago, that was a bit scary haha but overall it's great introdutory course on Data Science.
While the content of this course is interesting, it feels a bit disorganized. It starts strong with real world examples and professionals from companies as well-known as Google. But, it then divulges into using R to read in data, clean it and create R markdown documents for analysis. This was a fun project, but the instructor does not really teach anything here. They just use an example that is not related to the data in the project, and then they just copy and paste some R commands into the console. I had to use YouTube tutorials for pretty much all of it because there just isn't any actual teaching going on.
A great introduction to Data Science, with plenty of practical assignments that are flexible enough to explore our own questions of interest.
Exercises and lectures were hands-on and informative. I enjoyed the practical advice on data science as a profession.
Very practical overview of the field. Does require knowledge of R to do 2 simple projects if you take it for credit.
Good course, with broad overview and several specific discussions from people working in the field. My only issue is that week 3 seems to assume prior experience with R; just a little bit more support ("go here to install R", "here's a quick tutorial on tidyverse") would make it much easier to reach the level necessary for understanding the lectures and project.
In the end I have to say I learned a lot, but the lectures weren't nearly comprehensive enough and the instructions for the assignments were far too vague. I essentially had to teach myself most of what I needed for the assignments and I was left feeling like I was missing the bar, but then found that the grading criteria was more lenient than expected. It was also unclear at times what kind of document I needed to upload for the assignments. I think this content could be improved quite a bit.
This course is not worth any effort or expenditure of time. It doesn't offer any usable introduction to data science 'as a field'. Then the instructor plunges into random chunks of R code. And then there is the final assignment in the form of a video recording of an 'elevator pitch' of the results of an analysis. This is a joke.
Thanks a bunch, to Dr Jane Wall! I sincerely appreciate it. I am now equipped with valuable tools that will help me in my Data science career path. I really-really enjoyed this course. The in-depth analysis that we will be doing in Data Science will impact the world, hence, it is important to internalize all the concepts in this course. By applying the learnings from this course to the covid-19 and NYPD shootings case studies, I understood the value of preparing a good report that will help the key Organizations.
A detailed, step-by-step explanation of what is useful in data science practice.
Good course for exercise R programming, data analysis and communication skills.
It is very good course. Thank you very much professor.
got good knowledge of R
Weldon good very good
Good
Elementary.