Coursera john hopkins data science review năm 2024
This post is a summary of several posts that I had on my old blog about the Johns Hopkins Data Science certification offered by Coursera. Show I am not sure how typical of a student I was for this program. I currently work as a data scientist, have a decent background in AI, have a number of publications, and was completing a PhD in computer science at the time of this program. So, a logical question is what did I want from this program? At the time I took this certification I had not done a great deal of R programming. This program focused heavily on R. I view this as both a strength and weakness of the program. I am mostly a Java/Python/C#/C++ guy. I found the R instruction very useful. I’ve focused mainly in AI/machine learning, I hoped this program would fill in some gaps. I really liked this program. Courses 1-9 provide a great introduction to the predictive modelling side of data science. Both machine learning and traditional regression models were covered. R can be a slow and painful language, at times, but I was able to get through. It is my opinion that R is primarily useful for ferrying data between models and visualization graphs. It is not good for heavy-lifting and data wrangling. The syntax to R is somewhat appalling. However, it is a domain specific language (DSL), not a general purpose language like Python. Don’t get me wrong. I like R for setting up models and graphics. Not for performing tasks better suited to a general purpose language. In a nutshell, here are my opinions.
Course BreakdownHere are my quick opinions on some of these courses.
Peer Reviewed GradingIf you are not familiar with peer review grading, here is how it works. For each project you are given four counterparts that review and grade your assignment. This is mostly double-blind, as neither the student or reviewer knows the other. I used my regular GitHub account on all assignments. So it was pretty obvious who I was. I was even emailed by a grader once who recognized me from my open source projects. Your grade is an average of what those four people gave you. At $49 a course maybe this is the only way they can afford grade. I currently spend nearly 100 times that for each of my PhD courses. :( Overall, peer review grading worked good for me in all courses but one. Here are some of my concerns on peer grading.
So here is my story in the one case where peer review did not work for me. I in the upper 98-99% range on most of these courses. Except for course 8. I had good scores going into the final project. However, two of my peers knocked me for these reasons:
This took a toll on my grade, I still passed. But this is the one course I did not get “with distinction” credit. Yeah big deal. In the grand scheme of things I don’t really care. Just mildly annoying. However, if you are hovering near a 70%, and you get one or two bad reviewers you are probably toast. Capstone ProjectThe capstone project was to produce a program similar to Swiftkey, the company that was the partner/sponsor for the capstone. If you are not familiar with Swiftkey, it attempts to speed mobile text input by predicting the next word you are going to type. For example, you might type “to be or not to ____”. The application should fill in “be”. The end program had to be written in R and deployed to a Shiny Server. This project was somewhat flawed in several regards.
After spending several days writing very slow model building code in R, I eventually dropped it and used Java and OpenNLP to write code that would build my model in under 20 minutes. Others ran into the same issues. There are somewhat kludge interfaces between R and OpenNLP, Weka and OpenNLP. But these are native Java apps. I just skipped the kludge and built my model in Java and wrote a Shiny app to use the model in R. This was enough to pass the program. I was not alone in this approach, based on forum comments. Okay, I will just say it. I thought this was a bad capstone. This was just my experience on the first run of the certification; hopefully, they’ve improved it since. The rest of the program was really good! If I could make a suggestion, I would say to let the students choose a Kaggle competition to compete. The Kaggle competitions are closer to the sort of data real data scientists will see. I am proud of the certificate that I earned. If I were interviewing someone who had this certificate I would consider it a positive. The candidate would still need to go through a standard interview/evaluation process. ConclusionsGreat program. It won’t make you a star data scientist, but it will give you a great foundation to go from. Kaggle might be a good next step. Another might be a blog and doing some real, and interesting data science to showcase your skills! This is somewhat how I got into data science. A question that I am often asked, is what would I think of this certification, if I saw it on the resume of a new data scientist that I was interviewing. In isolation, I would not give a hire recommend based solely on this certification. However, it would show me that someone has mastered the basics of data science. They know what format data needs to be in for predictive modeling. They know their way around the R-programming language. They also took the initiative to undertake something that took a decent amount of effort. So yes, it is important, particularly, if your resume is lacking in the analytics area. Is John Hopkins data science course good?The experience and impact of a master's in data science vary from program to program. However, we can examine Johns Hopkins' career outcomes to get an idea of the general benefits of this program: 88% of graduates have been able to apply what they learned in their program to their jobs. Is data science course from Coursera worth it?The Verdict Coursera data science free courses are a great headstart for anyone trying to learn data science and machine learning for the first time. They are not valuable online degrees in data science. Which course is best for data science on Coursera?In summary, here are 10 of our most popular data science courses. Google Data Analytics: Google.. Applied Data Science with Python: University of Michigan.. Introduction to Data Science: IBM.. Foundations of Data Science: Google.. Introduction to Data Analytics: IBM.. Data Science Challenge: Coursera Project Network.. What is Johns Hopkins data science ranked?Johns Hopkins Engineering for Professionals Master's in Data Science program is ranked 2 by U.S. News & World Report. |