University Of Colorado Boulder Data Science Online – The University of Colorado Boulder’s (CU Boulder) Master of Science in Data Science (MSDS) program was launched in May 2021. The program is taught and taught through Coursera, a provider of massive open online courses (MOOC). 30 credit hour classes taught by CU Boulder instructors. As of May, I have completed four classes and completed the computer science pathway that I officially entered the program. Having no prior knowledge in computing or data science, and having never taken MOOCs for educational attainment, this program was a new experience for me. In addition, the program is new as the MSDS starts in May 2021. In this section I will give an overview of the program, examining the strengths and weaknesses that I have seen in my first months.
As mentioned before in another post; The main thing that drew me to this project was the performance based installations. A student must take three one-credit computer science courses to maintain a 3.0 GPA. Recently, the requirement for admission was expanded by a student maintaining a 3.0 GPA in three calculus courses (although ultimately six courses must be taken as part of the program’s curriculum). In addition, the program requires letters of recommendation for admission; No GRE scores or transcripts are required. The lack of letters of recommendation was a concern for me, as I was unable to connect with professors without strong academic connections for letters of reference. The MSDS program is very easy to get started; One should visit the MSDS web page; You must click on the sign up button and follow the instructions.
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Regarding registration, the program works differently than the on-campus grades (in my experience). This is very obvious with the list. In my undergraduate work, In addition to the spring and fall semesters, there are some classes that are offered in the summer and fall. Even CU Boulder’s MSDS contains spring and fall sections. It also includes summer schools. Again, each section is divided into two sections. So, throughout the year, Spring 1, Spring 2 Spring 1 Summer 1 and so on. Pool 2 Eight weeks each term and Summer 1 etc. At the end of each session.
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A student may enroll in three courses, each taking three credit hours. Therefore, they can take six credit hours per week and 18 credit hours per year. If the student achieves the highest level in each course. They complete the program in 10 sessions, or about a year and a half. It should be noted that the CU Boulder area only requires completion of the degree within eight years.
As with any formal education program, cost is an important consideration for the student. CU Boulder’s MSDS shines a light on this topic. Because it is an online program, there are no out-of-state or in-state schools. However, the cost per credit hour is $667; Therefore, if one reaches the highest level, $2001 per session; $4002 per week, and $20,010 for a Bachelor’s degree. Cost is $35; Compare that to the CU Boulder campus MSDS, which costs $154 for Colorado residents and $44,694 for non-Colorado residents. The price drop is very attractive, especially during the COVID-19 pandemic.
However, With its low price tag; MSDS on the internet is a disadvantage for the budget. Students in the program are not eligible for financial aid, such as the FAFSA. Also, enrollment in this program does not delay federal student loans and does not qualify the military for educational benefits. For some, this is a barrier to joining the program. However, private funds can be used to help pay for the project.
Now I want to change the most important part of education, the lessons themselves. There are four courses; Three courses in the computer science track covering algorithms and data structures and one course in the data science field are completed. All computer science courses have the same structure and are different from a general data science course, although programming skills are heavily used.
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From computer science courses; Not to mention that Professor Dr. Sankaranarayanan into the class and focused on the material. They often link the project to the topics found in technology and show up close to honoring the most famous people in the field. The class content is divided into four parts (or four weeks as Coursera calls it). For each course, Dr. Sankaranarayanan’s lectures are about two to three hours long, divided into separate lectures. These lectures are recorded and check your understanding of all unmarked review questions throughout the lecture.
After most of the lectures, there is an exam. I found these puzzles difficult but manageable. In addition, Coursera allows unlimited attempts on their quizzes, and most are multiple choice, so you have to repeat the test to get a good score. So, For each module in the Computer Science track; A program assignment. These efforts are endless endeavors. However, there is no guarantee of a correct answer. Instead, you need to find the right answer. I think each session will take about two hours, and I find that my time varies a lot. For some, I am finished in one hour; For others, it’s around 10. These functions are included in Jupyter Notebooks and provide test cases for each problem. In my experience, when my code passes the test cases, the last action step is skipped.
The last one is at the top of this programming exercise with quizzes. In the first course of the specialization, the final task was a traditional test conducted via web camera. So the last step for the next two lessons is programming. Each week’s programming is structured in the same way; Only one attempt to complete within 24 hours (although each final level has test cases). Overall, I like these lessons. My first lesson I didn’t take a formal computer science course before taking this program, but I adjusted easily. This is due to the quality of the course content and hands-on activities. I left these courses with a better understanding of algorithms and data structures.
Further studies Data science as a field is not surprising to me. However, it worked. This course provides an overview of data science and also serves as an introduction to R. I’m new coming into the course to R, so it’s a learning curve. Even the professor provided videos walking through an example of the project in RStudio. I ended up doing a lot of lessons on my own. I appreciate the introduction of the tutorial to the RStudio and R code files.
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Additionally, this course is the first experience using Coursera’s peer review rating system. Not sure if this system is the same for every class, So I will explain how I did it for this lesson. First, I uploaded a selected project, an R code file, or a video explaining my data analysis project. Next I will review the three works. In each example, a grading rubric was provided; I just follow this rule. In addition, I can send information about the assignment that the sender can answer. So, I will finish reviewing the three works; I got a grade on my assignment when others reviewed it (I’m not sure how many students reviewed my assignment before I got it).
You may be disappointed if you submit your assignment after the deadline specified by Coursera, but this system seems to work well. Getting your grade depends on others evaluating the assignments, so if you submit your work after the deadline, there may be no students to grade the assignment. This worries me. But I have to say that I’ve submitted some work past the deadline and I’ve still scored. In addition, when evaluating colleagues; The reporter and reviewer must be anonymous to each other.
I like the computer science course structure based on the topics of the course. I like solving problems to make test cases work while relying on programming assignments. In addition, I think that the materials in these classes will be more developed.
Finally, I would like to mention the peer-to-peer interactions for these courses. to interact with friends; CU Boulder has a dedicated Slack channel. It doesn’t replace traditional university spaces but is a source of interaction. In the collaboration of the teacher, it is an important part of traditional education; This interaction is not possible or allowed by MOOCs. This MSDS program is no exception, and in fact,
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