- Harvard University via edX
- 17 months (1-3 hours/weekly)
- 9 Skill building Courses
- Course Type: Self Paced
Being a person who has an interest in the field of data science, I always look for different learning resources mainly online courses on data science that can keep me updated with what is happening in the world of data science and how it works. As I continue with my quest to find the best learning resource, I discovered that Harvard introduced a massive catalog on the very reputable e-learning platform that is, EdX. Here I was able to find a lot of courses in this professional certificate in data science.
Who is suitable to enroll in this program?
It’s not like there are any prerequisites to enroll in this self-paced program but it’s also not like a beginner with no background in data science can expect to learn in the best possible manner. Also you can’t just start from any course you like. For example, if instead of starting from course no 1, you wish to take course no 4, then it is highly unlikely that you’d understand all the concepts the way you should.
So remember two things, one you should have some basic understanding of data science before enrolling in this program (for the sake of better understanding). Second, make sure you are taking the courses in the exact same order if you really wish to understand all the concepts in a meticulous manner.
My Overall Experience
Frankly speaking, completing this entire series of courses was one tough job for me. But what kept me moving was my interest in this field and the way all the courses were instructed. There is no doubt over the fact that Harvard is one of the best educational institutes there are and this somehow gives us the satisfaction that their faculty is also the best. So I would say that taking this certificate has been an overall positive experience for me. Why? Because it gave me a solid conceptual understanding of today’s data science. How? Well for starters, I was able to understand how to analyze, wrangle, and then visualize the data in a step-by-step guide. Then, before enrolling in this program, I had difficulty in using GitHub in most of my projects, but the “Data Science: Productivity tools” course gave me a better understanding of how GitHub and other tools like Linux/Unix can be used in my projects. I really appreciate how each course was put in order and if you start and finish the courses in the same order, then you’ll understand why they have been arranged this way.
The broad and modular approach of the course series is something to be appreciated. As it starts from the theoretical foundation that is rooted in the data science methodology. Once you’re done understanding the theoretical part, then comes other courses that will give you guidelines for the more practical coding sessions. Similarly, the entire curriculum was structured as if it were a project where you start from little knowledge to project delivery in a very smooth manner. I was so invested throughout the course series that there was not even a single moment where I felt I’m wasting my time. The instructor Rafael Irizarry turned out to be an amazing instructor. Throughout his course (Data Science: Visualization) he kept me busy with a lot of concepts like giving an overview of the data visualization principles. Then talking about the communication data-driven findings was also a fun part. Then, another course that I enjoyed learning the most was the 6th one that is, Data Science: Wrangling. This course also had me occupied the whole time as it has some technical topics in it like how to import data into R from different file formats. Then, wrangling data using dplyr was also quite interesting. Sure there were some stages where I was not able to wrap my head around the difficult topics like probability in data science and understanding inference and modeling. But going through them over and over again solved this problem too. I got the chance to interact with other students who had enrolled in this program. There were quite some talented students there and with the help of each other and the instructor as well, we managed to pull through. You might also be interested in free Ivy League Data Science courses.
One thing that I think should be cleared here is that these courses are not a child’s play and require serious concentration and determination from a learner. So unless you are enough determined and have a little background in data science, don’t enroll in this certificate program.
Completing the first four courses which are 1. Data Science: R Basics, 2. Data Science: Visualization, 3. Data Science: Probability, 4. Data Science: Inference and Modelling gave me a sense of completion and a clear head on how all these things work. Completing these four cases took me almost half a year. So I gave myself a break for two weeks and then started the 5th course that is the data science productivity tools and then started the next and so on until I reached the last course which is the capstone project. And this is how it took me 15-16 months to complete the whole certificate program.
So this was my overall experience with the Harvard professional certificate in data science. And trust me it was worth the time, effort, and of course money. Now if you want to judge yourself whether or not to go for this certificate program then I have listed below all the courses involved in this program and the overall cost and time it is going to take you to complete the whole series.
Structure and Course Topics:
Harvard’s professional certificate in data science encloses in it a series of 9 courses the names of which are mentioned below.
- Data science: R basics
- Data science: Visualization
- Data science: Probability
- Data science: inference and modeling
- Data science: Productivity tools
- Data science: Wrangling
- Data science: Linear regression
- Data Science: Machine Learning
- Data science: Capstone
Note that all these 9 courses are self-paced which means that you can study whenever you want and at your ease and pace. An overall estimate as to how much time it’s going to take to complete this entire program was made, which is, 1 year and 5 months based on putting in 2-3 hours per week. And above I have mentioned it took me 15-16 months to complete the program which is quite near to the expected given. So if I can complete this program in this much time, then anyone can.
In this series, you will get to work around the following tools.
What will you be Gaining from this Program?
Well, the program has 9 courses in it which clearly means you getting a chance to learn a lot about data science just like I did. So here are the details of it.
- Understanding of the fundamental R programming skills in detail.
- Getting an opportunity to understand the different statistical concepts like probability, inference, and modeling. And then getting a detailed explanation on how to apply them in practice.
- Similarly, you will gain a lot of experience with the tidyverse which also includes data visualization with ggplot2 and data wrangling with dplyr.
- After that, you will get a chance to become familiar with the different essential tools for practicing data scientists like Unix/Linux, git, and GitHub and then the RStudio.
- Right after that, you will be given step-by-step instructions as to how you can implement the machine learning algorithms like a professional.
- Then you are going to gain an in-depth knowledge of the fundamentals of data science with the help of real-world case studies.
As you can see, completing the entire series might be a little time taking or more but in the end, all the hard work and effort will be worthwhile.
Pros of Enrolling in this Program:
I have been talking about my experience so far so thought why not enroll some of the pros in front of the readers so that they’d also be able to understand the benefits they will experience throughout the series.
All the Courses were quite Engaging:
To be honest, when I first learned that there are 9 courses in this series, I kind of wanted to back out as 9 courses mean a lot of lecture videos, assignments, and notes, etc to follow right. But when I started the series, I’d say my interest grew bigger. The lecture videos were quite engaging and had to-the-point details in them that made me consistent on completing the entire series. By providing a good mix of video lectures, I felt that the approach adopted here was quite effective. And it felt quite natural and smooth making you want to start the next lecture as soon as you’re done with the first one.
Gave me the confidence to start with personal projects:
Though all the courses had something unique to offer and made me learn a lot of things. But what boosted my confidence to start doing something on my own was the final capstone project. By following everything there was in the final course, I got the confidence I needed to start doing projects on my own. So yes I would say the last course was the game-changer.
Amazing video quality was a plus point:
Whenever you are learning behind a screen, what matters the most is the quality of the video and audio. It’s because if you’re not able to see and listen to the instructor clearly then you get too much distracted by it and instead of focusing on what the instructor is telling, you focus on why the quality is not good. But I must admit all the 9 courses had the best audio and video quality. The print was so clear that for a moment it seemed like the instructor is sitting in front of me.
Lack of Statistical Concepts:
Though the entire program was exactly what I expected it to be but there was this one problem that I thought should be mentioned here. It is the lack of statistical concepts.
So this is everything you need to know about the Harvard Professional Data Science certificate. By reading all about my experience and how I was able to learn so much from the course, you can easily make a decision whether or not you need this program. Therefore, if you are a BI analyst or a person who is looking to get certified for an entry level data science position, then enrolling in this program has to be the best option. So, enroll now and don’t forget to stay home, stay safe, and never stop learning.
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