Learning to celebrate small accomplishment is a catalyst to attain giant strides and every accomplishment is worthy of gratitude. Thus,  Alhumdulillah I’m thankful for another goal achieved in the right direction as I completed the Applied Data Science with Python Specialization from Coursera. It was big learning opportunity for me to learn new skills and growing professionally.

Here some thoughts and my observations about the specialization: .

About The Specialization:

This specialization is a series of five courses, each of which focuses on some aspect of using Python for Data Science applications. Each course is focused on use of one or more free Python libraries.
This skills-based specialization is intended for learners who have a basic python or programming background, and want to apply statistical, machine learning, information visualization, text analysis, and social network analysis techniques through popular python toolkits such as pandas, matplotlib, scikit-learn, nltk, and networkx to gain insight into their data.

The courses and the libraries covered by each are :

1. Introduction to Data Science in Python : NumPy, SciPy, and Pandas
2.  Applied Plotting, Charting & Data Representation in Python : Matplotlib and seaborn
3. Applied Machine Learning in Python :  scikit-learn
4. Applied Text Mining in Python : NLTK and Gensim
5. Applied Social Network Analysis in Python : NetworkX

Time Commitment:

 As I’ve mentioned in my previous post, alhumdulillah I successfully completed my master degree and unfortunately could not get the admission in MS Data Science at ITU. It was bit heart breaking at that time but I decided to not to give up and follow my dream & make a my own path. At that time, I was not doing any freelance projects except for blogging and writing, and generally had an astounding amount of time to devote to the program.

I think the complete specialization is roughly equivalent to a one-semester university course. But It took me almost eight to nine months to complete this specialization. As a learner, I feel it’s always better to never rush but take our time to absorb knowledge and new skills.

Slow & study, wins the race.

But it was overall an awesome learning experience and I wanna do more now.

Useful Tip: That being said, if you are an absolute beginner, Coursera estimates the program will take 2 months to complete. Some people, of course, may take longer. However, if you are looking to finish each course within one month, I highly recommend completing some prerequisite learning programs before starting.

2. Cost

Each course of this specialization uses a subscription-based payment model. There is a monthly fee of $49 that gives you access to all the course modules, assignments, discussion forums, and peer-graded assignments. No matter how long it takes you to complete the courses, you will be charged the monthly fee unless you finish the program or cancel your subscription.

But I had availed the Financial Assistance from coursera.

The specialization build on one another course.

First course “Introduction to Data Science in Python” are designed to assume that you are proficient with NumPy and Pandas, and all courses after the second course ” Applied Plotting, Charting & Data Representation in Python ” assume you are proficient at creating plots with Matplotlib, and the last two courses “Text Mining” and “Social network analysis” assume you know how to train a machine-learning model. So, my recommendation is take the courses consecutively in the specified order, although you could take the last two at the same time.
Cost

This is an intermediate-level specialization, and it is assumed you already know how to write programs in Python. It also assumes some elementary knowledge of statistics and discrete mathematics, but nothing too advanced. I had previously taken Andrew NG’s Machine Learning course, so I already had some familiarity with the terminology and the math. I think it is one of the best course to start learning Machine Learning. I just needed to do a little googling once in a while to refresh my memory or get an introduction to unfamiliar concepts.

As hinted at by the word “Applied” in the specialization and course titles, there is not much theory presented in these courses. There is just enough theory to understand the exercises. I took the specialization concurrently with Andrew Ng’s deeplearning.ai specialization, so I got a nice dose of neural-network theory mixed in with my data science, but people who want to understand the algorithms in detail will need further study.

The programming assignments are both good and terrible. They are good in that I feel like I got a solid introduction to what libraries are available and how to use them, with enough challenge to make me dig into the documentation and Stack Overflow a lot, but they were not so difficult that I didn’t know what to do. They are terrible in that the exercises are automatically graded, and the auto-grader has a lot of problems. Assignments that should take an hour or two instead take two or three times that due to the auto-grader rejecting correct answers with a “wrong” data type, or “wrong” number of significant figures, or expecting a different result than what the assignment’s instructions specify. After doing each assignment, one has to spend an hour reading the course forums to find out the tricks to getting correct work accepted. If these were brand-new courses, I could excuse the auto-grader imperfections, but these courses have all been run multiple times, so it’s very frustrating that the bugs haven’t been worked out and the teaching assistants don’t understand the issues.

Most of the courses include some lectures or assignments dealing with the ethics of data science. I was glad to see that. We all need to be thinking about the consequences of these technologies.

I found the courses in this specialization useful. It was nice to find a set of courses in Python; I’ve abandoned a few other courses that use R or Octave, because I found learning and writing code in those languages while also learning new concepts to be too frustrating. I especially liked that the later courses used the tools and techniques introduced in earlier courses, and a few of the later assignments had the feel of doing real work. More than once, I was amazed at how easy it was to do things that I would have said were impossible before I took the courses.
but it is very insightful and I would absolutely recommend this to anyone who has been in a position where they have a love/hate relationship with their personal finances or to anyone who just wants to better themselves.
I know I don’t know enough to call myself a competent data-science practitioner, but I do feel like I would at least know where to start looking if faced with a data-science task. I think it will be beneficial for me to get into a good firm as an intern.

Everyday, I die to my old self and grow into the woman I must become. Thanks to the Team Coursera & UNiversity of Michigan
for putting the time and effort to make it available.
who moulded me because He is the source and I’m merely a resource.