"Introduction to Data Science in Python" is very good introductory course for Python DataFrames/Series and related data interpretation methods. I got to learn to read in excel/cvs/text files and clean them and extract meaningful data. The final assignment was very informative into how applied DataScience work. Overall its an intermediate level course with ample coding to do and experiment. It is a very hands on course which is most essential to understand fundamental concepts clearly. I am happy I took the course. Looking forward for the next course on visualization.

Es un muy buen curso.

Lo que lamento que es que el Autograder es Todo o Nada y es imposible tener menos de 100 puntos. El codigo puede tener cosas buenas o no tan buenas, pero no todo esta mal.

No logre aprobarlo en la ultima Q6 pero en general es muy buen curso.

Quizas por el tiempo que uno dispone , puede ser poco para profundizar mejor el estudio. Yo trabajo en area TI de Retail y en estos dias de fin de año es dificil..

Muchas gracias a todos. Quienes preparan el material y a los instructores.

Un abrazo para todos...menos para el implacable AutoGrader..

Atte

Ricardo

If you go into the lab and play with different parameters of the functions you'll get a hold of what they do. Just watching the videos is not gonna help you learn. I think people give this course a bad name because they didn't really spend the time to actually play with the functions themselves. The assignments are challenging but the materials are all covered in the lectures. I don't understand why people say the assignments require more than he teaches. Sure they are tough and yes I did consult my notes and stackoverflow. Overall great learning materials.

This is an excellent Pandas bootcamp but be prepared that you have to invest more time into the Labs than in other Coursera courses. You should know some Python. I found the Python-Specialization from UMI a good basis. Some prior knowledge on working with data can be helpful.

After some introductory videos you have to find your own way for solving the Labs. I found this very realistic. Later nobody will ask you how many Python functions you know by heart but you will get tasks and you have to find a way to solve them with Google, Stackoverflow etc.

this is a challenging course if you are just coming out of the Python Intro specialization. Much self learning is required, however that is how most programming happens, so I think overall this is a very good course to partake it. I don't know if perhaps the questions could be worded more clearly, as much time was spent trying to understand them, and I had to resort to the discussion forums to clarify their intent. In any event, that might be also reflective of difficult demands in the business world, so I still give this course a 5 star grade.

This is an excellent course. The professor builds concepts very naturally, lectures well, and gives good examples. Most of all, the exercises are really designed to test comprehension and the final week's assignment is an example of a real world question using real world data that must be cleaned and interpreted to test a simple hypothesis and derive an answer. This course has made me feel like I have the tools I need to take on my own datasets. Even the optional reading/listening assignments in this course are interesting and thought provoking.

Great introduction into pandas environment in Python.

First assignment was most difficult in my opinion. There were times i had no idea where to look but stackoverflow and the pandas documentation were great references, which once i understood how to better search and interpret, i was able to do what i wanted.

One thing i liked was there was ample struggle in this course. I've done other coursera courses and found that the exact problem statement and solution were posted online, which was hard to avoid when looking for more generalized help. I

This was a very helpful course in getting comfortable with using the pandas library and different concepts in numpy in data analysis. The fact that the instructors and course materials do not give you 100% of the tools to complete the assignments is a plus. Every data analyst and programmer inevitably will have to rely on self-guidance.

This course by itself may not be immensely useful in the professional world, but lays a strong foundation for the student to focus more on plotting, analysis, and conceptual learning, rather than on code.

The course has sufficient rigor to prepare you for what is coming in the rest of the program. My opinion is based on my experience with the many Johns Hopkins Data Science courses I completed on Coursera.

The auto grading system can be improved. The feedback on failed submissions is sparse and you have to go to the discussion boards to figure out the solution.

Warning to students who tend to get trapped into figuring out a solution on their own:

PLEASE go to the discussion often when doing the assignments and you will save a lot of time!

The assignment this course has is just awsome ,as it takes real the efforts to come across the solution but thanks to the discussion section of the course, the faculty is always there to help and question get answered real soon... But i believe that there is need to add more content to the teaching section of the course ... A special thanks to Prof Christopher he is so good at teaching every concept he teaches is as clear as a crystal. But still if there was just more content it would help a lot while working out assignment question.

PROS: Great course to getting started with data cleaning/curation! The course makes it possible to start work in real project with Python (Pandas) when it comes to data engineering activities. The main functionalities in the Panda libs. are covered which gives confidence to continue to built skills in the data engineer/science area. The instructors responded quickly in the forums. Highly recommended!

CONS: If you haven't worked with Pandas before, double at least the time estimates provided by the lecture instructors / course notes.

It is a good course for introduction to data science in Python. I was looking for something to get started with Python and Data Science. I found this course a bit challenging given that I did not have any knowledge of Python, but it was not difficult to catch up with the good friend Google.

The course is well structured. Short videos that give you a first insight on the topics, however to complete the assignments you need to search and read more deeply. This is good because is how it works in the real world and in a job.

I started this course a few months ago, but realised I needed a bit of Python to do it, so went back and did the Python for everyone class.

I've learned a lot, particularly about data wrangling in python, and how to approach problems. Its a good start to data science using Python.

And I was extremely grateful to the tutor for his help. Doing a MOOC, I don't really expect much support, and I think this is the first time I've ever asked a tutor something - its great to know that help is available when you need it.

Assignments were bit tricky and more challenging than i expected.Most of the problems were based on topics that i was totally unaware of.But soon i realised that self gained knowledge is actually the true knowledge.I had to refer some text books also, for completion of my assignments.But still the overall quality of the content was good.And after completing this course, i have acquired one more skill, i.e. to search for the genuine sources of information rather than the fuzzy, confusing and more decorated one's.

Definitely one of the best course I have taken so far.

The course started with refreshing the python basics and then it's a deep dive in to the ocean of Data Cleaning tasks.

Special Thanks to Dr Brooks for keeping the course straight forward and simple. All the concepts are made very clear during lecture and the assignments are a perfect application of these concepts.

Even though assignments are challenging, will feel the sense of accomplishment on completing these.

Thanks to the entire course team for the course.

This is an awesome course which slowly dives down into Python week by week. The professor has explained all the concepts in a concise manner. This course covers all the basics of pandas and numpy library and leaves you on the door step to explore them in detail.

Thoroughly loved the whole experience. Special mention to the Jupyter integration which makes it easy to code and execute.

Thank you to the entire team and specially to professor Brooks for making this special and providing a nice learning experience.

It was an overwhelming experience to gain amazing knowledge about python in depth and is perfect for getting started with data science. The assignments were awesome and traversing through the pandas documentation was quite exhaustive yet rewarding. The course offers great self learning and working on practical implementation of the projects. The idea that pandas can explore various data science approaches interestingly was given insight by the course. I thank the instructor for his awesome approach. Cheers!

Great intro to Python for Data Science. I have a database and programming background and self-taught Python. I could get by but didn't always understand the nuance of what I was doing (which often led to frustration and far too much time on Stack Overflow). This course is a good overview of the language, including numpy and pandas, and more importantly, it supplies much needed context. Instructor is easy to listen to, and the supplied jupyter notebooks allow you to follow along and play with the code.

I found this course very good.

I learn a lot about different aspects of data science : 1) epistemology, 3) tools (Pandas and NumPy in Python) to clean and analyse data, 4) some statistical tools, 5) ethical and/or methodological issues.

When I was doing assignments, I learned how internet communities are powerful in this era of information/knowledge society. Some plateforms as "Stackoverflow" are just wonderful.

One last thing: thank you for accepting my application for funding (in full) for this course.

There are a lot of negative reviews for this course, and I would say it's not for everyone, depending on what kind of learner you are. I learn best from trial and error, this course is very assignment-centric, requiring creative thinking about how to solve the problem rather than following a procedure. This is not the best course for learning the optimal way to perform specific functions, but it's a great course for those that are trying to learn Python as a new skill by solving well-designed problems.

Very good introduction to Pandas Series and DataFrames for Data Science. Fast paced course with good supplementary materials. The homework is progressively challenging. Sophie the Teaching Assistant is particularly helpful in the forums. I don't recommend this course for those without programming or python scripting experience. Also, the homework exercises took me significantly longer than the estimates projected, but I budgeted about double the time and was able to complete the course on time.

This was a really thought out and well planned course. Gave me a proper exposure on Pandas. The best part about the course is its assignments and the fact that it makes you think and even lose your mind. The discussion forums are a bliss and the work that Yusuf Ertas puts in is phenomenal. I've seen him responding in almost all of the doubts put forward. Above all this course taught me to read in data how ever challening it maybe into a dataframe and encouraged me in making my code more pandorable.

Overall good! The assignments is challenging and comprehensive enough to let students think out of the box and reinforce what has been learned. The assignment questions mimics the questions asked in real-world Data Science projects that indirectly teach student on asking Data Science questions. The instructor can explains the concept in easy & intuitive way and teaches with coding example. This course will definitely horn your basic Data Science skills in Python especially using Pandas library.

I love all the features that pandas and numpy have to make routine data cleaning tasks easy. They are so much easier to use than core python, require less code, and work faster. I love these methods (e.g. list comprehension, mapping lambda expressions across data frames, pandas datetime functions, read_csv, merge etc... the list goes on...). Thanks for the great tools. I've learned a lot of valuable techniques from this course, and have started using them at work already, to great benefit.

This course was very much helpful to understand Pandas as a Data Science tool. I started to understand the way you need to think, whenever you use Pandas. Especially the assignments were very good. A very small exception is the assignment in week 3, in which you have to clean your data frame. That was a bit too extensive, I think. I rather used that time and efforts to learn to apply more statistical methods.

But overall: this course exceeded my expectation and I am very much helped by it!