This course will continue the introduction to Python programming that started with Python Programming Essentials and Python Data Representations. We'll learn about reading, storing, and processing tabular data, which are common tasks. We will also teach you about CSV files and Python's support for reading and writing them. CSV files are a generic, plain text file format that allows you to exchange tabular data between different programs. These concepts and skills will help you to further extend your Python programming knowledge and allow you to process more complex data. By the end of the course, you will be comfortable working with tabular data in Python. This will extend your Python programming expertise, enabling you to write a wider range of scripts using Python. This course uses Python 3. While most Python programs continue to use Python 2, Python 3 is the future of the Python programming language. This course uses basic desktop Python development environments, allowing you to run Python programs directly on your computer.
DR
It's fantastic learning opportunity, well taught, covered almost everything.Baseball project is cherry on the cake.
VS
Great Course !Specially the project at the end uses some really powerful code, combining all that we had learnt throughout the course
JD
This course is good and the instructors way of teaching is quite attractive and interactive. I have learnt a lot during this course.
JX
Extremely clear explanations, and good practice exercises for evaluation. Builds well upon previous courses in the specialization. Excellent course.
AD
Good course to learn and enhance our skills. I faced issue in quiz as i was not able to edit to add code in quiz. Please resolve it.
SA
One of the best courses on data analysis in python. It was a deep dive into the basics. More than the course videos, i enjoyed the assignments more.
SC
Great class. It might not be perfect for beginners, but good for someone who has had one semester in Python programming. Thank you, everyone!
MW
Assignments get tough for a beginner like me. But that is exactly what is required to learn the material.... I think.
AA
more so a data management and cleaning course rather than an actual analysis course. Was expecting more statistical analysis computation
J
Important concepts covered - including dictionaries and data structures. Useful in developing a basic foundation and understanding of Python.
AS
One of the best course in this specialization. It was really interesting work with some real world data which could really enhance the problem solving skills.
MR
Excellent Lecture + good support from mentors (Patrick, the only one?). However, the final project's description is quite obscure, I have to rely heavily on owl-test.
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Overly simplistic lessons and quizzes followed by brutal projects that require you to teach yourself techniques not covered in the lessons. Now I don't feel like I've learned anything because I've just had to trial-error my way through half the code. What worked, what didn't work? I can't remember...
The lessons are good , but , the assignments /assignment descriptions are vague . I spent much more time understanding the question than answering the question
the assignments are not clearly illustrated to us. it is very difficult to understand what is required to be done . even it you code it, it is still hard to understand what is to be done.
Big disappointment after 2 previous courses. The lectures are not really giving you a lot of new material to learn but difficulty of projects you are expected to finish goes up a lot. It left me confused and frustrated. And I think I am not the only one (check on-line forum). Big plus for forum mentor who is helping a lot (he is even posting extra exercise there). And he is making a difference. Still at the moment I decided not to continue with the last course in this specialisation. Too much frustration in this one.
I just dislike coding
Excellent and challenging, especially the final project
It is clear that as the specialization advances, the complexity of the assignments increases. The last 2 weeks assignments, in my opinion, where REALLY confusing and for what I read on the forums, not only me, but a lot of students had trouble understanding the instructions, and inputs (Week 4)... it is nice to finally get the code but it is really frustrating and discouraging to spend too long in trying to find out what the instructions are. I think there should be more clarification on what is expected. I felt like Week 4 was way more complicated than the examples shown in the videos.
Final project is way more difficult than what they explain in the course.
Final project is not only difficult and long but also due to the amount of extra files and lack of explanation on how to start is very frustrating.
Some concepts like "list comprenhentions" are used by instructors in the course, but not explained and instructor just adviced to go to Python.org and learn it there.
Excellent! I previously completed the Fundamentals of Computing Specialization from the same instructors, yet there was ample new content in this course. This course focuses on techniques for dealing with tabular data, which are represented as nested arrays (list of lists, dictionary of dictionaries, list of dictionaries) using map, filter, lambda, list / dictionary comprehensions and the csv module for loading and saving csv files. The final project is rewarding, requiring relatively little code but a clear understanding of the underlying data structures.
Compared to the Fundamentals of Computing specialization (which later moves towards developing efficient algorithms), the series so far seems to be geared towards developing practical skills for applying Python for data science. Very rewarding.
Definitely more challenging than the previous, but if you want to be serious about scripting in Python, very few other courses will get you there than this one. A little too focused on reading csv files than I wanted, but even then, I was provided with enough challenges working with dictionaries, lists, all the while learning new Python skills (lambda functions, list comprehensions, etc.) that I can't really give it anything below 5 stars.
Focuses on Python dictionary skills and not Pandas dataframes.
Extremely clear explanations, and good practice exercises for evaluation. Builds well upon previous courses in the specialization. Excellent course.
One of the best courses on data analysis in python. It was a deep dive into the basics. More than the course videos, i enjoyed the assignments more.
Important concepts covered - including dictionaries and data structures. Useful in developing a basic foundation and understanding of Python.
Excellent Course, I really recommend it. This was my first course about python but now I gonna take all the courses available
It's fantastic learning opportunity, well taught, covered almost everything.Baseball project is cherry on the cake.
The reading and writing file csv file part is difficult, especially reading data into dictionary part.
the learning videos are somewhat basic but projects are way more complicated, not necessarily the coding but trying to figure out what they are looking for like they make it confusing for you. But once you decipher what they want, the coding is not that bad.
The material is taught in a straightforward and easily accessible manner which can lull you into thinking that this is easy, but it is not. The final assignment and optional programming exercise in the last week will really force you to dig deep to put it all together. Everything you need is right there in the lecture notes you just have to be willing to listen. Great course.
this course is very helpful to me to increase my coding skills on data analysis and the facuilty are very knowledgable and willing to share their knowledge. i am very thank to all who are worked in this project and specially i am thanking my company thats gave me a beautiful opportunity to learn and make certification in python data analysis