Showcase your Python skills in this Data Engineering Project! This short course is designed to apply your basic Python skills through the implementation of various techniques for gathering and manipulating data.
You will take on the role of a Data Engineer by extracting data from multiple sources, and converting the data into specific formats and making it ready for loading into a database for analysis. You will also demonstrate your knowledge of web scraping and utilizing APIs to extract data. By the end of this hands-on project, you will have shown your proficiency with important skills to Extract Transform and Load (ETL) data using an IDE, and of course, Python Programming. Upon completion of this course, you will also have a great new addition to your portfolio! PRE-REQUISITE: **Python for Data Science, AI and Development** course from IBM is a pre-requisite for this project course. Please ensure that before taking this course you have either completed the Python for Data Science, AI and Development course from IBM or have equivalent proficiency in working with Python and data. NOTE: This course is not intended to teach you Python and does not have too much new instructional content. It is intended for you to mostly apply prior Python knowledge.
WT
Very informative and educative course that I would recommend to any one pursuing a career in Data Engineering with Python
DJ
Challenging and informative. Some difficulty interacting with the IBM Cloud unrelated to Coursera.
IL
This should be a second level intro course. Everything you need to kick-off in practical Python is taught in this course. The Py packaging was a useful bonus.
RA
There were some minor issues along the way and I wish there was more guidance practice but, overall, I enjoyed the course and am loving this certificate. Thank you IBM!
PD
This course is very challenged both Python skills for Extract Transform and Load assignment.I really enjoyed it.
HA
Really Nice. But it could be a little more advanced, which would feel like a Project. It was too basic.
RB
A very thorough course, teaches you step by step how to build an ETL process, with lots of videos, readings, very thorough lab assignments, and many hours of practice. Thanks a lot !
AS
I have just one suggestion: please use simpler code in the projects you provided, as I was able to complete them using much easier code than what you offered.
IM
The rubric for grading is not correct for question 3. The instructions to the API questions is confusing. It asks for Country Name, but it seems that the quiz was looking for bank name.
HD
Learned a lot in this class. The practice project helped a lot in doing the final project. The optional module 3 was great! Thank you for teaching!
SA
Beautiful and challenging project. The project measures that students understand and complete the ELT process taught in the previous module.
SN
wow! what a project man, after completing this i tried by my own to scrap some data's in other sites and it worked. Loved it!!
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The final project needs to be completely revamped and have way more description with respect to the logging requirements.
This might be easy for someone with more experience but for a newbie like me, it was hard and it took me quite of time to complete and understand, there is support when you get stuck but they reply with the answer not with an explanation, which is a boomer.
issues on the module
Peer Review Assignment - Data Engineer - Webscraping
and there is not key or cheat sheet. big problems with this one. please help.
Need to explain more, it literally has no explanation at all
Okay, I am not sure how anonymous this is, but here it goes...
You better already know how to deal with Python dataframes. If your intent is to use this "course" as a way to learn about them, you will probably end up doing the same everyone else did: using Google, Pandas documentation and Stack Overflow to learn on your own. If you don't believe me, just go to the Discussion Forums for the course. It has over 60 pages talking about bugs and about how the lack of direction makes it really hard to follow.
You then start rating (peer reviewing) other classmates and you can see how people are struggling to get the questions right.
My rate for this is 1/5 stars. I already knew about dataframes and it should have just taken me about 2 hours to do the whole course, instead it took way longer to figure out what they were really asking and troubleshooting their bugs.
Al parecer no hay ninguna explicación para realizar los cursos de webescraping. Me parece muy mala estructura del curso.
Apparently there is no explanation for taking the webscraping courses. I think the structure of the course is very bad.
Should update contents as the names of api changes.
The video lecture is insufficient to prepare for final assignments. The lab exercise scattered here and there. API, web scrapping, ETL lab and then final assignment. I felt if the video lecture could elaborate more on the terms like variable, built in Panda dataframe, how to pass into a function (recap on what we learn earlier previous module) using ETL example. This would help us prepare better.
Nonetheless, the discussion forum was useful. I am glad the search box is there allow me to input the search on topic I needed most. There were some logistics issue like jupyter notebook wget unable to work. If should have corner for new comer to go for, prerequisites. Let them know if they use anaconda jupyter notebook this is what they need. If they use win/mac then another approach. I spend some time figuring that out.
I have some assignment was thank to help from fellow peers. I wish the lecturer could more active share more hints on final assignments or others. Not many of us are good in programming.
The questions were not clear. Labs poorly formulated
A quite challenging course. At beginning ones may think that the videos material is insufficient, but with the labs and the exercises you'll get a strong base knowledge. It's always good to practice on your own. I really enjoyed the course.
The rubric for grading is not correct for question 3. The instructions to the API questions is confusing. It asks for Country Name, but it seems that the quiz was looking for bank name.
I find that the grading by other students is inadequate.
Many of the assignment instructions are unclear
Good course to start learning basics of web scraping, APIs and ETL process, but unfortunately there are problems with some practical tasks:
• Unable to set up IBM Watson, it kept asking credit card info, I could not set it up despite having lite account plan.
• API exercise does not work, although I was able to get the API key, the server did not give access to data using the key (got the 403 error).
Fortunately the course is set up in the way that you can complete it even without IBM Watson. Needed using help from Google and some extra study, but I don't count it as bad thing because it teaches basic problem solving which you need when you face problems in your real work. Low score given because of all the technical issues with assignments.
This course was very difficult and confusing to navigate through as the screenshots for certain instructions were old and I'm not entirely sure I completed all the labs that are offered here because there is only one lab you can complete on the regular jupyter notebook and not in your ibm cloud watson thing. One of the python labs instructions are too old and need to be updated so that it works properly, read the newly updated documentation for the exchange rates api on APILayer to figure that out. Highly disappointed with this project course
Not everything explained in as much detail as it could be. Some instructions out of date and some issues with IBM cloud error messages.
Complete waste of time, got stuck with running codes as I am unable to use watson studio.
Error 400.
Many courses don't work :(
The hands on lab is a little bit challenging. You need to check previous courses or search the internet to find the best solutions to solve the puzzle. But that's very helpful to make your knowhow solid.
This project helped me to review the previous course Python for Data science