Вернуться к Introduction to Data Science in Python

4.5

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Оценки: 23,103

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Рецензии: 5,182

This course will introduce the learner to the basics of the python programming environment, including fundamental python programming techniques such as lambdas, reading and manipulating csv files, and the numpy library. The course will introduce data manipulation and cleaning techniques using the popular python pandas data science library and introduce the abstraction of the Series and DataFrame as the central data structures for data analysis, along with tutorials on how to use functions such as groupby, merge, and pivot tables effectively. By the end of this course, students will be able to take tabular data, clean it, manipulate it, and run basic inferential statistical analyses.
This course should be taken before any of the other Applied Data Science with Python courses: Applied Plotting, Charting & Data Representation in Python, Applied Machine Learning in Python, Applied Text Mining in Python, Applied Social Network Analysis in Python....

PK

9 мая 2020 г.

The course had helped in understanding the concepts of NumPy and pandas. The assignments were so helpful to apply these concepts which provide an in-depth understanding of the Numpy as well as pandans

SI

15 мар. 2018 г.

overall the good introductory course of python for data science but i feel it should have covered the basics in more details .specially for the ones who do not have any prior programming background .

Фильтр по:

автор: Karl R

•24 сент. 2020 г.

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.

автор: Melissa C

•27 февр. 2017 г.

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.

автор: Dibyajyoti D

•6 авг. 2020 г.

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.

автор: Chong O K

•4 окт. 2020 г.

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.

автор: Alan E

•21 нояб. 2017 г.

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.

автор: Pieter J S M

•9 июня 2019 г.

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!

автор: Ajit S

•28 мая 2017 г.

This is a very helpful course. The main advantage is that you will learn a lot of new ways to do operations over data. And this is an intermediary course that assumes that you already know about statistics, mathematics behind data. From my experience I want to tell that if you are taking this course don't just rely on the video this course provide (however videos gives you full context on the work that has to be done), you have to do your own research and reading from external sources too.

автор: Bala G

•21 нояб. 2016 г.

This course was excellent...I first found the course odd since the instructor went through the material quite quickly in the lectures. It took me a while to figure out that the material was available as a course download. Once I found that it was easy to follow along with the instructor...need two monitors ideally for this to work. If you cannot step through the Jupyter notebook as the instructor goes through the material, you will be lost...and you will not get the most out of the class.

автор: Vinayak N

•16 февр. 2019 г.

Awesome course for anyone looking to venture into the field of Data Science. The instructor puts forth various concepts lucidly and concisely without any irrelevant extraneous details. Beware though, if you are pursuing this for the sake of learning statistics, you might be disapppointed. The instructor adopts more of a tool-based approach teaching you pandas to solve your problems the way you want to. That said, kudos to Coursera and U Michigan for putting this course together.

автор: Nikolaos K

•27 дек. 2020 г.

Excellent course. Very well-paced, with good examples and useful code. The instructor seems very knowledgable in the subject meterial, and communicates perfectly. Some basic knowledge of python, data structures and maths is required in my opinion, to have the courser work well. Quizes and assignments are challenging, but that is good for a course, it makes you research the subjects on your own and go beyond the lectures. All in all, a very good course that I would recommend.

автор: Kevin B

•16 июня 2019 г.

Great course overall. I feel like the final output for run_ttest is incorrect though. There are two regions that belong in the university town buckets, but are missed due to capitalization differences, Illinois -DeKalb and Florida-DeLand. I made the region lowercase before merging and got (False, 0.011132653194002319, 'university town') as the output. When my grade came back as 5/10 I knew if I removed the cast to lowercase it would be correct. Thank you for everything!

автор: Sudev N

•10 сент. 2020 г.

Very useful course. I would suggest more assignments as they are where you do the brunt of the learning (by looking things up, and learning WHAT to look up), the lectures are very bare bones in my opinion. This could be intentional, but I still came out of the course with a solid understanding of pandas and how to manage data in a real-world environment. A great touch was making us import things straight from wikipedia, etc as it is representative of real data. THANK YOU!

автор: Derrick G

•13 авг. 2019 г.

There are plenty of how-to videos and tutorials on YouTube to get caught up on the basics, and there are lots of Data Science classes on coursera and other platforms that go deep into theory and stats. This is the first Data Science in Python class that I have found to strike a balance of practical and theoretical at an intro level.

The video and audio quality is great as well. Start here. Then move on to the deeper and more specific courses on stats or machine learning.

автор: Mastaneh r b

•6 февр. 2020 г.

I like this course!

If you have little experience in using python commands to work with data, go for this course!

Actually, it has assignments within which you learn a lot more that the videos. It has notebooks of the commands thaught in videos, that you can follow and return to when necessary.

There is a discussion forum that lets you see what answers are available for your questions.

Overall! I'm quiet happy I finished the first course!

Can't wait to start the next ^_^

автор: Paresh D

•11 июня 2019 г.

Fantastic course.I have learned a lot.I really enjoyed playing with various data frames in the assignment.Discussion Forums were very useful.Special Thanx to Yusuf,who used to help me in clearing my doubts.The only thing with which I am dissatisfied is the teaching instructor.He has enough knowledge and is a genius,but his teaching style is mundane.I wish this course was taught by Dr.Chuck.I really like the way Dr.Chuck taught Python for Everybody specialization

автор: Abdul S

•21 авг. 2017 г.

One of the best courses I've attended in the Coursera. The programming assignments were tough. But I think in some assignments instructions were not clear or were not available at single place. Ideally all the instructions/hints etc should be placed inside the notebook for assignments.

Overall the course challenged my learning. And I needed to do lots of googling to look into stackoverflow, pandas documentation etc to reach the correct answers. Awesome course.

автор: Davide C

•26 нояб. 2020 г.

Great course! You will learn using the Numpy and Pandas libraries, together with the Python Regular Expressions (Regex). The third and fourth assignments are challenging and put you in front of (relatively) complex real databases (which is great) and go also beyond the material discussed in the course, but this pushes you to search on Stack Overflow and read the libraries' documentation, which is a great way to learn. I would definitely recommend the course!

автор: Silvia P

•22 апр. 2020 г.

Wow! I've been looking for a solid course teaching me how to use Python applied to Data Science and not giving me only theoretical notions. I've struggled at times, but it's this struggle that let me master all the commands. When I felt stuck on a problem I would go in the Discussion section and read the post of my fellow course mates and I found helpful insights to apply in my code. I can't wait to go on with the whole Specialization! Keep up the good work!

автор: Moein T

•6 нояб. 2020 г.

This was indeed an amazing introduction to Data Science. I should accept that I found the assignments kind of challenging and had to spend lots of time on some of them, but that would only make you learn more. Also, a proper background with Python is required for this course. Make sure you have enough background with Python Data Structures. If not, I'd recommend the following course first:

Python Data Structures - Charles severance

Good luck on your journey!

автор: Yarema M

•26 мая 2020 г.

This course has been excellent for helping me understand the core functionality of the NumPy and Pandas modules. The lectures are very detailed and implore you to delve deeper into the subject on your own. My favorites, though, were the end-of-week assignments. These let me assume the role of a real data scientist, digging in huge amounts of data to uncover the important details. Overall, this course is a great introduction to the study of Data Science.

автор: Shreyashi G

•12 февр. 2019 г.

This course is a high level and precise introduction to the python programming skills necessary for any data analysis exercise. It is adequately paced which is great for anyone who has some prior knowledge of python. The assignments are particularly challenging which I thoroughly enjoyed. The lectures would effectively introduce a concept and the assignment to follow would test the understanding thoroughly - a structure which in my opinion worked great!

автор: Zhao J

•24 февр. 2017 г.

It's a great course! The assignment is amazing! It takes me several hours or even one or two days to finish it. I have to say the assignment is really valuable. Professor Brook is also great and the video is greatly filmed. It's worth taking if you have already learned some of python and want to know more about python in data analysis. I have already learned the book Learn Python the Hard Way and played with python for a while before I take this course.

автор: Gustavo W

•19 нояб. 2016 г.

Really good course BUT be prepared for a very fast pace of the lectures. I'd placed in the "advance beginner" or "intermediate low" level, therefore, you need to have previous knowledge of Python. As in College, lectures and assignments are somewhat related, but you will spend some additional time investigating by yourself to get the appropriate responses. Again, just like College where Lectures are level 2-3 but assignments are level 7-8 (out of 10).

автор: Jianjun Z

•11 апр. 2020 г.

The course is very good, but the assignment is a challenge for me. It took me a long time to finish my homework, but I learned a lot from it. Basically, the professor talked about the most important knowledge points, and the rest learned independently by extensively searching for information. I have learned a lot, but I am a bit worried about whether to choose the next course. After all, the time I can allocate to my studies is not that sufficient.

автор: Chirag R

•27 июня 2019 г.

The course was pretty exhaustive and I felt like I learnt everything that this course intended to teach me. The assignments were pretty tough, given that I had no experience of Python before this, but that's down to me for not taking the "Python for Everyone" course, as recommended by our professor. A few more interactive and intermediate level problems could go a long way in making the course takers better skilled and equipped with Python.

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