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Kursteilnehmer-Bewertung und -Feedback für Datenanalyse mit Python von IBM

4.6
6,220 Bewertungen
776 Bewertungen

Über den Kurs

Learn how to analyze data using Python. This course will take you from the basics of Python to exploring many different types of data. You will learn how to prepare data for analysis, perform simple statistical analysis, create meaningful data visualizations, predict future trends from data, and more! Topics covered: 1) Importing Datasets 2) Cleaning the Data 3) Data frame manipulation 4) Summarizing the Data 5) Building machine learning Regression models 6) Building data pipelines Data Analysis with Python will be delivered through lecture, lab, and assignments. It includes following parts: Data Analysis libraries: will learn to use Pandas, Numpy and Scipy libraries to work with a sample dataset. We will introduce you to pandas, an open-source library, and we will use it to load, manipulate, analyze, and visualize cool datasets. Then we will introduce you to another open-source library, scikit-learn, and we will use some of its machine learning algorithms to build smart models and make cool predictions. If you choose to take this course and earn the Coursera course certificate, you will also earn an IBM digital badge. LIMITED TIME OFFER: Subscription is only $39 USD per month for access to graded materials and a certificate....

Top-Bewertungen

RP

Apr 20, 2019

perfect for beginner level. all the concepts with code and parameter wise have been explained excellently. overall best course in making anyone eager to learn from basics to handle advances with ease.

OA

Jul 13, 2018

I have been looking for a very non-complicated course on data analysis and I hit the Jackport with this course! Very simplified and explanatory. You should definitely take the course

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701 - 725 von 769 Bewertungen für Datenanalyse mit Python

von Felix S

Jul 01, 2019

Material to learn data analysis was very good but had quite a few bugs. It was very annoying to review the assignment of a peer because it is not possible to zoom into the screenshot. Furthermore did I need to flag a person because he had copied screenshots and his notebook was empty or only with screenshots but I was still required to review a second person to complete the course.

von Pulkit D

Jun 29, 2019

Please update and explain Rigid Regression a little more

von Riddhima S

Jul 08, 2019

la lala la la laa aaa

von Roberto B

Jul 10, 2019

I'm not convinced that this is a great way to learn, I just feel there needs to be a better way of learning this than the approach this course takes, I kind of learned the python commands but I'm not sure I understand how to apply them in the real world. We'll see

von Nilanjana

Jul 12, 2019

More examples and code examples needed

von Naveen B

Jul 13, 2019

Some of the codes shown in the videos had minor errors. Also, a bit more explanation for function (in statistics terms) would have helped in having a better understanding.

von Alejandro A S

Jul 25, 2019

Experimented a lot of problems to complete the assignment

von Hao Z

Aug 12, 2019

IBM Cloud is difficult to use.

The generated link of notebook will not share the latest version, if you click the share icon before editing the notebook.

von Rosana R M

Aug 13, 2019

The course is too long. The material should be divided and explained more detailed.

von Arjun S C

Aug 14, 2019

Lots of bugs and errors. No instructors reply on the discussion forum.

von Ashwin G

Apr 26, 2019

Too fast and could have included more examples.

von Jessica B

Jun 14, 2019

Good content, but lots of typos. The outsourcing is extremely evident.

von Bjoern K

Jun 14, 2019

Week 4 is somewhat hard to follow - Here, an overview over the different concepts would really help

von Alexander P

Jun 14, 2019

Lots of spelling and grammatical errors that made it difficult to understand some of the material. It is otherwise an interesting course.

von Kristen P

Aug 19, 2019

The work in this course was incredibly interesting. However, there are many errors and the forums went for over a week without response to questions...It seems hastily put together.

von Lyn S

Aug 16, 2019

It's difficult to rate this course, because based on other courses in the data analysis program I had low expectations. I am not sure this is good for a beginner, very poorly explained, the person who wrote it is knowledgeable, but he is not a teacher. You will struggle a lot if you don't already know a fair amount. I had to go to third party internet sources to understand a few things. But, this is pretty cheap and easy. I was looking to learn and to show a credential certificate, this supplies the latter, but not so much the former. The most disappointing issue is the time we have to spend with easily fixable issues, such as code not running, no upload buttons for some test answers. You have to search thru a lot of other discussion issues to find out what to do - after spending hours trying to figure out on your own - very disrespectful. I am ok with typos, but it does show the entire thing is very sloppy.

von Pedro F

Aug 22, 2019

Little bit confusing, unstructured and not easy to follow. Material inside is good though, but the course needs to be improved.

von Filipe S M G

Aug 24, 2019

Good introductory course on Data Analysus with Python. Since the course is short, the functions and concepts are explained very quickly. There are also many mistakes in the slides, notebooks and even in the final assignment.

von Abhishek K

Aug 26, 2019

Model creation and analysis part are too short, should have more details to understand the concepts better.

von Sathiya P

Aug 27, 2019

Nicely thought, but I felt concepts like Decision trees, Random forest were missing

von Dominic M L C L

Sep 16, 2019

Too many errors in the code and explanations. Makes it very difficult to understand which is the right procedure/conclusion.

von Appa R M

Oct 24, 2019

The kernal is stuck for some questions and its annoying

von Brett W

Sep 17, 2019

While the lecture material is well presented and certainly can be followed, the slides are littered with spelling mistakes, and many in important places (code that couldn't run as displayed.) Even the final assignment had formatting issues, and without the discussion forums suggesting removing the confidence interval, it was taking an excessively long time to run. These are generally minor issues that can be ignored, but as a mass, they are embarrassing at best.

von Ying W O

Sep 27, 2019

There are lots of typos in the labs and assignments, which can be frustrating. I expect better quality from IBM. Content is great and easy to understand nevertheless.

von Sachin L

Sep 26, 2019

More examples and detailed explanation