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Data Analysis with Python, IBM

4.6
Оценки: 754
Рецензии: 110

Об этом курсе

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....

Лучшие рецензии

автор: 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

автор: UD

Aug 06, 2018

I like this one very much. Yes, the course was difficult but easy to follow and enjoyable. Thank you for making me think a lot, and thank you for all your hardwork.

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Рецензии: 111

автор: Alireza Rafiyi

Dec 11, 2018

It was a nice and informative course, but it needs improvements and revisions. Although, we cannot expect very much from an elementary course, the contents could be offered a bit better by adding more details and more practice questions. In particular, the final sketchy parts of the course could be offered better a part of the next course (Data Visualization with Python). And, the notebooks should have better names, starting with numbers.

автор: Zayani Mandigo

Dec 11, 2018

Toughest course so far. I liked being able to visually see the statistics behind data analysis, which was much more helpful than the textbooks I had to use to earn my math degree! However, the final week was still a challenge to get through and understand.

автор: Florian Parche

Dec 11, 2018

Decent introduction to basic concepts of data analysis. However, the 'labs' and quizzes feel insufficient to practice the theoretical aspects. Further on the downside, the quality of the material in this course is quite poor. Even worse, several months after learners mention errors in the discussion forums (and partly get an instructor response), the mistakes remain in the material.

автор: Ritesh Jain

Dec 10, 2018

Good course to start with. Course deals with basics of Python, Descriptive Analysis, Exploratory Analysis and Modeling.

автор: Jyoti Devi Sankhwar

Dec 09, 2018

This course is very informative and good for beginners.Exercises are very useful.

автор: Lei

Dec 09, 2018

very good. I will continue.

автор: RAVIKUMAR M

Dec 08, 2018

Good content through out the learning, the lab notebooks are great resource to do the Hands on by ourselves. Includes each corner of the analysis methods. Good foundation course

автор: Guy Pollack

Dec 06, 2018

So many mistakes in videos and labs, including spelling errors, misnaming functions and code that causes errors.

These have been listed extensively in the course discussion forums, with some complaints from over 6 months ago, and have not been addressed

автор: Vikram Raghava

Dec 04, 2018

I felt the course isn't designed well as it takes you little fast than expected and doesn't explain all the terms! May be one has to be very good at math or revise all the topics before taking this course.

автор: Benjamin Jones

Dec 01, 2018

many mistakes throughout