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Data Analysis with Python, IBM

4.6
Оценки: 1,529
Рецензии: 205

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: TA

Feb 10, 2019

Really this course shows the full path to master the Data Analysis with Python. This path is short, helpful, and rich information their. thank you at all and thank you Coursera

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Рецензии: 205

автор: Bakyt Niiazov

Feb 15, 2019

Very useful. Probably the most useful in the series (Professional Data Science Certificate)

автор: Sisir Kovuri

Feb 15, 2019

Highly technical and complex in nature. Difficult for people just starting out with data science. The hands-on labs are more useful than the videos themselves. The quizzes in between videos felt a bit too easy and mostly comprised of examples (as questions) in the videos themselves.

автор: Yuncheng yang

Feb 15, 2019

Points are made very clear in the lectures and lab, well constructed course for beginners

автор: sergey kutenko

Feb 14, 2019

imho, this course must include final assignment to improve modeling skills in python.

автор: Damian Dyrka

Feb 13, 2019

There are some mistakes in the course (wrong transcryptions, missing cells in LAB).

The material is quite difficult and more explanation / exercises would be needed.

There is no assignment at the end of the course which I consider as minus.

автор: Gerhard Engelbrecht

Feb 12, 2019

Copy of videos, not a fan of tools used in labs

автор: Neo Bai

Feb 11, 2019

Data visualization was taught in details in course 7 and regression was taught in course 8. With no backgrounds, the codes in this course are scaring pe

автор: Mark Hanson

Feb 10, 2019

Pretty dry material. Hard topic to teach since the process really comes from experience. Could stand to focus a bit more on ways to explore and clean data. Not bad though.

автор: Peter Antonaros

Feb 10, 2019

Too many fine mistakes in the lectures. Confusing for new learners when the math is wrong or the python syntax is wrong.

автор: Tareq Abufayad

Feb 10, 2019

Really this course shows the full path to master the Data Analysis with Python. This path is short, helpful, and rich information their. thank you at all and thank you Coursera