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Learner Reviews & Feedback for Introduction to Data Science and scikit-learn in Python by LearnQuest

4.0
stars
31 ratings

About the Course

This course will teach you how to leverage the power of Python and artificial intelligence to create and test hypothesis. We'll start for the ground up, learning some basic Python for data science before diving into some of its richer applications to test our created hypothesis. We'll learn some of the most important libraries for exploratory data analysis (EDA) and machine learning such as Numpy, Pandas, and Sci-kit learn. After learning some of the theory (and math) behind linear regression, we'll go through and full pipeline of reading data, cleaning it, and applying a regression model to estimate the progression of diabetes. By the end of the course, you'll apply a classification model to predict the presence/absence of heart disease from a patient's health data....

Top reviews

DH

Apr 4, 2022

The topic is great, and the linkage and references provided are valuable.

The hands-on quiz should be supported with better instructions and descriptions regarding what to do.

RZ

Nov 9, 2021

meskipun agak eror dalam lab penugasan tapi alhamdulillah sudah bisa

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1 - 11 of 11 Reviews for Introduction to Data Science and scikit-learn in Python

By David H

Apr 5, 2022

The topic is great, and the linkage and references provided are valuable.

The hands-on quiz should be supported with better instructions and descriptions regarding what to do.

By Raden R A A Z

Nov 10, 2021

meskipun agak eror dalam lab penugasan tapi alhamdulillah sudah bisa

By Luca S

Mar 1, 2022

The course stars off well, but there is a steep learning curve and fast pace, with assignments often much harder than what was discussed in the classes or than what is reported in the documentation. Assignments could be written more clearly. However, the course does cover quite some basics on the field.

By Karol P

May 5, 2022

The subject is interesting and exercises are educational but there are many shortcomings. Numerous assignments are broken and some answers in quizes are wrong. In the last one I have copied author's solution and even then I was 20 points short. Additionally, more than half of materials are just links to library documentation. The examples to work with are interesting, but those are almost exclusively the ones provided with sklearn library. I've learned a lot in this course, but I have a feeling that this is because numpy, pandas and sklearn are well documented, not because this course is well made.

By JUAN J S T

Aug 19, 2021

Nice course

By Henry T

Oct 28, 2021

The Dictionary assignment in week 1 requiring 100% to pass has an obvious fault in the assessment process not allowing correct answers to be graded as correct

By Ayush T

Jan 15, 2023

This was a good learning experience for someone new to data science and scikit-learn. The resources provided in the modules were helpful and mostly relevant. Besides the course not including various other models from scikit-learn, it was a fruitful experience.

By Andrei G

Nov 28, 2021

Good introduction. A bit too short for a 4-week course. The autograder is not very good, and some solutions are wrong.

By Celine T

Jan 31, 2022

It could be better if we can see where we did wrong after each assignment. Good and well-paced course otherwise

By Maarten v d S

Nov 14, 2022

pretty nice, even though the autograders were giving me a hard time on the labs. Good to brush up my panadas and scikit .

By Lewis N

Nov 9, 2022

- A lot of typos in documents

- Concepts and features were poorly explained; the majority of the course is documents and files being thrown on the table for learners to crawl through with hardly any further instructions, making the learning curve super steep.