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学生对 IBM 提供的 Data Science Methodology 的评价和反馈

4.6
20,981 个评分

课程概述

If there is a shortcut to becoming a Data Scientist, then learning to think and work like a successful Data Scientist is it. In this course, you will learn and then apply this methodology that you can use to tackle any Data Science scenario. You’ll explore two notable data science methodologies, Foundational Data Science Methodology, and the six-stage CRISP-DM data science methodology, and learn how to apply these data science methodologies. Most established data scientists follow these or similar methodologies for solving data science problems. Begin by learning about forming the business/research problem Learn how data scientists obtain, prepare, and analyze data. Discover how applying data science methodology practices helps ensure that the data used for problem-solving is relevant and properly manipulated to address the question. Next, learn about building the data model, deploying that model, data storytelling, and obtaining feedback You’ll think like a data scientist and develop your data science methodology skills using a real-world inspired scenario through progressive labs hosted within Jupyter Notebooks and using Python....

热门审阅

AG

May 13, 2019

This is a proper course which will make you to understand each and every stage of Data science methodology. Lectures are well enough to make you think as a data scientist. Thank you fr this course :)

JM

Feb 26, 2020

Very informative step-by-step guide of how to create a data science project. Course presents concepts in an engaging way and the quizzes and assignments helped in understanding the overall material.

筛选依据:

1751 - Data Science Methodology 的 1775 个评论(共 2,665 个)

创建者 pawan s

Apr 11, 2020

....

创建者 Camilo Q

Apr 4, 2020

Nice

创建者 Pankaj S

Mar 11, 2020

good

创建者 Priyanka R

Mar 10, 2020

Good

创建者 Priyansh P

Feb 26, 2020

good

创建者 KOPPARTHI H H

Feb 24, 2020

good

创建者 Rushikesh J

Nov 30, 2019

Best

创建者 Yuliana K

Sep 7, 2019

exce

创建者 Deleted A

Sep 4, 2019

good

创建者 Prabhu M

Aug 31, 2019

good

创建者 Inggriani W

Jul 17, 2019

none

创建者 SAURABH P

Jul 1, 2019

Nice

创建者 Senthil R B

Jun 24, 2019

Good

创建者 Miriam R

Mar 27, 2019

good

创建者 Moulay A E T

Dec 15, 2023

ok

创建者 Talha A

Sep 1, 2019

<3

创建者 Ritwik G

Feb 20, 2019

NA

创建者 Lobsang A B R

Oct 1, 2025

.

创建者 Bryan D A F

Sep 22, 2025

,

创建者 Jhonny A M T

Dec 9, 2024

.

创建者 Nithyasri S

Apr 15, 2022

创建者 Manoj N

Aug 31, 2021

创建者 Pradeep K S

May 25, 2020

5

创建者 James Y

Jan 28, 2020

P

创建者 Samuel W J

Apr 24, 2021

First, I would like to thank everyone at IBM for putting this course together. It’s like ordering a meal at a famous, beautiful, expensive restaurant. The customer orders the food and then they get what they ordered. However, they didn’t see and hear Gordon Ramsey in the kitchen and the fight it took to bring the best dish to you. When we as students come to the course, everything is already prepared. We don’t see the hours of hard work and extreme attention to details that go into it. So thank you all for what you do behind the scenes. I’ve learned a lot so far and I really can’t wait to keep going. In this course I really like the simple approach it took in the beginning and the illustrations and comparisons to cooking. It made it really easy because who can’t identify with wanting to have a good meal? Hopefully I can add a small touch about what I’ve observed to the vast knowledge of IBM. In the course, it was explained well what the data methodology is and then how that knowledge was applied in the case study. However, it was difficult to understand why that knowledge was applied the way it was. It felt like a math equation was shown on the board and then right after that, the answer was shown, but what was missing was the steps in between of why that was the answer. Another part that made it difficult to fully comprehend was the labs. I was looking forward to actually working with data, but everything was already there and it felt like the answers were shown to me without helping me understand why this conclusion had been reached. It’s easy to pick out things to work on because nobody is 100% free from flaws and really who am I to attempt to suggest anything to an industry that doesn’t need my viewpoint? I do hope that this was received well. This course still was a very hearty meal and left me wanting more. I look forward to the next course! Thank you again for all of your hard work!