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Learner Reviews & Feedback for Data Science Methodology by IBM

4.6
stars
20,241 ratings

About the Course

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....

Top reviews

DP

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This course covers the end to end cycle suggested framework that can be useful not only in Data Science but also in other Research Projects that manage information to create and deploy a solution.

CE

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The course is well structured but the case studies were not well elaborated, felt like I was struggling to understand. Also, Data science methodology should be taught after a course in Python.

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776 - 800 of 2,557 Reviews for Data Science Methodology

By Prashant P

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Mar 31, 2020

good understanding of Data science methodology videos and very clear

By Keith C

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Feb 4, 2020

It helped me to understand the methodology used as a data scientist.

By Jennifer I

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Jul 15, 2019

Very helpful information for organizing skill-sets related to topic.

By Marlon A Z Z

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Feb 18, 2019

Great course. They teach you very well, and gives you good examples.

By Bharath A

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Feb 2, 2022

The exercises were interesting. Thank you for your effort and time.

By Ina V

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Nov 4, 2021

I like how material was delivered to define between the components.

By I M A S

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Mar 29, 2021

Anyone intending to study data science must understand this process

By Barbara R

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Aug 20, 2020

Great class. I like how the materials is broken to into bite sizes.

By Niorys S

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Aug 9, 2020

Excellent methodology to learn and apply knowledge in Data Science.

By Atif H

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Aug 9, 2020

Nice course with basis understanding about data science methodology

By Anna C

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Apr 5, 2020

great material and instruction. enjoyed the readings and coursework

By Abhishek J

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Jul 16, 2019

This is really great that we are now familiar with the methodology.

By Konduru G

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Jun 11, 2019

Very nice course with full of theory but has hands on lab practical

By Haley T

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Jan 4, 2019

Great Material! I learned a lot about the Data Science Methodology.

By Thomas D

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Apr 10, 2022

Very concise, very appropriate structure. No unnecessary padding.

By Imam A K

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Jan 24, 2021

Great course introduces the methodology of Data Science very well.

By mnahaj h

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Dec 30, 2020

Very good content and well structured course delivery. Thank you !

By shravani g

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May 15, 2020

coursse is very nice , I got new things from this course.

THANK YOU

By Dr.M P

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Apr 4, 2020

Very good foundation for understand data science analysis methods.

By Syed F U

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Jul 28, 2019

This course is awesome and superficial for DataScientist Students.

By Nguyen V L

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Mar 4, 2019

This course gives an understanding of the methodology of analytics

By A. O

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Oct 19, 2022

Great intoductory course to Data Science processes. Very useful!

By Ahmed E

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Jul 4, 2021

It really amazing course, It organize mind set for data handling.

By Jingxia Z

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Mar 11, 2020

Well demonstrating the whole process of Data Science Methodology!

By Sanzio G N

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Mar 4, 2020

Great course. Much better and more meaningful than the first two.