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Data Science Methodology
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Data Science Methodology

Master the essential CRISP-DM methodology and foundational approaches to solve real-world data science problems effectively.

Course Cost

Free course

Beginner

Skill Level

5 Hours

Self-paced Video lessons

This course cannot be purchased separately - to access the complete learning experience, graded assignments, and earn certificates, you'll need to enroll in the full IBM Data Science Professional Certificate program. You can audit this specific course for free to explore the content, which includes access to course materials and lectures. This allows you to learn at your own pace without any financial commitment.

4.6

3,10,844 Enrolled

English

4.6

3,10,844 Enrolled

English

What you'll learn

  • Apply the six stages of CRISP-DM methodology

  • Determine appropriate analytic approaches for problems

  • Identify and evaluate data sources effectively

  • Develop systematic problem-solving strategies

  • Create effective data science solutions

Skills you'll gain

Data Science
CRISP-DM
Data Mining
Methodology
Business Understanding
Data Analysis
Model Deployment
Data Preparation
Model Evaluation
Data Storytelling

This course includes:

1 Hours PreRecorded video

11 assignments

Access on Mobile, Tablet, Desktop

FullTime access

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Share your certificate with prospective employers and your professional network on LinkedIn.

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Top companies offer this course to their employees

Top companies provide this course to enhance their employees' skills, ensuring they excel in handling complex projects and drive organizational success.

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There are 4 modules in this course

This foundational course introduces learners to key data science methodologies, focusing on the CRISP-DM (Cross-Industry Process for Data Mining) framework. Students learn the systematic approach to data science projects from business understanding to deployment and feedback. The curriculum covers essential stages including problem formulation, data requirements, preparation, modeling, evaluation, and deployment. Through hands-on labs using Python in Jupyter Notebooks, learners apply these methodologies to real-world scenarios.

From Problem to Approach and From Requirements to Collection

Module 1 · 1 Hours to complete

From Understanding to Preparation and From Modeling to Evaluation

Module 2 · 1 Hours to complete

From Deployment to Feedback and Final Evaluation

Module 3 · 0 Hours to complete

Final Project and Assessment

Module 4 · 1 Hours to complete

Fee Structure

Reviews

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Faculties

These are the expert instructors who will be teaching you throughout the course. With a wealth of knowledge and real-world experience, they're here to guide, inspire, and support you every step of the way. Get to know the people who will help you reach your learning goals and make the most of your journey.

Data Science Methodology

Beginner

Skill Level

5 Hours

Self-paced Video lessons

Course Cost

Free course

Completion

CERTIFICATE

Frequently asked Questions

Below are some of the most commonly asked questions about this course. We aim to provide clear and concise answers to help you better understand the course content, structure, and any other relevant information. If you have any additional questions or if your question is not listed here, please don't hesitate to reach out to our support team for further assistance.