Data Science Methodology Fundamentals
Master the systematic approach to data science problem-solving, from problem identification to model deployment and feedback analysis.
Master the systematic approach to data science problem-solving, from problem identification to model deployment and feedback analysis.
This comprehensive course teaches the fundamental methodology and practices of data science for effective problem-solving. Students learn a structured approach to tackling data science challenges, including identifying problems, collecting and analyzing relevant data, and building appropriate models. The curriculum emphasizes the Cross-Industry Process for Data Mining (CRISP-DM) methodology and its practical application in business scenarios. Through hands-on practice, participants develop skills in managing and analyzing big data while understanding the critical stages of the data science lifecycle.
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Instructors:
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What you'll learn
Master the systematic approach to data science problem-solving
Understand and apply the CRISP-DM methodology effectively
Identify appropriate data sources for analysis projects
Develop skills in managing and analyzing big data
Apply data science methods to real-world business scenarios
Skills you'll gain
This course includes:
PreRecorded video
Graded assignments, exams
Access on Mobile, Tablet, Desktop
Limited Access access
Shareable certificate
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Top companies provide this course to enhance their employees' skills, ensuring they excel in handling complex projects and drive organizational success.
Module Description
This course provides a comprehensive introduction to data science methodology and its practical applications. Students learn the systematic approach to solving data science problems, including problem identification, data collection and analysis, model building, and deployment feedback analysis. The curriculum emphasizes the CRISP-DM methodology while teaching students how to determine appropriate data sources and apply analytical techniques effectively. The course focuses on developing practical skills that can be applied to real-world business challenges.
Fee Structure
Instructor
Dr. Alex Aklson: Crafting Data-Driven Solutions and Innovating Smart Health Systems at IBM
Dr. Alex Aklson is a data scientist in IBM Canada’s Digital Business Group, where he has contributed to innovative projects, including the development of a smart system to detect early signs of dementia by analyzing walking speed and home activity patterns in older adults. Prior to IBM, Alex worked at Datascope Analytics in Chicago, where he crafted data-driven solutions using a human-centered approach. He holds a Ph.D. in Biomedical Engineering from the University of Toronto.
Testimonials
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4.5 course rating
16 ratings
Frequently asked questions
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