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.
Data Science Methodology
Data Science Methodology
This course is part of multiple programs.
Instructors: Alex Aklson
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4.6
(20,480 reviews)
Beginner level
Recommended experience
Flexible schedule
Approx. 6 hours
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What you'll learn
Describe what a data science methodology is and why data scientists need a methodology.
Apply the six stages in the Cross-Industry Process for Data Mining (CRISP-DM) methodology to analyze a case study.
Evaluate which analytic model is appropriate among predictive, descriptive, and classification models used to analyze a case study.
Determine appropriate data sources for your data science analysis methodology.
Skills you'll gain
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Assessments
11 assignments
Taught in English
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There are 4 modules in this course
In this module, you will discover what makes data science interesting, learn what a data science methodology is, and why data scientists need a data science methodology. Next, you’ll gain more in-depth knowledge of the first two data science methodology stages: Business Understanding and Analytic Approach. You’ll discover how to identify considerations and steps needed to define the data requirements for decision tree classification during the Data Requirements stage. Next, learn about the processes and techniques data scientists use to assess data content, quality, and initial insights and how data scientists manage data gaps. Round out this week with practical hands-on experience learning how to approach the Business Understanding and the Analytic Approach stage tasks and the Data Requirements and Collection stage tasks for any data science problem.
What's included
6 videos2 readings4 assignments1 app item7 plugins
In this module, you will learn what data scientists do when their tasks and goals are to understand, prepare, and clean the data. You’ll examine the purposes, characteristics, and goals of the data modeling process. You’ll also explore how to prepare a data set by handling missing, invalid, or misleading data. Then check out the hands-on labs where you can gain experience completing tasks relevant to the Data Understanding, Data Preparation, and Modeling and Evaluation stages. You’ll be able to apply the skills you learn to future data science problems.
What's included
6 videos4 assignments2 app items4 plugins
When you complete this module, you’ll be able to describe the deployment and feedback stages of the data science methodology. You’ll learn how to assess a data model’s performance, impact, and readiness. You’ll be able to identify the stakeholders who usually contribute to model refinement. You’ll also be able to explain why deployment and feedback should be an iterative process. To complete your hands-on lab experience, you’ll devise a business problem to solve using data related to email, hospitals, or credit cards. You’ll demonstrate your understanding of data science methodology by applying it to a given problem. You’ll construct responses that address each phase of the CRISP-DM based on a chosen business problem. After submitting your work, you’ll evaluate your peers’ final projects and provide constructive ideas and suggestions that fellow learners can apply right away.
What's included
4 videos2 assignments2 plugins
Before completing your final project, learn how CRISP-DM data science methodology compares to John Rollins’ foundational data science methodology. Then, apply what you learned to complete a peer-graded assignment using CRISP-DM data science methodology to solve a business problem you define. You'll first take on both the client and data scientist role and describe how you would apply CRISP-DM data science methodology to solve the business problem. Then, take on the role of a data scientist and apply your knowledge of CRISP-DM data methodology stages to describe how you would solve the business problem. After you submit your assignment, you'll grade the assignment of one peer who is enrolled in this session. Let's get started!
What's included
1 video4 readings1 assignment1 peer review1 plugin
Offered by
Recommended if you're interested in Data Analysis
University of California San Diego
Fundação Instituto de Administração
Coursera Instructor Network
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Learner reviews
4.6
20,480 reviews
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Showing 3 of 20480
HV
4
Reviewed on May 16, 2021
A bit more complex than what I would have hoped, but the material is still digestible. I think this course could be improve if the lecturer slow down a bit and spend more time on each topic
JG
5
Reviewed on Nov 29, 2019
This was a clear and concise overview of the methodology and using the case study really helped (although sometimes it got a bit advanced considering this comes before actually learning models).
DP
5
Reviewed on Aug 5, 2020
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.
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