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There are 4 modules in this 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.
In this module, you will discover what makes data science engaging, learn what a data science methodology is, and understand why it is essential for data scientists. You will then gain deeper insight into the first two stages of the data science methodology: Business Understanding and Analytic Approach.
You will explore the key considerations and steps required to define data requirements, including those needed for decision tree classification during the Data Requirements stage. You will also learn about the processes and techniques data scientists use to assess data content, quality, and initial insights, as well as how they manage data gaps.
The module concludes with hands-on practice, where you will apply Business Understanding and Analytic Approach stage tasks, along with Data Requirements and Data Collection stage tasks, to real-world data science problems.
What's included
6 videos9 readings5 assignments1 app item
Show info about module content
6 videos•Total 22 minutes
Course Introduction•3 minutes
Data Science Methodology Overview•4 minutes
Business Understanding•5 minutes
Analytic Approach•3 minutes
Data Requirements•4 minutes
Data Collection•3 minutes
9 readings•Total 85 minutes
Helpful Tips for Course Completion•5 minutes
Course Syllabus•10 minutes
Analytic Approach Based on the Question Type•10 minutes
Business Understanding: Identifying Relevant Questions•10 minutes
Analytical Approach: Identifying the Pattern to Address the Question•10 minutes
Lesson Summary: From Problem to Approach•10 minutes
Glossary: From Problem to Approach•10 minutes
Lesson Summary: From Requirements to Collection•10 minutes
Glossary: From Requirements to Collection•10 minutes
5 assignments•Total 57 minutes
Applying Business Understanding and Analytic Approach•15 minutes
Practice Quiz: From Problem to Approach•6 minutes
Practice Quiz: From Requirements to Collection •6 minutes
Graded Quiz: From Problem to Approach•15 minutes
Graded quiz: From Requirements to Collection •15 minutes
1 app item•Total 15 minutes
From Requirements to Collection•15 minutes
From Understanding to Preparation and From Modeling to Evaluation
Module 2•2 hours to complete
Module details
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 readings4 assignments2 app items
Show info about module content
6 videos•Total 22 minutes
Data Understanding•3 minutes
Data Preparation - Concepts•3 minutes
Data Preparation - Case Study•4 minutes
Modeling - Concepts•3 minutes
Modeling - Case Study•4 minutes
Evaluation•4 minutes
4 readings•Total 40 minutes
Lesson Summary: From Understanding to Preparation•10 minutes
Module 2 Lesson 1 Glossary: From Understanding to Preparation•10 minutes
Lesson Summary: From Modeling to Evaluation•10 minutes
Glossary: From Modeling to Evaluation•10 minutes
4 assignments•Total 42 minutes
Practice Quiz: Lesson 1 From Understanding to Preparation•6 minutes
Practice Quiz Lesson 2: From Modeling to Evaluation•6 minutes
Graded Quiz Lesson 1: From Understanding to Preparation•15 minutes
Graded Quiz Lesson 2: From Modeling to Evaluation•15 minutes
2 app items•Total 40 minutes
Hands-on Lab: From Understanding to Preparation•20 minutes
Hands-on Lab: From Modeling to Evaluation•20 minutes
From Deployment to Feedback and Final Evaluation
Module 3•1 hour to complete
Module details
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, your submission will be AI-graded, providing fast, precise, and constructive feedback.
What's included
4 videos2 readings2 assignments
Show info about module content
4 videos•Total 13 minutes
Deployment•4 minutes
Feedback •3 minutes
Storytelling•3 minutes
Course Summary•3 minutes
2 readings•Total 20 minutes
Module 3 Summary : From Deployment to Feedback•10 minutes
Module 3 Glossary: From Deployment to Feedback•10 minutes
2 assignments•Total 21 minutes
Practice Quiz: From Deployment to Feedback •6 minutes
Module 3 Graded Quiz: From Deployment to Feedback•15 minutes
Final Project and Assessment
Module 4•3 hours to complete
Module details
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 an AI-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, your submission will be AI-graded, providing fast, precise, and constructive feedback. Let's get started!
What's included
1 video6 readings1 assignment1 app item
Show info about module content
1 video•Total 5 minutes
Introduction to CRISP-DM•5 minutes
6 readings•Total 29 minutes
Final Assignment Overview•10 minutes
Reading: Final Project Submission Guidelines and Deliverables•10 minutes
Review what you learned•3 minutes
Congratulations and Next Steps•2 minutes
Thanks from the Course Team•2 minutes
IBM Digital Badge•2 minutes
1 assignment•Total 60 minutes
Final Quiz•60 minutes
1 app item•Total 60 minutes
Option 1: AI Graded - Final Project: Submission and Evaluation•60 minutes
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4.6 (2,411 ratings)
Alex Aklson
IBM
21 Courses•1,434,735 learners
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Learner reviews
4.6
21,070 reviews
5 stars
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4 stars
21.04%
3 stars
4.79%
2 stars
1.51%
1 star
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GO
4·
Reviewed on May 4, 2021
Great course for understanding data science and data related methodologies. Some parts that included machine learning algorithms confused me a little bit, but a little google search made it clear.
H
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
P
PA
5·
Reviewed on Apr 14, 2020
It's a very good course for getting the basic idea of the methodology of data science. It will help to get grip on how to proceed to a problem in a systematic manner for getting good results.
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