Data Science Methodology

IBM via Coursera

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Provider

Coursera
Pricing

Paid Course
Languages

English
Certificate

Certificate Available
Effort

8 hours 50 minutes
Sessions

Self-Paced
Level

Beginner
Subtitles

Arabic, French, Portuguese, Italian, German, Russian, English, Spanish, Farsi, Turkish, Hindi, Pashto, Bengali, Chinese, Hungarian, Ukrainian, Indonesian, Urdu, Kazakh, Swedish, Korean, Greek, Thai, Japanese, Azerbaijani, Polish, Dutch

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Overview

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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.

Syllabus

From Problem to Approach and From Requirements to Collection

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.

From Understanding to Preparation and From Modeling to Evaluation

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.

From Deployment to Feedback and Final Evaluation

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.

Final Project and Assessment

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!