Data Science Methodology (Coursera)

Offered by IBM,
Data Science Methodology (Coursera)

Despite the recent increase in computing power and access to data over the last couple of decades, our ability to use the data within the decision making process is either lost or not maximized at all too often, we don't have a solid understanding of the questions being asked and how to apply the data correctly to the problem at hand.

Class Deals by MOOC List - Click here and see Coursera's Active Discounts, Deals, and Promo Codes.

This course has one purpose, and that is to share a methodology that can be used within data science, to ensure that the data used in problem solving is relevant and properly manipulated to address the question at hand.
Accordingly, in this course, you will learn:

  • The major steps involved in tackling a data science problem.
  • The major steps involved in practicing data science, from forming a concrete business or research problem, to collecting and analyzing data, to building a model, and understanding the feedback after model deployment.
  • How data scientists think!

This course is part of multiple programs
This course can be applied to multiple Specializations or Professional Certificates programs. Completing this course will count towards your learning in any of the following programs:

Syllabus

WEEK 1
From Problem to Approach and From Requirements to Collection
In this module, you will learn about why we are interested in data science, what a methodology is, and why data scientists need a methodology. You will also learn about the data science methodology and its flowchart. You will learn about the first two stages of the data science methodology, namely Business Understanding and Analytic Approach. Finally, through a lab session, you will also obtain how to complete the Business Understanding and the Analytic Approach stages and the Data Requirements and Data Collection stages pertaining to any data science problem.

WEEK 2
From Understanding to Preparation and From Modeling to Evaluation
In this module, you will learn what it means to understand data, and prepare or clean data. You will also learn about the purpose of data modeling and some characteristics of the modeling process. Finally, through a lab session, you will learn how to complete the Data Understanding and the Data Preparation stages, as well as the Modeling and the Model Evaluation stages pertaining to any data science problem.

WEEK 3
From Deployment to Feedback
In this module, you will learn about what happens when a model is deployed and why model feedback is important. Also, by completing a peer-reviewed assignment, you will demonstrate your understanding of the data science methodology by applying it to a problem that you define.

Go to Class
MOOC List is learner-supported. When you buy through links on our site, we may earn an affiliate commission.

Related Courses

Practical Machine Learning on H2O (Coursera) Coursera
H2O.ai

Practical Machine Learning on H2O (Coursera)

In this course, we will learn all the core techniques needed to make effective use of H2O. Even if you have no prior experience of machine learning, even if your math is weak, by the end of this course you will be able to make machine learning models using a variety of algorithms. We will be using linear models, random forest, GBMs and of course deep learning, as well as some unsupervised learning algorithms.

Sep 14th 2026
5-12 Weeks
Data Science for Business Innovation (Coursera) Coursera
Politecnico di Milano,EIT Digital

Data Science for Business Innovation (Coursera)

The course is a compendium of the must-have expertise in data science for executive and middle-management to foster data-driven innovation. It consists of introductory lectures spanning big data, machine learning, data valorization and communication. Topics cover the essential concepts and intuitions on data needs, data analysis, machine learning methods, respective pros and cons, and practical applicability issues.

Sep 21st 2026
4 Weeks
Materials Data Sciences and Informatics (Coursera) Coursera
Georgia Institute of Technology

Materials Data Sciences and Informatics (Coursera)

This course aims to provide a succinct overview of the emerging discipline of Materials Informatics at the intersection of materials science, computational science, and information science. Attention is drawn to specific opportunities afforded by this new field in accelerating materials development and deployment efforts.

Sep 7th 2026
5-12 Weeks
Machine Learning Introduction for Everyone (Coursera) Coursera
IBM

Machine Learning Introduction for Everyone (Coursera)

This three-module course introduces machine learning and data science for everyone with a foundational understanding of machine learning models. You’ll learn about the history of machine learning, applications of machine learning, the machine learning model lifecycle, and tools for machine learning. You’ll also learn about supervised versus unsupervised learning, classification, regression, evaluating machine learning models, and more.

Sep 14th 2026
3 Weeks
Developing AI Policy (Coursera) Coursera
Fred Hutchinson Cancer Center

Developing AI Policy (Coursera)

AI tools are already changing how we work, and they will continue to do so for years. Over the next few years, we’re likely going to see AI used in ways we’ve never imagined and are not anticipating. This course will guide you as you lead your organization to adopt AI in a way that’s not unethical, illegal, or wrong. This course empowers you to make informed decisions and confidently create an AI policy that matches your organizational goals.

Sep 21st 2026
1 Week
Process Mining: Data science in Action (Coursera) Coursera
Eindhoven University of Technology

Process Mining: Data science in Action (Coursera)

Process mining is the missing link between model-based process analysis and data-oriented analysis techniques. Through concrete data sets and easy to use software the course provides data science knowledge that can be applied directly to analyze and improve processes in a variety of domains. Data science is the profession of the future, because organizations that are unable to use (big) data in a smart way will not survive. It is not sufficient to focus on data storage and data analysis. The data scientist also needs to relate data to process analysis.

Sep 21st 2026
5-12 Weeks
Data Science Math Skills (Coursera) Coursera
Duke University

Data Science Math Skills (Coursera)

Data science courses contain math—no avoiding that! This course is designed to teach learners the basic math you will need in order to be successful in almost any data science math course and was created for learners who have basic math skills but may not have taken algebra or pre-calculus. Data Science Math Skills introduces the core math that data science is built upon, with no extra complexity, introducing unfamiliar ideas and math symbols one-at-a-time.

Sep 24th 2026
4 Weeks
Introduction to Machine Learning (Coursera) Coursera
Duke University

Introduction to Machine Learning (Coursera)

This course will provide you a foundational understanding of machine learning models (logistic regression, multilayer perceptrons, convolutional neural networks, natural language processing, etc.) as well as demonstrate how these models can solve complex problems in a variety of industries, from medical diagnostics to image recognition to text prediction.

Sep 21st 2026
5-12 Weeks
Data Science in Health Technology Assessment (Coursera) Coursera
Genentech

Data Science in Health Technology Assessment (Coursera)

This course explores key concepts and methods in Health Economics and Health Technology Assessment (HTA) and is intended for learners who have a foundation in data science, clinical science, regulatory and are new to this field and would like to understand basic principles used by payers for their reimbursement decisions.

Sep 14th 2026
3 Weeks
Data Science in Real Life (Coursera) Coursera
Johns Hopkins University

Data Science in Real Life (Coursera)

Have you ever had the perfect data science experience? The data pull went perfectly. There were no merging errors or missing data. Hypotheses were clearly defined prior to analyses. Randomization was performed for the treatment of interest. The analytic plan was outlined prior to analysis and followed exactly. The conclusions were clear and actionable decisions were obvious. Has that every happened to you? Of course not. Data analysis in real life is messy. How does one manage a team facing real data analyses? In this one-week course, we contrast the ideal with what happens in real life. By contrasting the ideal, you will learn key concepts that will help you manage real life analyses.

Sep 7th 2026
1 Week