IBM Data Science Methodology
About this Course
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.
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!
What you will learn
Describe what a methodology is and why data scientists need a methodology.
Describe the six stages in the Cross Industry Process for Data Mining (CRISP-DM) methodology including Business Understanding and Data Understanding.
Describe some of the use cases for different analytic models and approaches, such as Predictive, Descriptive, and Classification models.
Explain the importance of identifying the correct sources of data for your data science project.
Skills you will gain
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.