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Data Science Methodology

IBM via Coursera


If there is a shortcut to becoming a Data Scientist, then learning to think and work like a successful Data Scientist is it. Most of the established data scientists follow a similar methodology for solving Data Science problems. In this course you will learn and then apply this methodology that can be used to tackle any Data Science scenario.

The purpose of this course 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 practicing data science
- Forming a business/research problem, collecting, preparing & analyzing data, building a model,
deploying a model and understanding the importance of feedback
- Apply the 6 stages of the CRISP-DM methodology, the most popular methodology for Data Science and Data Mining problems
- How data scientists think!

To apply the methodology, you will work on a real-world inspired scenario and work with Jupyter Notebooks using Python to develop hands-on experience.


  • 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.
  • 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.
  • 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.

Taught by

Alex Aklson


4.6 rating at Coursera based on 18803 ratings

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