Data Science Methodology
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Explore the methodologies used in data science. Understand the steps of the data science process, from problem identification and data collection to analysis, modeling, and insights generation, ensuring a structured approach to solving business problems.
At a Glance
Grab your lab coat, beakers, and pocket calculator … wait what? Wrong path! Fast forward and get in line with emerging data science methodologies that are in use and are making waves or rather predicting and determining which wave is coming and which one has just passed.
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.
- 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!
Course Syllabus
- Business Understanding
- Analytic Approach
Module 2: From Requirements to Collection
- Data Requirements
- Data Collection
Module 3: From Understanding to Preparation
- Data Understanding
- Data Preparation
Module 4: From Modeling to Evaluation
- Modeling
- Evaluation
Module 5: From Deployment to Feedback
- Deployment
- Feedback
- This course is self-paced.
- It can be taken at any time.
- It can be audited as many times as you wish.
- Data Science Hands-on with Open Source Tools(Tools for Datascience) (DS0105EN)
- Passion for Data Science
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