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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. 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.
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.
