How to do data science project? Data science methodology| step by step way to do data science|part 1

Welcome to Data Science Methodology 101! This is the beginning of a story -one that you'll be telling others about for years to come. It won't be in the form you experience here, but rather through the stories you'll be sharing with others, as you explain how your understanding of a question resulted in an answer that changed the way something was done. 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 as 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. Here is a definition of the word methodology. It's important to consider it because all too often there is a temptation to bypass methodology and jump directly to solutions. Doing so, however, hinders our best intentions in trying to solve a problem. 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 data science methodology discussed in this course has been outlined by John Rollins, a seasoned and senior data scientist currently practising at IBM. This course is built on his experience and expresses his position on the importance of following a methodology to be successful.watch the video for full transcribe.
Time:
0:0 Data science methodology introduction
from ploblem to approach
Requirement to collection


In this lesson, what will you learn:

1.The need to understand and prioritize the business goal.
2.The way stakeholder support influences a project.
3.The importance of selecting the right model.
4.When to use a predictive, descriptive, or classification model,

5.The significance of defining the data requirements for your model.
6.Why the content, format, and representation of your data matter.
7.The importance of identifying the correct sources of data for your project.
8.How to handle unavailable and redundant data.
9.To anticipate the needs of future stages in the process.

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