Data Science Project Life cycle

1. Exploratory Data Analysis: It is said that 90% of the work done by a data scientist is related to data analysis. The term “data analysis” refers to the cleaning and pre-processing of data before the construction of a statistical model. This step includes outliers, duplicate data, null values ​​, and many other anomalies, which do not fall within the data agreement required for business purposes.
2. Understanding the problem: It is imperative that the details of the problem are clear before you dive into the actual implementation part. It is important to find out what is right to get the right data and get the right solution.
3. Getting the right data: Once the problem is understood, it is mandatory to get the right data to perform the operation.
4. Using the correct metrics: Depending on the business domain, the metric that will determine the completeness of a model should be selected.
5. Data visualization: Data visualization is a general term that refers to a graphical representation of information and data using visual elements such as graphs, charts, and maps. Data visualization tools help people understand the importance of data and provide an accessible way to see the trends, patterns, and relationships in data.Once data has been cleaned and processed in advance, it is necessary to visualize the data to determine the correct features or columns to use in the statistical model.
6. Model Selection: The selection of the correct model is necessary for a particular problem statement because each model may not fit perfectly to each data set.
7. Hierarchical Encoding: This step of the data science process is applicable for instances where input attributes are explicit and need to be converted into numbers used in the model because the machine cannot function properly with some ranges.
8. Communication: businessmen, salesmen or shareholders, usually do not understand the technical knowledge of data science, and therefore it is necessary for their business to communicate the findings, products, and services to their customers in simple terms, which can then come up with measures to alleviate any potential risk.
9. Deployment: Sometimes, the word “implementation” is used to mean the same thing. Once the statistical model is built, and the business domain is satisfied with the findings and results, this model can be deployed and implemented to build analytical tools and improve business efficiency.

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