Life Cycle Of Data Science Project |Life Cycle Of Data Science

Data science is quickly evolving to be one of the hottest fields in the technology industry. With rapid advancements in computational performance that now allow for the analysis of massive datasets, we can uncover patterns and insights about user behavior and world trends to an unprecedented extent

Life Cycle Of Data Science Project

Life Cycle of Data Science is the process by which a project developed. There are several steps involves in the life cycle of data science.

1. Business Understanding
2. Data Acquisition
3. Data Processing
4. Data Exploration
5. Data Modelling
6. Deployment

Let's discuss every points in details

1.Business Understanding

Business Understanding is the first phase of develpment of data science project. In this phase we gain the knowledge of domain, in which field we are worked and create a documentation. After it we identify central objectives

2.Data Acqusition

Data acqusition is the next phase of data science life cylce. After after getting the knowledge of domain, we  need data by which solve this problem. in simple word what data do i need for our project?. and this data how we collect from different sourcess and what are the data sources?.and what is the most efficient way to store and access all of it?.

3.Data Processing

Data processing is the third steps of data science life cycle. this phase is consume maximum time of the project. Because in this phase clean the data by using some technique. The reason why this is such a time-consuming process is simply that there are so many possible scenarios that could necessitate cleaning. For instance, the data could also have inconsistencies within the same column, meaning that some rows could be labeled 0 or 1, and others could be labeled no or yes. The data types could also be inconsistent — some of the 0s might integers, whereas some of them could be strings. If we’re dealing with a categorical data type with multiple categories, some of the categories could be misspelled or have different cases, such as having categories for both male and Male. This is just a subset of examples where you can see inconsistencies, and it’s important to catch and fix them in this stage.

4.Data Exploration

Data Exploration is the fourth phase of data science life cycle.In this phase we have clearn data The data exploration stage is like the brainstorming of data analysis. This is where you understand the patterns and bias in your data. It could involve pulling up and analyzing a random subset of the data using Pandas, plotting a histogram or distribution curve to see the general trend, or even creating an interactive visualization that lets you dive down into each data point and explore the story behind the outliers.

Using all of this information, you start to form hypotheses about your data and the problem you are tackling. If you were predicting student scores, for example, you could try visualizing the relationship between scores and sleep. If you were predicting real estate prices, you could perhaps plot the prices as a heat map on a spatial plot to see if you can catch any trends.

5.Data Modelling

Data exploration is the fifth phase of data science life cycle. In this phase determine optima data features for the machine learning(ML)

model. and create a model that predicts target most accurately, evaluate and test the efficiency of the model.

6.Deployment

Deployment is the last phase. In this phase check the deployment environment for dependency issues and deploy the mode in a pre-production/test environment and monitor the performance.and for the best performance repeate this all cycle.



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