Life Cycle Of Data Science Project

Posted by NareshIT on January 22nd, 2020

In this big data era, the main focus is on data storage and data processing. With Hadoop and other frameworks, big data storage is successful. Now the challenge and concern are mainly on its processing. Data Processing mainly involves converting raw data into meaningful and informative data which is used for various purposes. Data Science is the secret sauce for this processing of data.

What is Data Science?

Data Science is a combination of various tools, algorithms and machine learning principles with an objective to discover ways of processing raw data. Data Science also called data-driven science is a combination of statistics and computation to elucidate data for decision making.
A data scientist is a person who does not only analyze but also uses machine learning algorithms to the occurrence of a specific event in the future. The role of a data scientist is crucial in filling the gap between analytical skills and business skills.

Life Cycle Of Data Science:

1.Understanding Topic
2.Acquisition Of Data
3.Preparation Of Data
4.Exploring Data
5.Predictive modeling and Evaluation
6.Interpretation and Deployment

1.Understanding Topic:

Firstly, Data Scientist identifies the problem and analyze the problem for solution. This is a decisive phase in which they also find if such a case happened in the past.

2.Acquisition Of Data:

Data Acquisition is also called data discovery or data collection. In this acquisition, data is readily available for working or you will be collecting data required to deal with. The acquisition of data depends on its quality and processing.

3.Preparation Of Data:

Data Preparation is the most important step in this life cycle. It does not matter how you collected the data you must clean data and make it ready for analysis. During this stage data will be wobbling, so we will sometimes need to go back and collect the data required. Many data scientists say this preparation and cleaning of data consume 80% of time

4.Exploring Data:

Data Exploration is also called Data Mining. This is a step where you start analyzing and understanding the patterns of the data prepared. You may need to do additional cleaning of data while analyzing it.

5.Predictive Modeling and Evaluation:

In this, you try different combinations with your data to evaluate the outcomes. You will be noticing new things as you analyze your data set. Using separate validation sets of data to know how your model is performing.

6.Interpretation and Deployment:

Once your prediction model is confirmed you outcome can interpret the data and results, Finally, your model is deployed and can be used in real-time.