Data Science Process or Lifecycle

These are the following process given below

1.Discovery:

Discovery step involves acquiring data from all the identified internal & external sources which helps you to answer the business question.

The data can be:

• Logs from webservers
• Data gathered from social media
• Census datasets
• Data streamed from online sources using APIs

2.Data Preparation:

 Data preparation is also known as Data Munging. In this phase, we need to perform the following tasks:

• Data cleaning
• Data Reduction
• Data integration
• Data transformation,

3.Model Planning:

In this stage, you need to determine the method and technique to draw the relation between input variables. Planning for a model is performed by using different statistical formulas and visualization tools. SQL analysis services, R, and SAS/access are some of the tools used for this purpose.

4. Model Building:

In this step, the actual model building process starts. Here, Data scientist distributes datasets for training and testing. Techniques like association, classification, and clustering are applied to the training data set. The model once prepared is tested against the "testing" dataset.

Following are some common Model building tools:

• SAS Enterprise Miner
• WEKA
• SPCS Modeler
• MATLAB

5. Operationalize:

In this phase, we will deliver the final reports of the project, along with briefings, code, and technical documents. This phase provides you a clear overview of complete project performance and other components on a small scale before the full deployment.

6. Communicate Results

 In this phase, we will check if we reach the goal, which we have set on the initial phase. We will communicate the findings and final result with the business team.