Data Analytics

 

 

Types of Measurement Scales

 

Data Analytic Process

1. Understanding the Business

This step involves understanding the objectives and requirements of a business organization. For example, an organization may have a lot of historical data and may wish to explore whether the new product will be successful or not. This involves a precise problem statement and its formulation.

2. Understanding the Data

The problem statement may involve the collection of data. Data can be structured data or unstructured data. Structured data is in the form of a table or a database. A non-structured data is in the form of text, images, or video. So, one has to understand the characteristic of the data for data collection. This stage involves the steps like data collection, understanding the characteristics of the data, and formulation of the hypothesis.

3. Preparation of Data and Data Preprocessing

The collected data may be “bad data”. For example, collected data may have missing values, inconsistent data, and noisy data. These data cause problems in data analytics as these data may cause inaccurate results. This problem is solved by preprocessing techniques that focus on cleaning the data.

4. Modelling

This step involves the application of a data analytics algorithm for data analysis. The aim may be to get some statistical details or to obtain a model or pattern. A model may be a formula or procedure that takes test data and produces the results.

5. Evaluation of a Model

This step involves the evaluation of the models using statistical analysis and visualization methods. The performance of the constructed model is evaluated using performance measures.

6. Deployment

This step involves the deployment of the model on the customer site and checking it in real-time mode. The results and feedback of the model can be used to improve the existing model for better performance.