As the name suggests, data science is the study of vast data using modern tools and techniques. This domain is used to find the unseen patterns, derive meaningful information, and make business decisions without much difficulty. This subject is focused on building predictive models using complex machine learning algorithms. The data used in this subject can come from various sources and they can be presented in various formats.
This field has emerged as an important subject in the IT landscape. Many of us are unaware of the fact that data science consists of five different lifecycle phases. These phases are:
- Capture: This phase is associated with the gathering of raw structured and unstructured data. Data acquisition, data entry, signal reception, and data extraction are the steps in this phase.
- Maintain: This phase takes and processes the raw data to make them ready for use. The steps involved in this phase are data warehousing, data cleansing, data staging and processing, and data architecture.
- Process: After clearing the data and extracting meaningful information, data scientists take the prepared data and examine its patterns and ranges. Some of the activities in this phase are data mining, clustering, data modeling and summarization.
- Analyze: Data scientists perform various analyses on the processed data to understand how it can be used. Predictive analysis, regression, text mining, and qualitative analysis are a few steps in this stage.
- Communicate: After gathering all the information, data scientists present the collected data into easy-to-understand formats like charts and graphs. With the help of data reporting and visualization, researchers can present the data easily.