In the modern world, data is of very high importance. Many disciplines have developed around the study and processing of data with data science among them. Data is processed through various stages during its lifetime, starting from creation, testing, processing, and consumption to finally being reused. These stages can be anywhere from five to sixteen overlapping, continuing processes. These stages are collectively called the data science life cycle or data science pipeline.
5 Stages Of Data Science Life Cycle
There are mainly five stages of the data science life cycle that fit within almost all definitions of the term.
Capturing or gathering data:
This stage is performed to gather raw data from relevant sources. The data gathered can be in a structured or unstructured form. The methods of gathering the data can vary from manual entry and web scraping to capturing data from systems and devices in real-time.
Examples of databases where data can also be gathered for this stage are Oracle, PostgreSQL, and MongoDB. Social media networks also allow their users to access data by connecting with their web servers, Twitter and Facebook are great examples of such platforms.
Prepare and maintain the data:
This stage mainly requires formatting the data by filtering and scrubbing it for processing and analyzing or machine learning or deep learning models. It can include cleaning, deduplicating, and reformatting the data. ETL (extract, transform, load) or any other data integration technology can also be used to combine the data. This combined data is put into systems such as data warehouses, data lakes, or any other unified store for analysis.
Exploring or processing the data:
In this stage, the data scientists examine the data for biases, patterns, ranges, and distributions of values within the data. It is done to determine the sustainability of the data when used with predictive analysis, regression, machine learning, and/or deep learning algorithms.
The characteristics of the data also require introspection. This is because different types of data such as nominal or ordinal data, numerical data, and categorical data need to be handled differently. Data visualization is also utilized to highlight important trends and patterns of data where significant data can be adequately comprehended in by simple aids like bars and line charts.
Analyzing and modeling of the data:
As the name suggests, data scientists perform statistical analysis, predictive analysis, regression, machine learning, and deep learning algorithms to extract the necessary insights from the prepared data.
Achieving this needs the modeling of data. Features and values that are not necessary for the prediction of results need to be omitted, and for this, data scientists need to choose the properties they deem essential for the prediction of the model and minimize the dimensions of each data set.
Communicating/Interpreting the data:
The insights that are learned from the analysis stage are finally presented as reports, charts, and other visualizations that help provide a clear understanding of the raw data. These data interpretations need to be clear to individuals with no technical knowledge of the data. The insights generated from the data science life cycle can obtain both predictive and prescriptive analytics. It gives businesses the knowledge to replicate positive and negative results.
Data science programming languages such as R or Python have components for generating visualizations. Data scientists can also use dedicated visualization tools as alternatives to produce simple visualizations of the interpreted data.
These are the five stages of the data science life cycle that every student of data science needs to be familiar with. Broadening their knowledge with relevant data science books can be a good place to start. Combining basic data skills along with the ability to present an actionable narrative is important for data scientists. Following top data scientists is also a great way to get familiar with data science.