To find insights concealed in an organization's data, data science integrates specialized programming, advanced analytics, artificial intelligence (AI), machine learning, and math and statistics with subject expertise. Strategic planning and decision-making can be aided by these insights.
Data science is one of the fields with the quickest growth rates across all industries due to the increasing volume of data sources and data itself. Thus, it should come as no surprise that Harvard Business Review (link located outside of IBM) named the position of data scientist the "sexiest job of the 21st century." Businesses are depending more and more on them to analyze data and make practical suggestions to enhance business results.
Analysts can gain practical insights from the data science lifecycle, which includes a variety of roles, tools, and processes. According to Factored AI, a data science project often goes through the following phases:
- Data ingestion
The lifecycle starts with the gathering of data, both unstructured and structured, from all pertinent sources through a range of techniques. These techniques can involve real-time data streaming from systems and devices, online scraping, and human entry. Along with unstructured data like log files, video, music, images, the Internet of Things (IoT), social media, and more, data sources might include structured data like customer data.
- Data processing and storage
Depending on the kind of data that needs to be recorded, businesses must take into account various storage systems because data can have a variety of formats and structures. Teams responsible for data management aid in establishing guidelines for data organization and storage, which makes it easier to work with analytics, machine learning, and deep learning models. This phase involves employing ETL (extract, transform, load) jobs or other data integration tools to clean, deduplicate, transform, and combine the data. Before loading into a data warehouse, data lake or other repository, this data preparation is crucial to improve data quality.
- Data analysis
To investigate biases, trends, ranges, and distributions of values within the data, data scientists perform an exploratory data analysis. The creation of hypotheses for a/b testing is driven by this data analytics exploration. Additionally, it enables analysts to assess the suitability of the data for modeling purposes related to deep learning, machine learning, and/or predictive analytics. Many organizations generally rely on this knowledge when making corporate decisions in order to achieve greater scalability, depending on the model.
Insights are finally made easier to understand for business analysts and other decision-makers through reports and other data visualizations that highlight the insights and their implications for the company. The components for creating visuals are built into data science programming languages like R or Python.