Data science is a combination of math and statistics, specialized programming, advanced analytics, artificial intelligence (AI), and machine learning combined with the domain expertise to uncover actionable insights hidden in an organization’s data. These insights can be used to inform decisions and strategic planning.
The increasing volume of data sources, and thus data, has made data science one of the fastest growing fields across all industries. As a result, it’s no surprise that Harvard Business Review named the role of data scientist the “sexiest job of the twenty-first century” (link resides outside of IBM). Organizations rely on them more to interpret data and make actionable recommendations to improve business outcomes.
The data science lifecycle includes a variety of roles, tools, and processes that allow analysts to derive actionable insights. A data science project typically goes through the following stages:
The lifecycle’s data collection phase begins with collecting raw unstructured and structured data from all relevant sources using various methods. These methods are manual entry, web scraping, and real-time data streaming from systems and devices. Customer data, for example, can be combined with unstructured data such as log files, audio, pictures, video, the Internet of Things (IoT), social media, and other sources.
Data Sorage and Processing
Because data can come in various formats and structures, businesses must consider various storage systems based on the type of data that needs to be captured. Data management teams contribute to the establishment of standards for data storage and structure, which facilitates workflows involving analytics, machine learning, and deep learning models. Cleaning data, deduplicating, transforming, and combining data using ETL (extract, transform, load) jobs or other data integration technologies are all part of this stage. This data preparation is critical for ensuring data quality before it is loaded into a data warehouse, data lake, or another repository.
In this step, data scientists perform exploratory data analysis to look for biases, patterns, ranges, and distributions of values in the data. This data analytics exploration drives the generation of hypotheses for a/b testing. It also enables analysts to determine the data’s relevance for use in predictive analytics, machine learning, and deep learning modeling efforts. Depending on the model’s accuracy, organizations can rely on these insights for business decision-making, allowing them to drive more scalability.
Finally, insights are presented as reports and other data visualizations to make the insights—and their impact on business—easier to understand for business analysts and other decision-makers. Data scientists can generate visualizations using a data science programming language such as R or Python or using dedicated visualization tools.