Data science combines multiple fields to extract value from data, including statistics, scientific methods, artificial intelligence (AI), and data analytics. Professionals who use data science are known as data scientists. They apply various skills to analyze data collected from the web, smartphones, customers, sensors, and other sources to generate actionable insights.
Data science includes preparing data for analysis, such as cleaning, aggregating, and manipulating data for advanced data analysis. Analytical applications and data scientists can review the results to uncover patterns, providing business leaders with well-informed insights.
Because corporations are sitting on a treasure trove of data, as modern technology has enabled the creation and storing of ever-increasing amounts of data, the volumes of data have increased. It is estimated that 90% of the data in the world was shaped in the last two years. For example, Facebook users upload 10 million photos per hour. But often, this data is stored in databases and data lakes, mostly intact.
The vast amount of data collected and stored by these technologies can bring transformative benefits to organizations and societies worldwide, but only if we know how to interpret it. That’s where data science comes in.
Data science reveals trends and generates information that companies can use to make better decisions and create more innovative products and services. Perhaps most importantly, it enables machine learning models to extract insights from the vast amounts of data fed to them, thus avoiding relying primarily on business analysts to see what they can discover.
Data is the foundation of innovation, but its value comes from the information that data scientists can extract from it and then use.
To better understand data science and how it can be harness. It is equally essential to be familiar with other terms related to this field, such as artificial intelligence (AI) and machine learning. You’ll often find these terms used interchangeably, but there are nuances.
Here’s a simple breakdown:
By refining products and services, organizations use data science to turn data into a competitive advantage. Some use cases of data science and machine learning are:
Many companies have prioritized data science and are investing heavily in it. For example, in Gartner’s latest survey of more than 3,000 CIOs and CIOs. Respondents ranked analytics and business intelligence as the most critical differentiating technologies for their organizations. In addition, the chief technology officers (CIOs) surveyed consider these technologies to be the most strategic for their companies and are investing accordingly.
The process of analyzing and using the data is iterative rather than linear. But this is how the data science life cycle typically flows in a data modeling project:
Data scientists often use various open source libraries or in-database tools to build machine learning models. Users often want APIs to help with data ingestion, data visualization, profiling, or feature engineering. They will need the right tools and access to the correct data and other resources, such as computing power.
Data scientists need to get their models to a high percentage of accuracy to be confident that they can be implement. Model evaluation will typically generate a comprehensive set of evaluation metrics and visualizations. To measure the model’s performance against new data and rank it over time for optimal behavior in production. Evaluation goes beyond performance and takes into account the expected baseline behavior.
Explaining the inner mechanics of machine learning model outputs in human terms has not always been possible. But it is becoming increasingly important. For example, data scientists want automated explanations of the relative weighting and importance of factors that go into generating a prediction, as well as specific descriptive details about model predictions.
It is often complicate and time-consuming to take a trained machine learning model and deploy it to suitable systems. However, it can be made more accessible by running the models as secure and scalable APIs or using machine learning models embedded in the database.
Science is a logical enterprise that builds and arranges knowledge through testable explanations and estimates about the universe. The initial roots in the history of science can be trace to Ancient Egypt and Mesopotamia from around 3000 to 1200 BCE.
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