Data science combines multiple fields such as statistics, scientific methods, artificial intelligence (AI), and data analytics to extract value from data. Data science practitioners are called data scientists. They combine various skills to analyze data collected from the web, smartphones, clients, sensors, and other sources to derive actionable insights.
Data science encompasses preparing data for analysis, cleaning, aggregating, and manipulating data to perform advanced analysis. Analytics applications and data scientists can review the results to uncover patterns and enable business leaders to gain informed insights.
Data science is one of the most sensational fields out there today. But why is it so important?
Because corporations have a treasure trove of untapped data, now that modern technology has enabled the creation and storing of ever-increasing amounts of information, the volume of data has exploded. It is estimated that 90% of the data in the world was generated in the last two years. For example, Facebook users upload 10 million snaps per hour.
But this data is often just sitting still in databases and data lakes, basically untouched.
The massive amount of data collected and stored by these technologies can benefit 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 allows machine learning (ML) models to learn from the vast amounts of data fed to them rather than relying primarily on business analysts to see what they can discover from the data.
Data is the foundation of innovation, but its value comes from the information scientists can extract and then use.
To better understand it (and how you can take advantage of it), it’s essential to know other terms related to the field, such as artificial intelligence (AI) and machine learning. You’ll often find these terms used interchangeably, but there are nuances.
It is a summary:
And, just in case, we include another definition.
Organizations use data science to turn data into a competitive advantage by refining products and services. Some use cases for data science and machine learning include:
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, respondents ranked analytics and business intelligence as the most critical technologies to differentiate their organizations. In addition, the CIOs surveyed see these technologies as the most strategic for their companies and are making the corresponding investments.
The process of analyzing and using the data is iterative rather than linear, but this is the standard data science lifecycle flow for a modelling project:
Define a project and its possible results.
Data scientists frequently use a variety of open-source libraries or in-database tools to build machine learning models. Users often need APIs to help with data ingestion, visualization and profiling, or feature engineering. In addition, they need the right tools and access to the correct data and other resources like processing power.
Data scientists must achieve high accuracy in their models before confidently implementing them. Model evaluation typically generates comprehensive metrics and visualizations to measure the model’s performance against new data and rank it over time to enable optimal decision-making behavior. Model evaluation goes beyond raw presentation to take into account likely baseline behavior.
We have not always been able to explain the inner mechanics of the results of autonomous learning models in human terms, but this is becoming increasingly important. For example, data scientists want automated explanations of the relative weighting and importance of factors that generate a prediction, along with model-specific descriptive details about the model’s predictions.
Taking a trained machine learning model and deploying it to suitable systems is often a time-consuming and challenging process. However, this can be simplified by operationalizing the models as secure and scalable APIs or using machine learning models within the database.
Unfortunately, the implementation of the model is not the final step. Models should constantly be monitored after deployment to ensure they are working correctly. Over time, the data on which the model was trained may become outdated for future predictions. In fraud detection, for example, criminals are constantly finding new ways to hack into accounts.
Creating, evaluating, implementing, and monitoring machine learning models can be complex. It is why the number of data science tools has increased. Data scientists use many tools, but one of the most common is open basis notebooks, web applications for text and running code, visualizing data, and viewing results, all within a single environment.
Some of the most standard notebooks are Jupiter, RStudio, and Zeppelin. Notebooks are handy for analysis, but they have some limitations when data scientists work in teams. To solve this problem, data science platforms were created.
For example, some users prefer a data source independent service that uses open source libraries. Others like the speed of machine learning algorithms in the database.
In most organizations, this projects are typically overseen by three types of managers:
These directors work with the data science team to define the problem and develop a strategy for analysis. They may be the heads of a line of business such as Marketing, Finance or Sales and have a data science team directly reporting to them. They work closely with the Directors of Data Science and Information Technology to ensure that projects come to fruition.
Senior CIOs are responsible for the infrastructure and architecture that will support data science operations. They continuously monitor operations and resource usage to ensure this teams operate efficiently and securely. They may also be responsible for creating and updating IT environments for data science teams.
These managers oversee the data science group and their day-to-day work. They are team makers who can balance team development with project planning and monitoring.
Data Science is responsible for analyzing large volumes of information with the help of artificial intelligence to improve information management.
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