Data Science: Today Techodu will talk about Data Science.
Definition of Data Science
Data science involves the fields of statistics, scientific method, artificial intelligence (AI), and data analysis, and aims to extract value from data. People who apply data science in practice are called data scientists, and they use a combination of skills to analyze data collected from the web, smartphones, customers, sensors, and other sources for actionable insights.
Data science processes include the process of preparing data for analysis, including cleaning, aggregating, and populating data to perform advanced data analysis. Data scientists can then view the results through analytics applications to uncover patterns in the data that help business leaders gain informed insights.
Data Science: Machine Learning Resources to Be Developed
Data science (DS) is one of the most exciting fields today. Why is it so important?
Because businesses are sitting on massive amounts of data. With the development of modern technology, more and more information is created and stored, and the amount of data has also exploded. It is estimated that 90% of the world’s data was created in the past two years. For example, Facebook users upload ten (10) million photos every hour.
However, this data is often just located in databases and data lakes and is largely underutilized.
The vast amount of data collected and stored through technology can bring transformative benefits to organizations and societies around the world, but only if we can interpret it. This is what data science is all about.
Data science uncovers trends and generates insights that businesses can use to make better decisions and launch more innovative products and services. Most importantly, data science enables machine learning (ML) models to learn from the vast amounts of data collected, rather than relying on business analysts to manually see what they can discover from the data.
Data is the cornerstone of innovation, but the value of data can only be realized if data scientists can gather information from data and then act on it.
What is the difference between data science, artificial intelligence, and machine learning?
To gain a deeper understanding of data science and how to leverage it, it is equally important to understand other terms related to the field, such as artificial intelligence (AI) and machine learning. You’ll find that these terms are often used interchangeably, but there are still some nuances.
The simple distinction is as follows:
AI refers to making computers mimic certain human behaviors.
Data science is a subset of AI that refers more to the overlapping fields of statistics, scientific method, and data analysis (all used to extract meaningful insights from data).
Machine learning is also a subset of AI that uses a variety of techniques to enable computers to gain insights from data and deliver AI applications.
To distinguish better, we will introduce another definition.
Deep learning ( DL) is a subgroup of machine learning (ML) that enables computers to solve more complex problems
How data science is revolutionizing business models
Businesses are using data science to improve products and services, turning data into a competitive advantage. machine learning and Data science use cases include:
- Analyze data collected from call centers to determine churn so marketing can take action to retain customers.
- Improve efficiency by analyzing traffic patterns, weather conditions and other factors, helping logistics companies speed up deliveries and reduce costs.
- Analyzing medical test data and reporting symptoms to improve patient diagnosis and help physicians diagnose disease earlier and treat it more effectively.
- Optimize supply chains by predicting equipment downtime.
- Detect fraud in financial services, including identifying suspicious and unusual behavior.
- Improve sales performance by providing recommendations to customers based on their purchase history.
Many companies make data science a top priority and invest heavily in it. In a recent Gartner survey of more than 3,000 CIOs, respondents unanimously rated analytics and business intelligence as the technologies that best differentiate their organizations. Interviewed CIOs see these technologies as strategic to their companies and invest accordingly.
How data science is performed
The data analysis and processing flow is iterative, not linear, but this is the typical data science life cycle flow in a data modeling project:
Planning: Define the project and its potential outcomes.
Building data models: Data scientists often use various open-source libraries or in-database tools to build machine learning models. In general, users want to use APIs to support data ingestion, data profiling, visualization, or functional design. They need the right tools to access the right data and other resources like computing power.
Evaluate the model: Before deploying a model, data scientists must ensure that the model is highly accurate. Model evaluation typically generates a comprehensive set of evaluation metrics, visualized, and then measures model performance against new data, ranking it on an ongoing basis for optimal production behavior. In addition to raw performance, model evaluation takes into account expected baseline behavior.
Explaining Models: While it is not always feasible to explain the internal mechanisms of machine learning model results in human language, its importance is growing. Data scientists want the system to automatically account for the relative weights and importance of the factors that generated the predictions, as well as the specific model interpretation details of the model’s predictions.
Deploying the model: Taking a trained machine learning model and putting it into a suitable system is often a difficult and laborious process. This can be made easier by manipulating the model as an extensible and secure API or by using an in-database machine learning model.
Monitor Model: Unfortunately, the deployment model is not the end of the process. Models also need to be monitored after they are deployed to ensure they are functioning properly. After a period of time, the data used to train the model will no longer be suitable for future predictions. Take fraud detection as an example, criminals will always come up with new ways to hack accounts.
Data Science Tools
Building, evaluating, deploying, and monitoring machine learning models is a complex process. To deal with these complexities, data science tools are increasingly available. Among the many tools used by data scientists, one of the most common is open-source notebooks. It’s a web app for writing and running code, visualizing data, and viewing results, all within the same environment.
Some of the mainstream notebook tools mainly include Jupyter, RStudio, and Zeppelin. Notebook tools, while very useful in performing analysis, have certain limitations in enabling data scientists to collaborate in teams. The emergence of data science platforms solves this problem.
To decide which data science tool is best for you, start by answering the following questions: What language do your data scientists speak? What kind of work methods do they prefer? What data source do they use?
For example, some users prefer to use open-source library-based, data-source-agnostic services. Others prefer faster in-database machine learning algorithms.
Who will oversee the data science process?
In most organizations, data science projects are typically overseen by three categories of managers:
Business Manager: Business managers work with the data science team to define problems and develop analytical strategies. They might be line-of-business leaders such as marketing, finance, or sales, to whom the data science team needs to report. They work closely with the data science team and IT managers to ensure project delivery.
IT Managers: Senior IT managers are responsible for the infrastructure and architecture design to support data science operations. They continuously monitor operations and resource utilization to ensure the data science team is operating efficiently and securely. Additionally, they may be responsible for building and updating the IT environment for the data science team.
Data Science Manager: A data science manager oversees a data science team and its day-to-day work. They are team builders, able to balance team development with project planning and monitoring.
But in this process, data scientists are the protagonists.
What is a data scientist?
As a profession, data science is still young. It originated from the fields of statistical analysis and data mining. The Journal of Data Science was first published in 2002 by the International Scientific Council: Council on Data for Science and Technology. In 2008, the title “Data Scientist” came into existence, and the field of data science grew rapidly. Since then, while more colleges and universities have started offering data science degrees, there has been a shortage of data scientists.
Data scientists’ responsibilities include developing data analysis strategies; preparing data for analysis; exploring, analyzing, and visualizing data; building models from data using programming languages ​​such as Python and R; and deploying models into applications.
The job of a data scientist is not independent. In fact, the most effective data science is done in teams. In addition to data scientists, the team may include business analysts responsible for defining the problem, data engineers responsible for preparing the data and determining how it will be accessed, IT architects responsible for basic processes and infrastructure, and deploying models or analysis results to applications and application developers in products.
Challenges of implementing data science projects
While many businesses see the promise of data science and invest heavily in data science teams, they don’t realize the full value of data. In the competition for talent acquisition and data science project creation, some companies have adopted inefficient team workflows. Different people use different tools and processes to work together efficiently. Without tighter, more centralized management, executives may not reap the full return on their investment.
This chaotic environment presents a lot of challenges.
Data scientists cannot work effectively. Because access to data is authorized by IT administrators, data scientists often wait long periods of time to get the data and resources they need to analyze. After gaining access, data science teams may use multiple incompatible tools to analyze the data. For example, a data scientist might develop a model in R, but the application that uses the model is written in another language. This is why it can take weeks or even months to deploy a model into a working application.
App developers don’t have access to machine learning available. Sometimes, developers receive machine learning models that cannot be deployed directly into applications. Also, application developers need to address scalability issues because access points are not flexible enough to deploy models in all scenarios.
IT administrators spend too much time on support. Due to the proliferation of open source tools, IT needs to support more and more tools. For example, data scientists on a marketing team and a finance team might use different tools. Different teams may also have different workflows, which means that the IT team must constantly rebuild and update the environment.
Business managers are disconnected from data science. Data science workflows are not always integrated into business decision-making processes and systems, making it difficult for business managers to fully collaborate with data scientists. If the integration is poor, business managers will have a hard time understanding why it takes so long to go from prototype to production—and they’re less likely to support investments in projects they think are too slow.
Data Science Platform Offers New Capabilities
Many businesses have come to realize that data science work without an integrated platform is inefficient, insecure, and difficult to scale. This realization has led to the rise of data science platforms. A data science platform is the software hub around which all data science work revolves. A great platform can reduce many of the challenges of implementing data science and help businesses turn data into insights more quickly and efficiently.
With a centralized machine learning platform, data scientists can work in a collaborative environment using their favorite open source tools, with all work synchronized through a version control system.
Advantages of Data Science Platforms
A data science platform enables teams to share code, results, and reports, reducing redundancies and driving innovation. It removes bottlenecks in the workflow by simplifying management and incorporating best practices.
Overall, a great data science platform can:
Improves productivity by helping data scientists deliver models faster and reduce errors
Make it easier for data scientists to process large volumes of different types of data
Deliver enterprise-grade AI that is unbiased, auditable, replicable, and trusted
Data science platforms are built to support collaboration among users, including data science experts, general data scientists, data engineers, and machine learning engineers or experts. For example, data science platforms allow data scientists to deploy models as APIs, making it easy to integrate them into different applications. Data scientists can access tools, data and infrastructure without waiting for IT intervention.
Market demand for data science platforms has exploded. In fact, the platform market is projected to grow at a CAGR of over 39% over the next few years, reaching $385 billion in 2025.
What Platform Capabilities Do Data Scientists Need
When looking at the capabilities of a data science platform, some key features to consider include:
Choose a project-based UI to facilitate collaboration. The platform should enable staff to collaborate from model ideation to final development. It should support self-service access to data and resources for all team members.
Prioritize integration and flexibility. Make sure the platform supports new open source tools as well as common version control providers like GitHub, GitLab, and Bitbucket, and is tightly integrated with other resources.
Contains enterprise-level features. Make sure the platform can scale as your team and business grow. The platform should have strong access control and high availability and support a large number of concurrent users.
More powerful self-service for data science. Look for a platform that reduces the burden on IT and engineering, making it easy and convenient for data scientists to instantly spin up environments, track all work, and easily deploy models into production.
Simplified model deployment. Model deployment and operation is a very important step in the machine learning lifecycle, but it is often overlooked. Make sure you choose a service that helps simplify model operations, whether it provides an API or ensures that users can build models in an easy-to-integrate way.
Make using a data science platform a smart move
It’s time for your business to use a data science platform if you notice the following:
- Lack of productivity and collaboration.
- Unable to audit or replicate machine learning models.
- The model was never deployed to production.
A data science platform can create tangible value for your business. Oracle Data Science Platform provides rich services and a comprehensive end-to-end experience to accelerate model deployment and improve data science outcomes.