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We're here to help you find itOracle Cloud Infrastructure Data Science Professional: Hands-on Workshop certification comprises a focused exploration of advanced features offered by Oracle in data management and Analytics. This certification signifies an individual's expertise in using Oracle Cloud Infrastructure for implementing and managing complex data science projects. The hands-on workshop facilitates learning about Oracle’s advanced tools for machine learning, Data exploration, Model training and data visualisation. Industries utilize this certification to identify proficient data science professionals who can leverage Oracle Cloud Infrastructure to drive business insights. It helps in demonstrating the professional's ability to use Advanced data science techniques for actionable business decisions.
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♱ Excluding VAT/GST
Classroom Training price is on request
You can request classroom training in any city on any date by Requesting More Information
Machine learning is a branch of artificial intelligence that allows computers to learn and make decisions from data without being explicitly programmed. It involves algorithms and statistical models that computers use to perform specific tasks by processing large amounts of data. As the system receives and digests more data, its ability to make accurate predictions or decisions improves. This technology is widely used in various fields such as finance, healthcare, marketing, and more, enabling enhanced decision-making, predictive analysis, and automation of complex processes.
Data exploration is the initial step in data analysis, where you examine large data sets to discover patterns, anomalies, or relationships. This process helps to understand the nature and quality of data before applying any formal analysis or predictive modeling. Techniques used can include summarizing statistics, visualizing data distributions with graphs, and querying databases to extract useful information. The goal is to gain insights that can guide further data projects and decision-making processes. While exploring, it’s crucial to clean and prepare data, ensuring that the insights you derive are based on accurate and relevant information.
Oracle Cloud Infrastructure (OCI) provides a collection of cloud services that allow businesses to build and manage a wide range of applications in a highly available, scalable, and secure cloud environment. It's designed to support various workloads, specifically offering powerful tools and capabilities for data science through the Oracle Data Science Cloud. This platform helps professionals easily develop, deploy, and manage machine learning models, providing integrated, scalable data processing, and storage solutions, which optimizes performance and cost for data science projects.
Data management involves the process of collecting, storing, organizing, and maintaining the information created and collected by an organization. Effective data management ensures that data is accurate, accessible, and secure, enabling businesses to make data-driven decisions quickly. It encompasses a variety of disciplines including data integration, warehousing, and analytics. With the integration of cloud technologies like Oracle Data Science Cloud, organizations can enhance their data management strategies by utilizing advanced analytics tools to process and analyze large datasets more efficiently, facilitating improved insights and operational efficiencies.
Analytics involves studying historical data to find trends, make predictions, and guide decision-making. It uses various statistical techniques and tools to understand, evaluate, and visualize data in a meaningful way. Whether it’s predicting market trends, enhancing customer engagement, or improving operational efficiency, analytics helps businesses and organizations make informed, data-driven choices. This process is crucial in environments like Oracle Data Science Cloud, where data science applications help extract deeper insights from complex data sets, boosting analytics capabilities efficiently and effectively.
Model training is part of developing machine learning algorithms where a model learns from processed historical data to make predictions or decisions without direct programming for each step. It involves feeding the model data, allowing it to adjust its internal parameters based on the accuracy of its predictions during training. This process repeats iteratively, enhancing the model’s performance with each cycle. Optimal model training results in a model that generalizes well to new, unseen data, thereby delivering reliable outcomes relevant to various applications like image recognition, financial forecasting, or even self-driving cars.
Advanced data science techniques involve sophisticated methods to extract insights and predictions from data. This includes machine learning, where algorithms learn from data patterns to make decisions; deep learning, a type of machine learning with complex neural networks that mimic human brain functions; and ensemble methods, which combine multiple models to improve accuracy. These techniques are crucial in areas like predictive analytics, where they help forecast future trends based on historical data, enhancing decision-making across various industries. Oracle Data Science Cloud provides a platform that integrates these advanced tools, offering scalable computing power to handle large datasets efficiently.
