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This course aims to help software engineers understand the world of big data by exploring various technologies and paradigms such as data warehouses, data lakes, SQL, NoSQL, and more. The goal is to evolve software developers into data practitioners who can leverage modern data tools effectively. The course teaches the skills needed to comprehend different…

This course aims to teach learners how to handle and process large volumes of data efficiently using a Bloom Filter. The course covers topics such as infrastructure, processing telemetry data, Bloom Filters, probabilistic data structures, and safe browsing. The teaching method involves a lecture-style presentation. This course is intended for individuals interested in data processing,…

This course introduces the role of a data scientist and explores how data mining techniques, some using PHP, are applied to create value from data. The course covers the significance of data in various industries and the future of the web in finding answers and discoveries. The teaching method includes sharing Data Scientist case studies…

This course aims to teach learners how to scale R to big data using Hadoop and Spark. By the end of the course, students will be able to set up a Spark cluster with R installed, manipulate data within HDFS using R, and develop and implement a machine learning model using R. The course primarily…

This course explores the new age of advertising in the era of big data. The learning outcomes include gaining insights into customer acquisition costs, understanding big data competitors, and learning about building and branding in the data marketing landscape. The course covers topics such as the creative data divide, the future of data marketing, multichannel…

This course focuses on the importance of privacy controls in a digital world. It aims to help learners understand the balance between big data and online privacy. The course discusses the increasing significance of privacy in the mainstream and provides insights from industry experts. The intended audience for this course includes individuals interested in data…

This course explores best practices for accessing data on local or shared file systems, emphasizing guidelines and tools to enhance performance, reliability, scalability, and portability of code. It covers real customer issues, the importance of data access as datasets grow, and considerations for scientific and high-performance computing. The course is taught by an expert in…

This course covers the learning outcomes and goals of designing big data architectures using Azure Machine Learning Studio. Participants will learn how to integrate Azure ML with different data stores, work with data loads, batches, and streams, customize ML processes using R modules, and scale Azure ML web services. The teaching method includes a session…

This course covers the learning outcomes and goals of building a big data platform in the cloud, focusing on avoiding common mistakes and implementing successful strategies. Participants will learn about scalable architectures, transparent reporting, GDPR compliance, and deployment optimization. The teaching method involves sharing real-world experiences and insights from Unacast’s journey in handling massive amounts…

This course teaches learners how to build a Node.js app that utilizes machine learning models for tasks such as detecting landmarks in photos, analyzing text sentiment, and translating text into multiple languages. The course covers using Google Cloud APIs for Vision, Natural Language Processing, and Speech, as well as querying terabytes of data in the…