Category: Big Data Beginner

  • Infonomics I: Business Information Economics and Data Monetization

    Thriving in the Information Age compels organizations to deploy information as an actual business asset, not as an IT asset or merely as a business byproduct. This demands creativity in conceiving and implementing new ways to generate economic benefits from the wide array of information assets available to an organization. Unfortunately, information too frequently is…

  • Spark, Hadoop, and Snowflake for Data Engineering

    This is primarily aimed at first- and second-year undergraduates interested in engineering or science, along with high school students and professionals with an interest in programmingGain the skills for building efficient and scalable data pipelines. Explore essential data engineering platforms (Hadoop, Spark, and Snowflake) as well as learn how to optimize and manage them. Delve…

  • Cloud Computing Applications, Part 2: Big Data and Applications in the Cloud

    Welcome to the Cloud Computing Applications course, the second part of a two-course series designed to give you a comprehensive view on the world of Cloud Computing and Big Data! In this second course we continue Cloud Computing Applications by exploring how the Cloud opens up data analytics of huge volumes of data that are…

  • Black Matters: Introduction to Black Studies

    Interdisciplinary survey of people of African descent that draws on the overlapping approaches of history, literature, anthropology, legal studies, media studies, performance, linguistics, and creative writing. This course connects the experiences of African-Americans and of other American minorities, focusing on social, political, and cultural histories, and on linguistic patterns. https://www.classcentral.com/course/mit-ocw-24-912-black-matters-introduction-to-black-studies-spring-2017-292285

  • Mathematics of Big Data and Machine Learning

    This course introduces the Dynamic Distributed Dimensional Data Model (D4M), a breakthrough in computer programming that combines graph theory, linear algebra, and databases to address problems associated with Big Data. Search, social media, ad placement, mapping, tracking, spam filtering, fraud detection, wireless communication, drug discovery, and bioinformatics all attempt to find items of interest in…

  • Mathematics of Big Data and Machine Learning, IAP 2020

    This course focuses on artificial intelligence and machine learning with an emphasis on data handling challenges. The learning outcomes include understanding the mathematics behind big data and machine learning, as well as gaining knowledge in areas such as associative arrays, group theory, entity analysis, structured data analysis, graph theory, bio sequence cross-correlation, and Kronecker graphs.…

  • Bioinformatics Capstone: Big Data in Biology

    In this course, you will learn how to use the BaseSpace cloud platform developed by Illumina (our industry partner) to apply several standard bioinformatics software approaches to real biological data. In particular, in a series of Application Challenges will see how genome assembly can be used to track the source of a food poisoning outbreak,…

  • Business Intelligence and Visual Analytics

    Building on “Data Warehousing and Business Intelligence,” this course focuses on data visualization and visual analytics. Starting with a thorough coverage of what data visualization is and what type of visualization is good for a given purpose, the course quickly dives into development of practical skills and knowledge about visual analytics by way of using…

  • Introduction to Big Data Analytics

    This course is for novice programmers or business people who’d like to understand more advanced tools used to wrangle and analyze big data. In this course you will be guided in basic approaches to querying and exploring data using higher level tools built on top of a Hadoop Platform. You will be walked through query…

  • Big Data, Artificial Intelligence, and Ethics

    This course gives you context and first-hand experience with the two major catalyzers of the computational science revolution: big data and artificial intelligence. With more than 99% of all mediated information in digital format and with 98% of the world population using digital technology, humanity produces an impressive digital footprint. In theory, this provides unprecedented…

Big Data Labs
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