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Big Data Analytics

Big Data and Data Analytics for Managers and Business Users

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  • Overview

    The purpose of this course is for a student to get a broad familiarity with the relevant concepts of data analytics and data science and how they are applied to a wide range of business, scientific and engineering problems. The course will also explore the unique challenges of doing data analytics at very large scales, i.e. “Big Data”.

  • Who Should Take This Course

    AUDIENCE

    The course is intended for a general audience including business people, managers, system architects/engineers, business/systems analysts, and software developers..

  • Schedule
  • Course Outline
    • History of data analytics and data science
    • Defining key terms and understanding the jargon
    • The key drivers for data analytics (data, math, computation and analysis)
    • The basic phases of a data analytics project
    • Descriptive, Predictive and Prescriptive analytics
    • Data science algorithms and their application
      • Linear Regression
      • Classification and Decision Trees
      • Na├»ve Bayes
      • Clustering
    • Data Governance
    • Big Data Tools
      • For data analytics: SQL, Pig and Hive, Spark
      • For data visualization: Tableau
      • For ETL: Pig, Apache NiFi
    • For Storage: HDFS, Cassandra, Ceph, Cloud
    • Coding tools: R vs Python, NumPy, Pandas Data Analysis Library
  • FAQs
    Is there a discount available for current students?

    UMBC students and alumni, as well as students who have previously taken a public training course with UMBC Training Centers are eligible for a 10% discount, capped at $250. Please provide a copy of your UMBC student ID or an unofficial transcript or the name of the UMBC Training Centers course you have completed. Online courses are excluded from this offer.

    What is the cancellation and refund policy?

    Student will receive a refund of paid registration fees only if UMBC Training Centers receives a notice of cancellation at least 10 business days prior to the class start date for classes or the exam date for exams.

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