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Big Data Analytics

Introduction to Data Analytics

Group Training + View more dates & times
    
                     
  • Overview

    This course is a survey of processes and tools commonly used in applications that rely heavily on data analysis. The course will describe data pipelines deployed by data engineers and data scientists to ingest data for use in an application and to manipulate that data for use by analysts. Hands-on activities will include a combination of instructor demos and several instructor-guided labs to promote understanding of select topics.

  • Who Should Take This Course

    AUDIENCE

    This course is suitable for: Analysts, Team Leads, Project Managers, Data analysts, Data engineers, and Developers.

    PREREQUISITES

    Students should have basic proficiency navigating websites using a browser and also with using a spreadsheet application such as Excel. A basic proficiency in some programming language is helpful but not required.

  • Why You Should Take This Course
    • Understand the different components of a modern data ecosystem.
    • Describe the typical roles and responsibilities of Data Engineers, Data Analysts, Data Scientists, and Business Intelligence Analysts.
    • Describe common types of data structures, file formats, and sources of data such as CSV, TSV, XML, JSON, Parquet and Logging formats.
    • Gain an understanding of the languages and tools that data professionals use to manipulate data.
    • Explain ETL processes used to extract, transform, and load data into data repositories.
    • Gain experience with some different tools for acquiring, importing, wrangling, and cleaning data. along with some of their characteristics, strengths, limitations, and applications.
    • Become aware of typical use cases for different types of data repositories such as Databases, Data Warehouses, Data Marts, and Data Lakes.
    • Understand tools available for Data Visualization.
    • Understand platforms and tools for Big Data processing.
    • Understand Machine Learning and available tools.
  • Schedule
  • Course Outline
    1. Data Science Ecosystems
    2. Data Sources and Formats
    3. Extract, Transform, and Load (ETL)
    4. Data Repositories
    5. Acquiring and Wrangling Data
    6. Using pandas for Data Analysis
    7. Data Visualization
    8. Big Data Processing
    9. Machine Learning
  • 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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