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Big Data Analytics

Applied Data Analytics and Machine Learning

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  • Overview

    This course helps students develop a broad familiarity with primary tools and techniques in modern data analytics and machine learning and how they are applied to a wide range of business, scientific and engineering problems.

    Course duration

    3 to 10 Days (see the outline options below)

  • Who Should Take This Course


    This course is intended for software engineers and data scientists who wish to learn data analytics and machine learning techniques.


    Students should have programming experience in a modern structured language. Experience with Python and SQL is also recommended.

  • Schedule
  • Course Outline

    1. Data Analysis and Visualization with Python

    • Python basics, focused on how to use python for data analysis
    • The numpy library, which is a foundation of numerical computing in Python
    • The Pandas library, a very popular data analysis library
    • Building visualizations using Seaborn and matplotlib


    2. Machine Learning with Python

    • Introduction and history of machine learning
    • Framing a problem as a supervised machine learning problem
    • Overview of the most popular supervised machine learning algorithms, like neural networks, random forests, and support vector machines
    • Brief overview of unsupervised learning and clustering


    3. PySpark

    • Hadoop and HDFS basics
    • Spark architecture basics
    • Overview of Spark
    • How to use the PySpark RDD interface to write distributed data computing jobs
    • How to use the PySpark DataFrame interface to analyze data in a distributed fashion
    • How to use SparkSQL in PySpark to run SQL-based analysis on data setsOPTIONAL COURSE MODULES (1 Day Each):


    4. Advanced Spark

    • This one-day module expands on the topics covered in the first day of Spark training. It covers advanced topics on performance tuning through caching, data formats, query structure, data layout, configuration parameter tuning, and more. It also covers the basics of Scala in Spark, which can be used to get the most performance out of Spark.


    5. Unsupervised Learning and Anomaly Detection

    • This one-day module covers unsupervised learning, which is learning off of unlabeled data. Approaches inside of unsupervised learning include pattern matching, clustering, and anomaly detection.


    6. Graph Analytics

    • This one-day module covers how to analyze the relationships between entities in graph-style analytics. The overall concept of graph analytics is covered, such as community detection, pathfinding, edge recommendation, and others. Common graph-based systems such as GraphX, Neo4j, and Titan will be covered briefly.


    7. Deep Learning for Computer Vision

    • This one-day module covers how to apply deep learning to computer vision problems such as image classification, localization, and segmentation. The basics of Tensorflow or PyTorch are also covered as the system that executes the deep learning workloads.


    8. Overview of Natural Language Processing

    • This one-day module covers an overview of natural language techniques and approaches. Topics include syntactic parsing, part of speech tagging, entity recognition, coreference resolution, topic modeling, word embeddings, word clouds. The newer advancements in deep learning will also be briefly covered.


    9. Deep Learning for Text and Natural Language Processing

    • This one-day module covers how to apply deep learning and neural networks to text problems. Approaches covered include word2vec, GLoVE, transformer networks, recurrent networks, and transfer 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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