Components of a Big Data and AI Solution Introduction

Components of a Big Data and AI Solution Introduction (BIG111)

Course Length: 3 days
Delivery Methods: Available as private class only
Course Overview

Unlock the true potential of your business with our cutting-edge Components of a Data and AI Solution course! This hands-on introduction takes you on a transformative journey from raw data to invaluable insights, leveraging the power of data and AI. Gain a competitive edge by understanding what tools can do and how to extract real business value from their output. Our comprehensive training integrates an overarching view of the data-to-insights process with focused data science expertise, empowering you to store, manage, process, and analyze massive volumes of structured and unstructured data. Decision-makers will benefit significantly from exposure to available options and establishing a common vocabulary with technical practitioners. Maximize your potential with our Components of a Data and AI Solution training today!

Course Benefits
  • Define the importance of data and its analysis in today's data-driven world
  • Differentiate between different types of data
  • Describe different types of data storage
  • Assess the quality of data
  • Outline the ETL and ELT processes
  • Define Hadoop and HDFS
  • Describe Spark
  • Work with Kafka
  • Define NoSQL
  • Introduce the different types of Big Data data stores
  • Gain experience using Big Data data stores, including Redis, MongoDB, Cassandra, and Neo4j
  • Perform text searches with Lucene and Elasticsearch
  • Discuss statistical analysis of data
  • Explore machine learning, including recommendations, clustering, and classification
  • Introduce key ideas behind neural networks
  • Utilize deep neural networks for more complex problems
  • Examine generational neural networks
  • Visualize data to communicate results
  • Examine plots used for different purposes
Course Outline
  1. Data and the Enterprise
    1. Define the importance of data and its analysis in today's data-driven world
    2. Differentiate between different types of data
  2. Storing and Querying Data
    1. Describe different types of data storage
    2. Assess the quality of data
    3. Outline the ETL and ELT processes
  3. HDFS, Spark, and Kafka
    1. Define Hadoop and HDFS
    2. Describe Spark
    3. Work with Kafka
  4. NoSQL Databases
    1. Define NoSQL
    2. Introduce the different types of Big Data data stores
      1. Key-value
      2. Document
      3. Column family
      4. Graph
    3. Gain experience using Big Data data stores, including
      1. Redis
      2. MongoDB
      3. Cassandra
      4. Neo4j
    4. Perform text searches with Lucene and Elasticsearch
  5. Analyzing and Interpreting Data
    1. Discuss statistical analysis of data
    2. Explore machine learning, including
      1. Recommendations
      2. Clustering
      3. Classification
  6. Neural Networks
    1. Introduce key ideas behind neural networks
    2. Utilize deep neural networks for more complex problems
    3. Examine generational neural networks
  7. Visualization
    1. Visualize data to communicate results
    2. Examine plots used for different purposes
Class Materials

Each student will receive a comprehensive set of materials, including course notes and all the class examples.

Live Private Class
  • Private Class for your Team
  • Live training
  • Online or On-location
  • Customizable
  • Expert Instructors
Request Proposal