Components of a Big Data and AI Solution Introduction
Course 1250
3 DAY COURSE

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. Plus, decision-makers 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!

Components of a Big Data and AI Solution Introduction Benefits

• In this course, you will:

  • Store, manage, and analyze structured and unstructured data.
  • Select the appropriate storage type for different datasets.
  • Process large datasets efficiently using distributed systems like HDFS and Spark to extract valuable insights.
  • Apply common machine learning techniques such as clustering, classification, and regression using SparkML and Python.
  • Harness the power of generative models like ChatGPT programmatically.
  • Benefit from continued support with post-course one-on-one instructor coaching.
  • Access a computing sandbox for hands-on practice and experimentation.

• Prerequisites

  None.

Data and AI Solution Course Outline

Module 1: Data and the Enterprise

Define the importance of data and its analysis in today's data-driven world

Differentiate between different types of data

Module 2: Storing and Querying Data

Describe different types of data storage

Assess the quality of data

Outline the ETL and ELT processes

Module 3: HDFS, Spark, and Kafka

Define Hadoop and HDFS

Describe Spark

Work with Kafka

Module 4: NoSQL Databases

Define NoSQL

Introduce the different types of Big Data data stores

• Key-value
• Document
• Column family
• Graph

Gain experience using Big Data data stores, including

• Redis
• MongoDB
• Cassandra
• Neo4j

Perform text searches with Lucene and Elasticsearch

Module 5: Analyzing and Interpreting Data

Discuss statistical analysis of Data

Explore machine learning including

Recommendations

Clustering

Classification

Module 6: Neural Networks

Introduce key ideas behind neural networks

Utilize deep neural networks for more complex problems

Examine generational neural networks

Module 7: Visualization

Visualize data to communicate results

Examine plots used for different purposes