Components of a Big Data and AI Solution Introduction

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