Components of a Big Data and AI Solution Introduction
Course Outline
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
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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.
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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
- choosing a selection results in a full page refresh