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!
Data and AI Solution Training Delivery Methods
Data and AI Solution Training Course Information
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.
Data and AI Solution Course Outline
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
Work with Kafka
Introduce the different types of Big Data data stores
- Column family
Gain experience using Big Data data stores, including
Perform text searches with Lucene and Elasticsearch
Discuss statistical analysis of Data
Explore machine learning including
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