Components of a Big Data and AI Solution Introduction

Course 1250

Duration: 3 days
Labs: Yes
Language: English
Level: Foundation

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

In-Person
Online

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.

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

Get This Course $2,885 GOV $2,539

3-day instructor-led training course
Hands-on labs included
End-of-course Learning Tree exam included
After-course coaching available
After-course computing sandbox

#1250

Guaranteed to Run - You can rest assured this class will not be canceled. Delivery format is subject to change – please call Customer Service if you have any questions.
Sep 3 - 5 9:00 AM - 4:30 PM EDT

Virtual
Nov 24 - 26 9:00 AM - 4:30 PM EST

Herndon, VA or Virtual
Feb 11 - 13 9:00 AM - 4:30 PM EST

Herndon, VA or Virtual

3-day instructor-led training course
Hands-on labs included
End-of-course Learning Tree exam included
After-course coaching available
After-course computing sandbox

#1250

Guaranteed to Run - You can rest assured this class will not be canceled. Delivery format is subject to change – please call Customer Service if you have any questions.
Sep 3 - 5 9:00 AM - 4:30 PM EDT

Virtual
Nov 24 - 26 9:00 AM - 4:30 PM EST

Herndon, VA or Virtual
Feb 11 - 13 9:00 AM - 4:30 PM EST

Herndon, VA or Virtual

Bring this or any training to your organization
Full-scale program development
Delivered when, where, and how you want it
Blended learning models
Tailored content
Expert team coaching

#1250

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Data and AI Solution Training FAQs

No, the course is designed for both a technical and a non-technical audience. The demos can be simply executed or, if one is so inclined, one can examine the code that makes them run.

We primarily use Java and Python languages in this course.

After the course, you have access to the virtual machines for 90 days. You can also download the code from your My Learning Tree account, but in that case, you would have to install the various programs on your own computer.

The benefit to a non-technical person is an appreciation for what it takes to get the insights you are after and the vocabulary you will use to specify what you need.

Yes, indeed the course does discuss large language models, ChatGPT being an example, and you run several such models programmatically from Python.

Working knowledge of how to execute commands on a computer. The instructions for activities are very detailed, so one does not need to be a seasoned programmer. Typical job roles include Project and IT Managers, Database Administrators & Data Architects, Developers & SQL Developers, Data Scientists & Business Intelligence.

There will be examples of technologies that fill different roles. A short list includes Kafka, Redis, Cassandra, MongoDB, HDFS, SparkML, Python, Java.

Components of a Big Data and AI Solution Introduction

Data and AI Solution Training Delivery Methods

Data and AI Solution Training Course Information

Data and AI Solution Course Outline

Module 1: Data and the Enterprise

Module 2: Storing and Querying Data

Module 3: HDFS, Spark, and Kafka

Module 4: NoSQL Databases

Module 5: Analyzing and Interpreting Data

Module 6: Neural Networks

Module 7: Visualization

Data and AI Solution Training FAQs

Do I need extensive programming experience for this course?

What programming languages are used in the course?

Do I get access to the source code used for the demos and exercises?

I am a decision maker and not technical. Will I still benefit from this course?

Does this course discuss large language models?

What background do I need for this course?

What kind of technologies will I be exposed to in this course?

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