ML, AI, and Data Management

IN-PERSON PROGRAM

Level: Intermediate to Advanced

Become an AI & Data Specialist.

This program is an intensive training pathway that will help transform aspiring and current IT professionals into certified, job-ready experts in the most sought-after domains: Python Programming, Cloud Computing (AWS/Azure), Data Science, Artificial Intelligence, and Business Intelligence.

This program will help students prepare for the following certifications:

  • PCEP™ - Certified Entry-Level Python Programmer

  • AWS Certified AI Practitioner

  • AI-900 Microsoft Azure AI Fundamentals

  • Microsoft Azure Data Fundamentals (DP-900)

  • Data Science Solution on Microsoft Azure (DP-100)

  • Microsoft Certified: Fabric Analytics Engineer Associate (DP-600)

  • Power BI Data Analyst (PL-300)

Prerequisites: HS Diploma, G.E.D or equivalent

Required materials: All required materials are included with tuition

Who is this program for?

This program is ideal for aspiring data scientists, cloud AI engineers, and analytics professionals seeking multi-platform expertise in high-demand roles.

Delivery: In-Classroom

Duration: 12 weeks (240 hours)

  • 4-hour class, Mon-Fri

Tuition: $9,086

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What’s Included?

  • Exam vouchers

  • Practice exams

  • Books & Materials

Instructor Support

  • Certified instructor

  • Weekly in-person classes

Curriculum Breakdown

Python Programming

This course will explain the software programming basics using Python. As of the year 2022, Python is the most popular programming language because of its simple syntax and extensive, open-source library of modules. Python is commonly used for creating desktop and website applications in finance, business, and even social media like Instagram and Pinterest.

No coding experience is required for this course.

Prerequisite: None

  • Python Novice:

    • Getting Started with Python: Introduction

    • Complex Data Types in Python

    • Conditional Statements & Loops

    • Functions in Python

    Python Apprentice

    • Advanced Python Topics

    • Python Classes & Inheritance

    • Data Structures & Algorithms in Python

    Python Journeyman

    • Python Unit Testing

    • Python Requests

    • Flask in Python

    • Python Concurrent Programming

    Pythonista

    • Introduction to Using PyCharm IDE

    • Excel with Python

    • Socket Programming in Python

    • Python Design Patterns

    • Junior Python Developer

    • Software Developer / Software Engineer

    • Application Developer

    • Data Analyst

    • Automation Engineer / Test Automation Engineer

  • PCEP™ - Certified Entry-Level Python Programmer

    40 minutes

    NDA/Tutorial: 5 minutes

    Number of Questions: 30

    Format: Single- and multiple-select questions, drag & drop, gap fill, sort, code fill, code insertion, interactive & scenario-based items

    Passing Score: 70%

AWS Certified AI Practitioner (AIF-C01)

This course is designed for individuals who are looking to demonstrate foundational knowledge of artificial intelligence (AI) and machine learning (ML) concepts, and how they are implemented using Amazon Web Services.

Prerequisite: None

    • Basic AI Concepts and Terminologies

    • Practical Use Cases for AI

    • The ML Development Lifecycle

    • ML Operations (MLOps)

    • Basic Concepts of Generative AI

    • Capabilities and Limitations of Generative AI

    • Building Generative AI Applications with AWS

    • Design Factors for Applications Using Foundation Models

    • Effective Prompt Engineering Techniques 

    • Training, Fine-Tuning, and Evaluating Foundation Models

    • Guidelines for Responsible AI

    • Security, Compliance, and Governance for AI Solutions

    • AI/ML Assistant

    • AI Support Specialist

    • Machine Learning Technician

    • Cloud AI Specialist

    • Data Technician / Data Assistant

    • AI Solutions Associate

    • AI Project Assistant

    • Junior Machine Learning Engineer

  • 90 minutes

    65 questions; either multiple choice or multiple response

    Delivery: Pearson VUE testing center or online proctored exam

Microsoft Azure AI Fundamentals (AI-900)

This course helps students demonstrate their real-world knowledge of diverse machine learning (ML) and artificial intelligence (AI) workloads, and how they can be implemented with Azure AI.

Prerequisite: None

  • In this course, you will learn to:

    • identify features of common AI workloads and guiding principles for responsible AI

    • identify common ML types

    • describe core ML concepts

    • identify core tasks in creating an ML solution

    • describe capabilities of no-code ML with Azure Machine Learning Studio

    • identify common types of computer vision solutions

    • identify Azure tools and services for computer vision tasks

    • identify features of common NLP workload scenarios

    • identify Azure tools and services for NLP workloads

    • identify common use cases and Azure services for conversational Al

  • The AI-900 certification prepares you for entry-level roles:

    • AI Analyst

    • Data Analyst

    • Cloud AI Associate

    This certification can also lead to more technical or specialized roles:

    • AI Engineer

    • Machine Learning Engineer

    • Cloud Solution Architect

    • Business Intelligence Specialist

  • Duration: 45 minutes

    Exam Policy

    This exam will be proctored. You may have interactive components to complete as part of this exam. To learn more about exam duration and experience, visit: Exam duration and exam experience.

    If you fail a certification exam, don’t worry. You can retake it 24 hours after the first attempt. For subsequent retakes, the amount of time varies. For full details, visit: Exam retake policy.

Microsoft Azure Data Fundamentals (DP-900)

Designed for individuals who are new to data services and want to demonstrate foundational knowledge of data concepts and how they are implemented using Microsoft Azure. This certification is ideal for those who are exploring a career in data management, analytics, or business intelligence and is perfect for beginners who want to understand core data concepts, Azure data services, and how to work with data in the cloud.

Prerequisite: None

  • Describe core data concepts

    • Structured Data

    • Semi-Structured and Unstructured Data

    • Azure Data Storage

    • Data Storage Considerations

    • Azure Analysis Services

    • Analytical Workloads

    • Online Transaction Processing (OLTP)

    Describe considerations for working with non-relational data on Azure

    • Relational and Non-Relational Databases

    • Azure Database for MySQL and PostgreSQL

    • Azure SQL Database

    • SQL Server on Azure VM

    Describe an analytics workload on Azure

    • Azure Blob Storage

    • Azure Data Lake Storage

    • Azure File Storage

    • Azure File Shares

    • Azure Table Storage

    • Azure Cosmos DB

    • Azure Cosmos DB for MongoDB, Apache Cassandra, and Gremlin

    Azure Data Services: Ingestion, Transformation, and Business Intelligence

    • Data Ingestion and Processing

    • Data Warehousing Services

    • Azure Synapse Analytics

    • Data Analyst

    • Business Intelligence (BI) Analyst

    • Data Engineer (Entry-Level)

    • Database Administrator (Junior)

    • Cloud Data Specialist

    • Reporting Analyst

    • Azure Data Support Technician

    • Data Operations Associate

  • Duration: 45 minutes 

    This exam will be proctored. You may have interactive components to complete as part of this exam. To learn more about exam duration and experience, visit: Exam duration and exam experience.

Microsoft Azure Data Scientist Associate (DP-100)

This course prepares learners for the DP-100 exam by covering the design and implementation of data science solutions on Microsoft Azure. Topics include machine learning models, Azure ML services and workspaces, data storage and orchestration, and model deployment and monitoring.

Prerequisite: None

    • Machine Learning

    • ML Services

    • ML Regression Models

    • ML Classification Models

    • ML Clustering Models

    • Project Jupiter & Notebooks

    • Azure ML Workspaces

    • Azure Data Platform Services

    • Azure Storage Accounts

    • Storage Strategy

    • Azure Data Factory

    • Non-relational Data Stores

    • ML Data Stores & Compute

    • ML Orchestration & Deployment

    • Model Features & Differential Privacy

    • ML Model Monitoring

    • Azure Data Storage Monitoring

    • Data Process Monitoring

    • Data Solution Optimization

    • High Availability & Disaster Recovery

    • Data Scientist

    • Machine Learning Engineer

    • AI Engineer

    • Data Analyst (Advanced/ML-focused)

    • Azure AI Specialist

    • Cloud Data Scientist

    • Predictive Analytics Engineer

    • Business Intelligence (BI) Developer with ML focus

  • Duration: 100 minutes 

    This exam will be proctored. You may have interactive components to complete as part of this exam. To learn more about exam duration and experience, visit: Exam duration and exam experience.

Implementing Analytics Solutions Using Microsoft Fabric (DP-600)

The DP-600 training program prepares learners to become skilled Microsoft Fabric Analytics Engineers capable of designing, building, and deploying end-to-end analytics solutions within the Microsoft Fabric ecosystem. This course covers essential concepts such as data ingestion, transformation, modeling, governance, and real-time analytics using tools like Data Factory, Data Engineering, Data Science, Power BI, and Lakehouse architecture.

Students learn how to implement scalable data solutions, optimize performance, apply security and compliance best practices, and deliver actionable insights across organizational workflows. Upon completion, learners are equipped with the practical skills needed to support enterprise-level analytics operations and succeed in roles involving data engineering, analytics development, and Fabricenabled business intelligence.

Prerequisite: None

  • Getting Started with the DP-600 Exam

    Working with Data Lakehouses

    • Spark & the Capacity Metrics App for Lakehouses

    • Spark Configuration & Delta Tables

    • The Medallion Architecture with a Star Schema

    Working with Data Warehouses in Microsoft Fabric

    • Introducing Data Warehouses

    • Monitoring Fabric Warehouses

    • Implementing Security in Fabric Warehouses

    Data Pipelines and Dataflows in Microsoft Fabric

    • Data Transformations Using Dataflow Gen2

    • Creating and Using Simple Data Pipelines

    • Working with Complex Data Pipelines

    OneLake, KQL, and KQL DB

    • OneLake, KQL, and KQL D

    • Data Analyst

    • Analytics Engineer

    • Business Intelligence (BI) Developer

    • Data Engineer

    • Power BI Developer

    • Microsoft Fabric Specialist

    • BI Analyst

    • Data Insights Developer

    • Reporting Analyst

    • Data Solutions Engineer

  • Duration: 100 minutes 

    This exam will be proctored. You may have interactive components to complete as part of this exam. To learn more about exam duration and experience, visit: Exam duration and exam experience.

Power BI Data Analyst (PL-300)

Designed to equip you with the skills and knowledge necessary for effective data analysis and visualization using Microsoft Power BI.

Prerequisite: None

  • Prepare the data

    • Power BI for Data Analysis

    • Loading and Transforming Data in Power BI

    • Preparing Data for Visualizations in Power BI

    Model the data

    • An Overview of Data Modeling in Power BI

    • Applying the DAX Formula Language in Power BI

    • Working with Filters in Power BI

    • Using Time Intelligence in Power BI

    • Advanced Modeling Techniques in Power BI

    Visualize and analyze the data

    • Understanding Data Visualization

    • Creating and Formatting Charts in Power BI

    • Leveraging Ribbon, Line, Column, and Pie Charts in Power BI

    • Maps, Waterfall Charts, and Scatter Plots in Power BI

    • Matrix and Treemap Controls in Power BI

    • Using the Power BI Service

    Deploy and maintain items

    • Analysis and Sharing Features in Power BI

    • Extracting Insights from Data Using Power BI

    • Applying Power BI’s Advanced Analysis Features

    • Sharing Power BI Reports and Workspaces

    • Power BI Data Analyst

    • Business Intelligence (BI) Analyst

    • Reporting Analyst

    • Data Visualization Specialist

    • Data Analyst

    • Business Analyst

    • Analytics Consultant

    • BI Developer (Entry to Mid-Level)

  • Duration: 100 minutes 

    This exam will be proctored. You may have interactive components to complete as part of this exam. To learn more about exam duration and experience, visit: Exam duration and exam experience.