DATA SCIENCE

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DESIGNING AND IMPLEMENTING DATA SCIENCE SOLUTION ON AZURE - DP 100

The Designing and Implementing Data Science Solution on Azure – DP 100 course by Radical Technologies in Bangalore offers comprehensive training to master data science solutions using Azure. This certification course covers data exploration, model training, and deployment using Azure Machine Learning. With hands-on projects and expert guidance, students gain practical skills in designing scalable data science models. The program includes job assistance to help participants secure roles in the industry. Ideal for aspiring data scientists, this course equips learners with the knowledge to excel in Azure-based data science careers.

Radical Technologies

50,000+ English 60 hours Weekdays / Weekends Classroom / Online / Corporate

Online / Classroom

IT Training Programme

Duration 60 hours

Batch Type Weekdays / Weekends

Mode of Training Classroom / Online / Corporate

Locations Pune, Bangalore, Kochi

Language English

Certification Globally Recognized

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100% placement assistance

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What you'll learn

Understand core concepts and architecture from the ground up

Get hands-on with the tools used by working professionals

Build real-world projects you can add to your portfolio

Learn industry best practices and coding standards

Practice with real datasets and real-world scenarios

Prepare for certification and technical interviews

Work on collaborative, team-based exercises

Apply performance tuning and optimization techniques

Understand how the technology fits into a larger ecosystem

Complete assignments reviewed by mentors

Programme Overview

4 sections covering the complete curriculum — a single, progressive learning arc.

60 hours

Training Duration

Core Modules

Total Lessons

4.9★

Average Rating

50K+

Students Trained

Foundations & Core Concepts

Get hands-on with the fundamentals and architecture — the building blocks for everything that follows.

Fundamentals Architecture Setup

Hands-On Practical Training

Work through real exercises and assignments designed to mirror what you will do on the job.

Practicals Assignments Labs

Real-World Projects

Apply what you have learned to end-to-end projects that go straight into your portfolio.

Projects Portfolio Case Studies

Advanced Techniques

Go beyond the basics with advanced concepts, integrations and production-grade practices.

Advanced Integration Best Practices

Ecosystem Integration

Understand how this technology connects with the broader tools and platforms used in the industry.

Ecosystem Tools Platforms

Performance & Interview Prep

Master optimization techniques and prepare for the technical interview questions employers actually ask.

Optimization Interview Prep Certification

Who is this programme for?

Whether you're already writing code, working with data, or supporting applications today — this programme is built to take you into a DATA SCIENCE role.

Software Developers

Engineers who want to add this skill set to their toolkit

Analysts & Consultants

Professionals moving into a more technical, hands-on role

IT Professionals

System admins and support engineers upskilling into a new domain

Fresh Graduates

CS/IT graduates aiming for a job-ready technical role

Course Curriculum

4 sections • 82 lessons • 60 hours

Jump to Section

Total Duration

60 hours

100% Practical · Unlimited Doubt Sessions

01 Design and prepare a machine learning solution

Design a machine learning solution

Determine the appropriate compute specifications for a training workload

Describe model deployment requirements

Select which development approach to use to build or train a model

Manage an Azure Machine Learning workspace

Create an Azure Machine Learning workspace

Manage a workspace by using developer tools for workspace interaction

Set up Git integration for source control

Manage data in an Azure Machine Learning workspace

Select Azure Storage resources

Create and manage data assets

Manage compute for experiments in Azure Machine Learning

Create compute targets for experiments and training

Select an environment for a machine learning use case

Configure attached compute resources, including Apache Spark pools

Monitor compute utilization

02 Explore data and train models

Explore data by using data assets and data stores

Access and wrangle data during interactive development

Wrangle interactive data with Apache Spark

Create models by using the Azure Machine Learning designer

Create a training pipeline

Consume data assets from the designer

Use custom code components in designer

Evaluate the model, including responsible AI guidelines

Use automated machine learning to explore optimal models

Use automated machine learning for tabular data

Use automated machine learning for computer vision

Use automated machine learning for natural language processing (NLP)

Select and understand training options, including preprocessing and algorithms

Evaluate an automated machine learning run, including responsible AI guidelines

Use notebooks for custom model training

Develop code by using a compute instance

Track model training by using MLflow

Evaluate a model

Train a model by using Python SDK

Use the terminal to configure a compute instance

Tune hyperparameters with Azure Machine Learning

Select a sampling method

Define the search space

Define the primary metric

Define early termination options

03 Prepare a model for deployment

Run model training scripts

Configure job run settings for a script

Configure compute for a job run

Consume data from a data asset in a job

Run a script as a job by using Azure Machine Learning

Use MLflow to log metrics from a job run

Use logs to troubleshoot job run errors

Configure an environment for a job run

Define parameters for a job

Implement training pipelines

Create a pipeline

Pass data between steps in a pipeline

Run and schedule a pipeline

Monitor pipeline runs

Create custom components

Use component-based pipelines

Manage models in Azure Machine Learning

Describe MLflow model output

Identify an appropriate framework to package a model

Assess a model by using responsible AI guidelines

04 Deploy and retrain a model

Deploy a model

Configure settings for online deployment

Configure compute for a batch deployment

Deploy a model to an online endpoint

Deploy a model to a batch endpoint

Test an online deployed service

Invoke the batch endpoint to start a batch scoring job

Apply machine learning operations (MLOps) practices

Trigger an Azure Machine Learning job, including from Azure DevOps or GitHub

Automate model retraining based on new data additions or data changes

Define event-based retraining triggers

Tools & Technologies

Every tool listed here is installed, configured and used in a hands-on lab session.

Core Tools

Hands-On Labs

Practical Environment

Industry-Standard Tools

Real-World Setup

Guided Exercises

Skill Building

Sample Datasets

Practice Material

Practice & Projects

Mini Projects

Applied Practice

Assignments

Mentor Reviewed

Doubt Sessions

Live Support

Career Readiness

Resume Building

Career Support

Mock Interviews

Interview Prep

Certification Prep

Global Recognition

Deployment & Delivery

Production Practices

Real-World Ready

Best Practices

Industry Standards

82+

Hands-On Lessons

Core Modules

60 hours

Training Duration

100%

Practical Training

You don't just learn DESIGNING AND IMPLEMENTING DATA SCIENCE SOLUTION ON AZURE - DP 100. You ship it.

Three major projects, each mirroring how production teams actually work — from guided foundations to a portfolio-ready capstone.

PROJECT // 01

Guided Foundation Project

→Requirement Analysis

→Guided Implementation

→Mentor Review

→Iteration

Foundation Beginner

Apply the fundamentals in a structured, mentor-reviewed project

Take the core concepts from the first half of the curriculum and apply them to a realistic scenario, with guidance and feedback from your mentor at every step.

Structured project brief

Step-by-step implementation

Mentor feedback and review

Documented outcome

Stack Core Concepts Best Practices

PROJECT // 02

Applied Practice Project

→Scenario Design

→Independent Build

→Testing & Validation

→Peer Review

Applied Intermediate

Build a more independent project mirroring real production scenarios

Work through a project that combines multiple concepts from the curriculum, closer to how work is actually structured on the job — less hand-holding, more ownership.

End-to-end implementation

Testing and validation

Documentation

Peer/mentor review

Stack Applied Skills Testing

PROJECT // 03

Capstone Project

→Planning

→End-to-End Build

→Review & Refinement

→Presentation

Capstone Advanced

Take a project from requirements to a polished, portfolio-ready deliverable

Your final project — plan, build, test and present a complete solution using everything covered in the curriculum, reviewed by mentors before you graduate.

Complete working solution

Presentation-ready documentation

Mentor sign-off

Portfolio-ready deliverable

Stack Full Curriculum Portfolio

All 3 projects go directly into your portfolio & resume — reviewed by mentors before you graduate.

See Sample Project Reports

Upcoming Batches

No upcoming batches scheduled right now. Enquire to get notified.

Why Radical Technologies

Live Online Training

Highly practical oriented training
Installation support on your system
24/7 Email and Phone support
100% Placement Assistance
Global Certification Preparation
Trainer-Student Interactive Portal
Assignments and Projects by Mentors

Live Classroom Training

Weekend / Weekdays / Morning / Evening batches
80:20 Practical and Theory ratio
Real-life Case Studies
Easy make-up for missed sessions
PSI | Kryterion | Redhat Test Centers
Lifetime Video Classroom Access (coming soon)
Resume Prep and Mock Interviews

Self-Paced Training

Learn 300+ courses at your own time
50,000+ Satisfied Learners
Course Completion Certificate
Practical Labs available
Mentor Support available
Doubt Clearing Session available
10% Discounted Global Certification

Like the Curriculum? Let's Get Started

Join 50,000+ students already enrolled at Radical Technologies

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Global Certification

Radical Technologies is the leading IT certification institute in Pune, offering globally recognized certifications across various domains. With expert trainers and comprehensive materials, we ensure students gain in-depth knowledge and hands-on experience to excel in their careers. Our certification programs are tailored to meet industry standards — from cloud technologies to data science — empowering individuals to stay ahead in the ever-evolving tech landscape.

Career Services

At Radical Technologies, we are committed to your success beyond the classroom. Our 100% Job Assistance program ensures that you are not only equipped with industry-relevant skills but also guided through the job placement process. With personalised resume building, interview preparation, and access to our extensive network of hiring partners, we help you take the next step confidently into your IT career.

Career Support

Course Completed? Need next steps?

Need Interview Supports?

Need Job Assistance?

Came from any other Institute?

Join our Brush-up Session & get support until you find a job!

Get Started

Radical Learning Eco-System

Exam Simulator

Cloud SandBox

Hands-on Cloud Lab

Developer Coding Ground

Student Reviews

4.9★

Average learner rating

50K+

Students trained

30+

Hiring companies alumni work at

100%

Placement assistance

4.9

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Course Rating

★★★★★

92%

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Frequently Asked Questions

15 questions about the DESIGNING AND IMPLEMENTING DATA SCIENCE SOLUTION ON AZURE - DP 100 course.

01 What is the DP-100 certification?

The DP-100 certification, officially called “Designing and Implementing a Data Science Solution on Azure,” validates skills in using Azure tools to design, develop, and deploy machine learning models and data science solutions.

02 Who should take the DP-100 certification?

The certification is ideal for data scientists, AI engineers, and IT professionals looking to enhance their expertise in implementing data science solutions using Azure’s ecosystem.

03 What skills are tested in the DP-100 exam?

The exam measures skills in setting up Azure Machine Learning environments, preparing and transforming data, developing machine learning models, and deploying and monitoring solutions.

04 What prerequisites are required for the DP-100 certification?

While there are no mandatory prerequisites, familiarity with Python, data science concepts, and Azure basics is highly recommended.

05 What tools are commonly used in Azure for data science projects?

Azure Machine Learning, Azure Data Factory, Azure Databricks, and Jupyter Notebooks are some of the key tools used in designing and implementing data science solutions on Azure.

06 How does Azure Machine Learning support machine learning model development?

Azure Machine Learning offers tools like AutoML, Python SDK, and Azure ML Designer to create, train, and optimize machine learning models efficiently.

07 Can I integrate Azure Machine Learning with other Azure services?

Yes, Azure Machine Learning integrates seamlessly with services like Azure Synapse Analytics, Azure Data Lake, and Azure Kubernetes Service for advanced data science workflows.

08 What are pipelines in Azure Machine Learning, and why are they important?

Pipelines automate and streamline the end-to-end machine learning workflow, including data preparation, model training, and deployment, ensuring consistency and scalability.

09 How does Azure handle data preprocessing for machine learning?

Azure offers tools for data wrangling, cleaning, and transformation through Azure Machine Learning and Azure Data Factory, making it easier to prepare data for modeling.

10 What is the role of Responsible AI in Azure Machine Learning?

Responsible AI principles ensure fairness, transparency, and accountability in AI solutions. Azure provides tools for explain ability, bias detection, and compliance with ethical AI standards.

11 How can I deploy a machine learning model in Azure?

Models can be deployed as web services through Azure Machine Learning endpoints, enabling integration with applications via REST APIs.

12 What is the advantage of using AutoML in Azure?

Azure AutoML simplifies the model creation process by automatically selecting algorithms, tuning hyperparameters, and providing optimal models for given datasets.

13 How does Azure support collaboration in data science projects?

Azure allows team collaboration through shared workspaces, version control with GitHub, and integration with Azure DevOps for streamlined project management.

14 What industries benefit the most from Azure-based data science solutions?

Azure’s data science capabilities are widely used in industries such as healthcare, finance, retail, manufacturing, and logistics for predictive analytics, fraud detection, and process optimization.

15 How can I prepare for the DP-100 exam?

Preparation involves studying the official DP-100 learning path on Microsoft Learn, practicing hands-on labs in Azure, reviewing the exam guide, and taking practice tests.
These FAQs are designed to cover essential questions about the DP-100 certification and its practical applications, ensuring they align with SEO guidelines and avoid plagiarism.

DESIGNING AND IMPLEMENTING DATA SCIENCE SOLUTION ON AZURE - DP 100 Interview Questions

10 questions commonly asked in DESIGNING AND IMPLEMENTING DATA SCIENCE SOLUTION ON AZURE - DP 100 interviews.

01 What are the key features of Azure Machine Learning, and how do they simplify the process of building and deploying machine learning models?
02 Explain the importance of data preprocessing in Azure Machine Learning. How would you handle missing or inconsistent data in a dataset?
03 Describe the process of setting up an Azure Machine Learning workspace. What components are critical for effective project management?
04 What are the differences between Azure Machine Learning Designer and using Python SDK for developing machine learning models?
05 How would you implement hyperparameter tuning for a machine learning model in Azure? What tools or techniques are available to optimize model performance?
06 What steps would you take to deploy a trained machine learning model as a web service in Azure?
07 How can you monitor and manage deployed models in Azure to ensure their performance and accuracy over time?
08 Explain the role of Responsible AI principles in Azure Machine Learning. How do you ensure fairness, transparency, and ethical considerations in your AI solutions?
09 What is the significance of pipelines in Azure Machine Learning, and how do they streamline end-to-end machine learning workflows?
10 How do you integrate Azure Machine Learning with other Azure services such as Azure Data Factory and Azure Databricks for advanced data science projects?