DP-100 Designing and Implementing a Data Science Solution on Azure Training - OEM Certkit

• Language: English
• Target Audience: The target audience for DP-100, the Microsoft Azure Data Scientist Associate exam, consists of professionals interested in working as data scientists on the Azure platform. This course is designed for individuals who already have experience with data analysis and machine learning and want to apply their skills in the Azure environment. Typical audiences for DP-100 are data analysts, data engineers and machine learning professionals who want to extend their expertise to working with Azure data science services and tools.
• Required Knowledge Level: A thorough understanding of data analytics and machine learning concepts is required to take full advantage of the DP-100 training.
• Course duration: 22:40 hours(depending on personal speed)
• Online access: 24/7, 365 days after activation
• Award Winning E-learning
• Contact with instructor by email and chat (Online Mentor)
• Full training for the official Exam
• User-friendly environment
• Certificate of Participation included

Microsoft Azure DP-100 Designing and Implementing Training

With the CertKit for Azure Machine Learning, you will learn how to use machine learning solutions at the cloud scale. You will discover how to apply your existing knowledge of Python and machine learning to ingest and prepare data, train and deploy models, and monitor and manage machine learning solutions in Microsoft Azure.

Specifically:

• Creating cloud resources in Microsoft Azure.
• Using Python to explore and visualize data.
• Training and validating machine learning models using common frameworks like Scikit-Learn, PyTorch, and TensorFlow.
• Working with containers.

Course content

DP-100 - Azure Data Scientist Associate: Machine Learning

Course: 1 Hour, 8 Minutes

• Course Overview
• Machine Learning
• Data Manipulation and Datasets
• Labeled and Unlabeled Data
• Data Features
• Machine Learning Regression Algorithms
• Machine Learning Classification Algorithms
• Machine Learning Clustering Algorithms
• Supervised Machine Learning
• Unsupervised Machine Learning
• Course Summary

DP-100 - Azure Data Scientist Associate: Machine Learning Services

Course: 45 Minutes

• Course Overview
• Azure Machine Learning Services
• Azure Machine Learning Studio
• Creating an Azure ML Studio Account
• Inspecting Azure ML Studio Components
• Ingesting Data from Azure Blob Storage
• Creating and Using Compute
• Course Summarys

DP-100 - Azure Data Scientist Associate: Machine Learning Regression Models

Course: 1 Hour, 13 Minutes

• Course Overview
• Machine Learning Regression Models
• Regression Model Training Concepts
• Regression Model Selection
• Training a Regression Model
• Executing the Regression Model Pipeline
• Evaluating a Regression Model
• Creating an Inference Pipeline
• Deploying Regression Model Predictive Services
• Course Summary

DP-100 - Azure Data Scientist Associate: Machine Learning Classification Models

Course: 1 Hour, 10 Minutes

• Course Overview
• Machine Learning Classification Models
• Classification Model Training Concepts
• Classification Model Selection
• Training Classification Models
• Executing the Classification Model Pipeline
• Evaluating the Classification Model
• Creating an Inference Pipeline
• Deploying Classification Model Predictive Services
• Course Summary

DP-100 - Azure Data Scientist Associate: Machine Learning Clustering Models

Course: 58 Minutes

• Course Overview
• Machine Learning Clustering Models
• Clustering Model Training Concepts
• Clustering Model Training
• Creating a K-means Clustering Model
• Evaluating a Clustering Model
• Creating a Clustering Model Inference Pipeline
• Deploying Clustering Model Predictive Services
• Course Summary

DP-100 - Azure Data Scientist Associate: Project Jupyter & Notebooks

Course: 1 Hour, 34 Minutes

• Course Overview
• Project Jupyter
• Jupyter Notebooks
• Installing Jupyter Notebook
• Using a Jupyter Notebook for Data Analysis
• Using a Jupyter Notebook for Regression Models
• Using a Jupyter Notebook for Classification Models
• Using a Jupyter Notebook for Clustering Models
• Using a Jupyter Notebook with PyTorch
• Using a Jupyter Notebook with TensorFlow
• Course Summary

DP-100 - Azure Data Scientist Associate: Azure Machine Learning Workspaces

Course: 1 Hour

• Course Overview
• Azure Machine Learning Workspaces
• Creating an Azure Machine Learning Workspace
• Installing Azure Machine Learning Tools
• Creating Scripts for Machine Learning Experiments
• Using Azure Machine Learning Metrics
• Creating Scripts for Azure Machine Learning Training
• Running a Notebook to Train Predictive Models
• Course Summary

DP-100 - Azure Data Scientist Associate: Azure Data Platform Services

Course: 1 Hour, 17 Minutes

• Course Overview
• Azure Data Structure Types
• Azure SQL Database
• Azure Cosmos DB
• Azure Data Storage
• Azure Data Lake Storage Gen
• Azure Synapse Analytics
• Azure Stream Analytics
• Azure Databricks
• Azure Data Factory
• Azure HDInsight
• Course Summary

DP-100 - Azure Data Scientist Associate: Azure Storage Accounts

Course: 1 Hour, 7 Minutes

• Course Overview
• Azure Storage Accounts
• Azure Storage Containers
• Azure File Shares
• Azure Table Storage
• Azure Queue Storage
• Azure Storage Account Tools
• Creating an Azure Storage Account
• Creating Azure Containers and File Shares
• Creating Azure Tables

DP-100 - Azure Data Scientist Associate: Storage Strategy

Course: 1 Hour, 3 Minutes

• Course Overview
• Azure Storage Strategy
• Azure Data Structure Types
• Azure Storage Security
• Uploading and Downloading Blobs
• Azure Storage Services
• Creating a VM and Storage Account
• Uploading Data to an Azure Storage Account
• Downloading from an Azure Storage Account
• Configuring Metrics Storage Account Monitoring
• Course Summary

DP-100 - Azure Data Scientist Associate: Azure Data Factory

Course: 1 Hour, 3 Minutes

• Course Overview
• Azure Data Factory
• Azure Data Factory Integration Runtime
• Azure Data Factory Linked Services and Datasets
• Azure Data Factory Pipelines and Activities
• Azure Data Factory Triggers
• Creating an Azure Data Factory
• Creating Data Factory Linked Services and Datasets
• Creating Data Factory Pipelines
• Creating Data Factory Triggers
• Course Summary

DP-100 - Azure Data Scientist Associate: Non-relational Data Stores

Course: 1 Hour, 16 Minutes

• Course Overview
• Azure Non-relational Data
• Azure Cosmos DB
• Creating an Azure Cosmos DB
• Azure Blob Storage
• Creating an Azure Blob Storage Container
• Azure Data Lake Storage Gen
• Creating an Azure Data Lake Storage Gen2 Solution
• Azure Data Masking
• Azure Data Management
• Course Summary

DP-100 - Azure Data Scientist Associate: Machine Learning Data Stores & Compute

Course: 1 Hour, 2 Minutes

• Course Overview
• Azure Data Stores
• Creating and Registering Data Stores
• Working with Datasets
• Running a Jupyter Notebook to Work with Data
• Machine Learning Studio Compute Targets
• Creating Python Environments in Azure ML Studio
• Working with a Compute Instance in Azure
• Creating an Azure Machine Learning Compute Cluster
• Running a Jupyter Notebook with Compute
• Course Summary

DP-100 - Azure Data Scientist Associate: Machine Learning Orchestration & Deployment

Course: 1 Hour, 13 Minutes

• Course Overview
• Machine Learning Pipelines
• Moving Data Using Machine Learning Pipelines
• Creating Machine Learning Pipelines
• Publishing Machine Learning Pipelines
• Triggering Machine Learning Pipelines
• Troubleshooting Machine Learning Pipelines
• Deploying Real-time Services
• Consuming Real-time Services
• Troubleshooting Service Deployments
• Course Summary

DP-100 - Azure Data Scientist Associate: Model Features & Differential Privacy

Course: 1 Hour, 6 Minutes

• Course Overview
• Machine Learning Global and Local Features
• Machine Learning Model Explainers
• Creating Machine Learning Explainers
• Interpreting Machine Learning Models with Python
• Visualizing Machine Learning Explanations
• Machine Learning Model Bias
• Azure Machine Learning and Fairlearn
• Detecting and Mitigating Model Fairness
• Course Summary

DP-100 - Azure Data Scientist Associate: Machine Learning Model Monitoring

Course: 52 Minutes

• Course Overview
• Application Insights
• Monitoring Models
• Creating Data Drift Monitors
• Visualizing Data Drifts Using ML Studio
• Differential Privacy
• Working with Differential Privacy Using Python
• Course Summary

DP-100 - Azure Data Scientist Associate: Azure Data Storage Monitoring

Course: 1 Hour, 6 Minutes

• Course Overview
• Azure Data Storage Monitoring
• Monitoring Azure Blob Storage
• Monitoring Data Lake Storage Gen
• Monitoring Azure Synapse Analytics
• Monitoring Cosmos DB
• Configuring Azure Monitoring Alerts
• Azure Log Analytics
• Using the Azure Monitor
• Course Summary

DP-100 - Azure Data Scientist Associate: Data Process Monitoring

Course: 1 Hour, 10 Minutes

• Course Overview
• Azure Monitor
• Creating Azure Monitor Metrics
• Analyzing Azure Resource Logs
• Querying Azure Monitor Logs
• Creating Log Analytics Dashboards
• Creating Azure Monitor Alerts
• Monitoring Data Factory Pipelines
• Monitoring Azure Log Analytics
• Course Summary

DP-100 - Azure Data Scientist Associate: Data Solution Optimization

• Course Overview
• Azure Data Solution Optimization
• Data Partitioning Strategies
• Azure Blob Lifecycle Management
• Azure Data Lake Storage Gen2 Optimization
• Azure Stream Analytics Optimization
• Azure Synapse Analytics Optimization
• Azure Cosmos DB Optimization
• Azure Blob Storage Optimization
• Azure Databricks Optimization
• Course Summary

DP-100 - Azure Data Scientist Associate: High Availability & Disaster Recovery

Course: 1 Hour, 33 Minutes

• Course Overview
• High Availability and Disaster Recovery
• SQL Server Business Continuity
• Azure SQL Server Backup and Restore
• Azure Availability Groups
• Creating Azure Availability Groups
• Configuring Virtual Machines for Disaster Recovery
• Azure SQL Server Disaster Recovery
• Azure SQL Database Elastic Pools
• Configuring Elastic Pools for Azure SQL Databases
• Azure Database for PostgreSQL - Hyperscale
• Creating Azure Database for PostgreSQL - Hyperscale
• Exploring PostgreSQL with Hyperscale
• Course Summary

What is DP-100 Designing and Implementing a Data Science Solution on Azure Training?

Microsoft Azure DP-100, or "Designing and Implementing a Data Science Solution on Azure," is an exam and certification aimed at professionals involved in designing and implementing data science solutions on the Azure platform. This exam tests skills and knowledge in using Azure services and tools to create data science workflows, build and deploy machine learning models, and manage and visualize data.

DP-100 focuses on several aspects, including understanding the data science lifecycle, exploring and transforming data, selecting appropriate machine learning techniques, building and training models, implementing solutions in production environments, and monitoring and maintaining models.

By earning DP-100 certification, professionals can demonstrate their expertise in designing and implementing data science solutions on Azure, which can enhance their career opportunities and make them valuable to organizations using Azure for their data science projects.

10 benefits of DP-100 Designing and Implementing a Data Science Solution on Azure Training

1. Acknowledgement of expertise: Earning DP-100 demonstrates that you have the knowledge and skills to design and implement data science solutions on Azure, enhancing your credibility and professional reputation.
2. Career opportunities: DP-100 opens the door to new career opportunities as a data scientist, data engineer, machine learning engineer or Azure consultant, as there is a growing demand for professionals with Azure data science skills.
3. Deep understanding of Azure data science: The DP-100 training and certification provide a thorough understanding of the Azure services and tools used in designing and implementing data science solutions, enabling you to excel in your work.
4. Effective data science workflows: You will learn the best practices and methodologies to design and implement data science workflows, enabling you to work more efficiently and effectively on data science projects.
5. Machine Learning Skills: DP-100 provides you with the opportunity to develop in-depth knowledge and practical skills in machine learning, including building, training and deploying machine learning models on Azure.
6. Azure ecosystem understanding: You will gain an understanding of the broader Azure ecosystem and how data science solutions can be integrated with other Azure services, such as Azure Databricks, Azure Data Factory and Azure Cosmos DB.
7. Understanding data exploration and transformation: You will learn how to explore, transform and cleanse data to gain reliable and actionable insights, which is critical for effective data science projects.
8. Hands-on experience with Azure data science tools: You will get hands-on experience using Azure Machine Learning, Jupyter Notebooks and other relevant Azure tools, which will familiarize you with the latest technologies in the industry.
9. Troubleshooting: DP-100 prepares you to tackle real data science challenges and solve complex problems using Azure data science solutions.
10. Access to a professional network: Passing DP-100 gives you access to a global network of Azure data science professionals, allowing you to learn from others, foster collaboration and build valuable connections in your field.

Why is DP-100 Designing and Implementing a Data Science Solution on Azure important?

DP-100 Designing and Implementing a Data Science Solution on Azure is important because it is an officially recognized certification exam that focuses on the skills and knowledge needed to design and implement effective data science solutions using Microsoft Azure technologies. This exam allows data science and machine learning professionals to validate and demonstrate their expertise and knowledge, which can lead to new career opportunities and improve the quality of their work.
Additionally, the DP-100 certification can help organizations identify qualified professionals capable of designing and implementing effective data science solutions on the Azure platform, which can lead to improved business results and competitive advantage.

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