Course: Azure Data Scientist Associate (Exam: DP-100)

$329.00

$398.09 incl. vat

duration: 36 hours |

Language: English (US) |

access duration: 180 days |

Details

An Azure Data Scientist applies Azure's machine learning techniques to evaluate and deploy models that solve business problems.

The following topics are covered, among others:

Types of machine learning algorithms, including regression algorithms, classification algorithms, and clustering algorithms.
Work with Azure Machine Learning Studio to create machine learning models.
Classification models for machine learning.
Using Jupyter Notebook to perform basic data analysis and create a regression model, classification model, and cluster model.
Using the Azure Machine Learning SDK.
Azure Data Platform Services.
Creating Azure Storage accounts, containers, and file shares, as well as Azure Table and Queue storage.
Encrypt and decrypt blobs using Azure Key Vault.
Differences between structured and unstructured data types.
The features of the Azure Data Factory, such as linked services and datasets, pipelines, and triggers.
Non-relational data stores.
Create, publish, and schedule machine learning pipelines.
Create metric charts using Azure Monitor, collect and analyze Azure resource log data, and query the Azure Monitor logs.
Optimization of data solutions.
And much more!

This training prepares you optimally for the Designing and Implementing a Data Science Solution on Azure DP-100 exam. After obtaining this certification you will be an Azure Data Scientist Associate.

Result

After completing this training you will be familiar with:

Managing Azure resources for machine learning.
Runing experiments and training models.
Deploying and operationalizing machine learning solutions.
Implementing responsible machine learning.

You are also optimally prepared for the Microsoft DP-100 exam.

Prerequisites

You have basic Azure knowledge. You can gain this by following the Microsoft Azure Fundamentals (AZ-900 exam) training. It is also advisable to have knowledge of data science. Finally, you are familiar with Python programming and how to use the Python libraries pandas, scikitlearn, matplotlib and seaborn.

Target audience

Data analist

Content

Azure Data Scientist Associate (Exam: DP-100)

36 hours

DP-1000 - Azure Data Scientist Associate: Machine Learning

Machine Learning uses real data to train algorithms that can be
used for anomaly detection, computer vision, and natural language
processing. In this course, you'll learn about datasets and how to
manipulate data for them. Next, you'll learn the difference between
labeled and unlabeled data and why some AI models require labeled
data. You'll examine the features that should be used for a
selected dataset. Next, you'll learn about the types of machine
learning algorithms that are available, including regression
algorithms, classification algorithms, and clustering algorithms.
Finally, you'll explore the difference between supervised and
unsupervised machine learning models. This course is one in a
collection that prepares learners for the Designing and
Implementing a Data Science Solution on Azure (DP-100) exam.

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

Azure Machine Learning Studio can be used to create and train
machine learning models. Support is provided for multiple
development tools, programming languages such as Python and R, and
numerous machine learning frameworks. In this course, you'll learn
about the services provided by the Azure Machine Learning Studio,
how to create an Azure account, and how to register and signup to
use Azure Machine Learning Studio. You'll also explore available
Azure Machine Learning Studio components, which can be used to
create machine learning workflows, ingest data from an Azure Blob
storage resource, create and use an Azure Machine Learning
workspaces, and create and use a compute resource. This course is
one in a collection that prepares learners for the Designing and
Implementing a Data Science Solution on Azure (DP-100) exam.

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

Machine learning regression models are used to predict numeric
labels for the features of an item. In this course, you'll learn
more about using regression models in the Azure Machine Learning
Studio. First, you'll learn about why regression models are used,
the available types of regression models in machine learning, and
the steps required to train a regression model. Next, you'll
examine the best metrics for determining which regression model to
use. You'll learn how to use a subset of data to train the
regression model and run the training pipeline. Finally, you'll
explore how to use an existing pipeline to create a new inference
pipeline and create and deploy a predictive service. This course is
one in a collection that prepares learners for the Designing and
Implementing a Data Science Solution on Azure (DP-100) exam.

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

Machine learning classification models are used to predict the
class or category that an item belongs to. For example, using
patient characteristics such as age, weight, and BMI to predict if
they are at risk for specific diseases. In this course, you'll
learn about using classification models in the Azure Machine
Learning Studio. You'll explore the available types of
classification models and the steps required to train a
classification model. Next, you'll learn the ideal metrics for
determining the best classification model to use for the given
data. Finally, you'll examine how to use an existing pipeline to
create a new inference pipeline and create and deploy a predictive
service for a classification model. This course is one in a
collection that prepares learners for the Designing and
Implementing a Data Science Solution on Azure (DP-100) exam.

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

Machine learning clustering models are used to group similar
items based on their features and use unsupervised learning. In
this course, you'll learn about using clustering models in the
Azure Machine Learning Studio. First, you'll explore the available
types of clustering models in Azure Machine Learning Studio and the
steps required to train a clustering model. Next, you'll learn how
to train and evaluate a clustering model. Next, you'll examine how
to create a K-means clustering model in Azure Machine Learning
Studio. Finally, you'll learn how to create and deploy a new
inference pipeline to create a predictive service for a clustering
model. This course is one in a collection that prepares learners
for the Designing and Implementing a Data Science Solution on Azure
(DP-100) exam.

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

Data scientists spend a majority of their time exploring and
analyzing data, which may become the foundation for a machine
learning model. The Azure Machine Learning Studio provides Jupyter
Notebooks that can be used to perform data analysis. In this
course, you'll learn about project Jupyter and how it is used for
by data scientists to perform data analysis. You'll explore how to
create a compute instance in Azure Machine Learning Studio and
clone a sample training repository. Next, you'll learn how to use a
Jupyter Notebook to perform basic data analysis and , create a
regression model, classification model, and clustering model.
Finally, you'll examine how to use Jupyter Notebook to perform deep
learning using PyTorch and perform deep learning using TensorFlow.
This course is one in a collection that prepares learners for the
Designing and Implementing a Data Science Solution on Azure
(DP-100) exam.

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

Azure Machine Learning workspaces provide an environment for
performing experiments and managing data, computer targets, and
other assets. Other assets can include notebooks, pipelines, and
trained models. This course will focus on using the Azure Machine
Learning SDK. In this course, you'll learn to create an Azure
Machine Learning workspace by creating a machine learning
resources, creating compute resources, and cloning a notebook.
Next, you'll examine how to install the Machine Learning SDK for
Python and create code to connect to a workspace. You'll learn to
create Python scripts to run an experiment, log metrics, and
retrieve and view logged metrics. Finally, you'll examine how to
use the Azure Machine Learning SDK to run code experiments, create
a script to train a model, and run a notebook using Jupyter to
train predictive models. This course is one in a collection that
prepares learners for the Designing and Implementing a Data Science
Solution on Azure (DP-100) exam.

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

One of the key components of Azure Cloud platform is the ability
to store and process large amounts of data. Azure provides several
data platforms for stored data and numerous services for processing
data. In this course, you'll explore the differences between
structured and unstructured data. You'll learn about some of the
available data storage platforms, including Azure SQL Database,
Azure Cosmos DB, Azure Data Storage, and Azure Data Lake Storage
Gen2. In addition, you'll learn about the data processing services
such as Azure Synapse Analytics, Azure Stream Analytics, Azure
Databricks, Azure Data Factory, and Azure HDInsight, which are all
available to perform operations on the data stored in each of the
data platforms. This course is one in a collection that prepares
learners for the Designing and Implementing a Data Science Solution
on Azure (DP-100) exam.

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

The Azure Cloud platform provides the ability to store various types of data. Azure provides the ability to store data blobs, files, tabular data, and data using a queue. In this course, you'll learn about Azure storage accounts and why they are needed. Next, you'll explore options for storing data using Azure storage containers, Azure file shares, Azure Table storage, and Azure Queue storage. Next, you'll learn about the tools that can be used to create your Azure storage account. Finally, you'll examine how to create Azure storage accounts, containers, and file shares, as well as Azure Table and Queue storage. This course is one in a collection that prepares learners for the Designing and Implementing a Data Science Solution on Azure (DP-100) exam.

DP-100 - Azure Data Scientist Associate: Storage Strategy

When using the Azure Cloud platform for storing data, strategies
must be devised to ensure that the storage solution is the best fit
for the data that is being stored. In this course, you'll learn
strategies for determining the best Azure Storage option based on
the type of data being stored and other factors. Next, you'll
explore the differences between structured and unstructured data
types and strategies and mechanisms for securing storage account
data. Finally, you'll learn how to encrypt and decrypt blobs using
the Azure Key Vault, how to create a virtual machine using an Azure
Storage Account, how to upload data to Azure Storage in parallel,
how to download data from Azure Storage, and how to configure
metrics to monitor data throughput. This course is one in a
collection that prepares learners for the Designing and
Implementing a Data Science Solution on Azure (DP-100) exam.

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

Once you have data in storage, you'll need to have some
mechanism for transforming the data into a usable format. Azure
Data Factory is a data integration service that is used to create
automated data pipelines that can be used to copy and transform
data. In this course, you'll learn about the Azure Data Factory and
the Integration Runtime. Next, you'll explore the features of the
Azure Data Factory, such as linked services and datasets, pipelines
and activities, and triggers. Finally, you'll learn how to create
an Azure Data Factory using the Azure portal, Azure Data Factory
Linked services and datasets, and Azure Data Factory pipelines and
activities, as well as how to trigger a pipeline manually or using
a schedule. This course is one in a collection that prepares
learners for the Designing and Implementing a Data Science Solution
on Azure (DP-100) exam.

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

Unstructured data is prevalent in business and needs specific
datastores for storing and managing this type of data. Azure
provides several database systems that fulfill these needs. In this
course, you'll learn about the types of non-relational data and
available Azure non-relational datastores. Next, you'll explore
Azure Cosmos DB and the available API models that can be used with
it. Next, you'll learn about the features and how to work with
Azure data blobs and Azure Data Lake Storage Gen2. Finally, you'll
examine how to secure data by using dynamic data masking and
encrypting data at rest and in motion in Azure. This course is one
in a collection that prepares learners for the Designing and
Implementing a Data Science Solution on Azure (DP-100) exam.

Azure Data Scientist Associate: Machine Learning Data Stores & Compute

Azure Machine Learning Studio can make use of various types of
data stores and datasets for training and testing data. In this
course, you'll learn about the types of data stores that are
available in Azure, including Azure Storage (blob and file
containers), Azure Data Lake stores, Azure SQL Database, and Azure
Databricks file system. Next, you'll explore how to create and
register data stores and the types of datasets that can be created.
Next, you'll learn how to run a notebook using Jupyter to work with
data, data stores, and datasets, as well as how to create a compute
cluster. You'll examine the available compute targets such as local
compute, compute clusters, and attached compute, as well as the
types of environments. Finally, you'll learn to create and manage a
compute instance and a compute cluster in the Azure Machine
Learning workspace. This course is one in a collection that
prepares learners for the Designing and Implementing a Data Science
Solution on Azure (DP-100) exam.

DP-100 - Data Science Solution on Azure: Machine Learning Orchestration & Deployment

Azure Machine Learning Studio provides DevOps support in the
form of orchestrating machine learning pipelines. In this course,
you'll learn to create, publish, and schedule machine learning
pipelines. First, you'll examine Azure Machine Learning pipelines
and how they are used to build, optimize, and manage machine
learning workflows. Next, you'll explore how to use the Azure
Machine Learning SDK to create and run machine learning pipelines.
You'll learn how to use a pipeline to import, transform, and move
data between steps, as well as how to publish and track pipelines
and use triggers to schedule a machine learning pipeline based on
some event. Finally, you'll learn techniques for troubleshooting
and debugging machine learning pipelines This course is one in a
collection that prepares learners for the Designing and
Implementing a Data Science Solution on Azure (DP-100) exam.

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

The Azure Machine Learning SDK provides components to quantity
the importance of features, identify bias in models, and determine
differential privacy. In this course, you'll learn more about these
features and how they can be used to increase the quality of your
machine learning models. First, you'll examine how models can use
global and local features to quantify the importance of each model
feature. You'll explore how model explainers can be created using
the Azure Machine Learning SDK and how to visualize the model using
the Azure Machine Learning Studio. Next, you'll learn how to use a
Jupyter Notebook and Python to generate explanations that are part
of a model training experiment. Finally, you'll learn about
training model bias and how to analyze model fairness using the
Fairlearn Python package to detect and mitigate unfairness in a
trained model. This course is one in a collection that prepares
learners for the Designing and Implementing a Data Science Solution
on Azure (DP-100) exam.

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

Being able to monitor and analyze an Azure Machine Learning web
service is crucial to determining the correctness of the server.
Azure Machine Learning Studio provides the tools required to
perform this monitoring and analysis. In this course, you'll learn
how application insights can be used to monitor an Azure Machine
Learning web service, as well as to capture and review telemetry
data. Next, you'll examine how to create a data drift monitor and
schedule it to run using Jupyter Notebook and Python. You'll
explore problems relating to data privacy and how differential
privacy works. Finally, you'll learn how to use SmartNoise to
generate and submit differentially private queries. This course is
one in a collection that prepares learners for the Designing and
Implementing a Data Science Solution on Azure (DP-100) exam.

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

Being able to monitor data storage system to ensure they are
operational and working correctly is a crucial part of running your
business. Azure provides the Azure Monitor service and the Azure
Log Analytics service to perform this function. In this course, you
will learn about the features Azure Log Analytics and the Azure
Monitor service and how it can be used to monitor storage data and
monitor Azure Blob storage. Next, you'll learn how to access
diagnostic logs to monitor Data Lake Storage Gen2, how to monitor
the Azure Synapse Analytics jobs and the adaptive cache and how to
monitor the Azure Cosmos DB using the portal and resource logs.
Finally, you'll learn how to configure, manage, and view metric
alerts using the Azure Monitor and how to configure, manage, and
view activity log alerts using the Azure Monitor. This course is
one in a collection that prepares learners for the Designing and
Implementing a Data Science Solution on Azure (DP-100) exam.

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

Being able to monitor data processes to ensure they are
operational and working correctly is a crucial part of running your
business. Azure provides the Azure Monitor and Azure Log Analytics
services to perform this function. In this course, you'll learn
about the features of the Azure Monitor tools and the concepts of
continuous monitoring and visualization. You'll explore how to
create metric charts using the Azure Monitor, collect and analyze
Azure resource log data, and perform queries against the Azure
Monitor logs. Next, you'll examine how to create and share
dashboards that display data from Log Analytics, create Azure
Monitor alerts, and use the Azure Data Factory Analytics solution
to monitor pipelines. Finally, you'll learn how to query Azure Log
Analytics and filter, sort, and group query results. This course is
one in a collection that prepares learners for the Designing and
Implementing a Data Science Solution on Azure (DP-100) exam.

Data Science Solution on Azure: Data Solution Optimization

Ensuring that data storage and processing systems are operating
efficiently will allow your organization to save both time and
money. There are several tips and tricks that can be used to
optimize both Azure Data Storage service and processes. In this
course, you'll learn about cloud optimization and best practices
for optimizing data using data partitions, Azure Data Lake Storage
tuning, Azure Synapse Analytics tuning, and Azure Databricks
auto-optimizing. You'll examine strategies for partitioning data
using Azure-based storage solutions. Next, you'll explore the
stages of the Azure Blob lifecycle management and how to optimize
Azure Data Lake Storage Gen2, Azure Stream Analytics, and Azure
Synapse Analytics. Finally, you'll learn about optimizing Azure
Data Storage services such Azure Cosmos DB using indexing and
partitioning, as well as Azure Blob Storage and Azure Databricks.
This course is one in a collection that prepares learners for the
Designing and Implementing a Data Science Solution on Azure
(DP-100) exam.

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

Organizations rely on systems and data to be available and
operational when they are needed to manage and run their
businesses. Azure provides functionality to ensure high
availability of storage systems and the mechanism to ensure a swift
and painless disaster recovery strategy. In this course, you'll
learn about high availability and disaster recovery, and how they
are related to each other and used to provide business continuity.
Next, you'll examine how to back up, store, and restore SQL Server
databases on virtual machine instances. You'll move on to explore
the purpose and features of Azure Always On availability groups and
elastic pools, as well as how they are used by Azure SQL databases
to provide business continuity. Finally, you'll learn about using
Azure Database for PostgreSQL - Hyperscale to create highly
available and distributed databases. This course is one in a
collection that prepares learners for the Designing and
Implementing a Data Science Solution on Azure (DP-100) exam.

Course options

We offer several optional training products to enhance your learning experience. If you are planning to use our training course in preperation for an official exam then whe highly recommend using these optional training products to ensure an optimal learning experience. Sometimes there is only a practice exam or/and practice lab available.

Optional practice exam (trial exam)

To supplement this training course you may add a special practice exam. This practice exam comprises a number of trial exams which are very similar to the real exam, both in terms of form and content. This is the ultimate way to test whether you are ready for the exam.

Optional practice lab

To supplement this training course you may add a special practice lab. You perform the tasks on real hardware and/or software applicable to your Lab. The labs are fully hosted in our cloud. The only thing you need to use our practice labs is a web browser. In the LiveLab environment you will find exercises which you can start immediately. The lab enviromentconsist of complete networks containing for example, clients, servers,etc. This is the ultimate way to gain extensive hands-on experience.

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