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Matt Milner,
Xavier Morera,
Tim Warner,
Janani Ravi,
Mike West,
Benjamin Culbertson,
Jared Rhodes,
Axel Sirota,
Neeraj Kumar,
David Tucker,
Saravanan Dhandapani,
Emilio Melo,
Michael Heydt,
Bismark Adomako,
Steph Locke,
Paul Foran,
and
Ravikiran Srinivasulu
Microsoft Azure offers a set of related services to address the day-to-day workflow of a data scientist. This skill teaches how these Azure services work together to enable various parts of this workflow.This path is designed to address the Microsoft DP-100 certification exam.
Read more
Microsoft Azure offers a set of related services to address the day-to-day workflow of a data scientist. This skill teaches how these Azure services work together to enable various parts of this workflow.This path is designed to address the Microsoft DP-100 certification exam.
Read more
Microsoft Azure offers a set of related services to address the day-to-day workflow of a data scientist. This skill teaches how these Azure services work together to enable various parts of this workflow.This path is designed to address the Microsoft DP-100 certification exam.
What You'll Learn
Register for this course and see more details by visiting:
OpenCourser.com/collection/5ie282/microsoft
What's inside
21 courses
Understanding Ethical, Legal, and Security Issues in Data Science
(1 hours)
Understanding Ethical, Legal, and Security Issues in Data Science
(1 hours)
Data is being called the new oil, as you can generate so much value from it. However, if it's not properly secured, compliant, and used ethically, it can quickly go from asset to liability. In this course, you'll learn how to manage data in Azure.
Communicating Expectations to the Business
(0 hours)
Communicating Expectations to the Business
(0 hours)
Learn how to manage stakeholders' expectations for a solution, procure data for training a model, and explore synthetic data generation to save your project and budget.
Representing, Processing, and Preparing Data
(2 hours)
Representing, Processing, and Preparing Data
(2 hours)
This course covers data processing tools, including spreadsheets, Python, and relational databases, and deals with data quality issues and visualizing data for insight generation.
Sourcing Data in Microsoft Azure
(1 hours)
Sourcing Data in Microsoft Azure
(1 hours)
This course targets software developers looking to source data from inside and outside of the cloud. You'll learn foundational knowledge of data types, data policy, and finding data.
Cleaning and Preparing Data in Microsoft Azure
(1 hours)
Cleaning and Preparing Data in Microsoft Azure
(1 hours)
This course targets software developers and data scientists looking to understand the initial steps in a machine learning solution. The content will showcase methods and tools available using Microsoft Azure.
Combining and Shaping Data
(3 hours)
Combining and Shaping Data
(3 hours)
This course covers both conceptual and practical aspects of pulling together data from different sources, with different schemas and orientations, into a cohesive whole using Excel, Python, and various tools available on the Azure cloud platform.
Summarizing Data and Deducing Probabilities
(2 hours)
Summarizing Data and Deducing Probabilities
(2 hours)
This course covers the most important aspects of exploratory data analysis using different univariate, bivariate, and multivariate statistics from Excel and Python.
Experimental Design for Data Analysis
(2 hours)
Experimental Design for Data Analysis
(2 hours)
This course covers building and evaluating machine learning models, using data judiciously and accounting for biases.
Interpreting Data with Statistical Models
(2 hours)
Interpreting Data with Statistical Models
(2 hours)
Data is everywhere, and we often hear about statistics. Over this course, we will shape up our statistical knowledge, going from zero to hero analyzing complex patterns of everyday real-world problems.
Interpreting Data with Advanced Statistical Models
(3 hours)
Interpreting Data with Advanced Statistical Models
(3 hours)
Machine Learning is changing the world. With this course, you will know how to create an ML application for problems that appear at your work and understand the basis behind it. You will learn the basics of Machine learning, linear regression, how to classify with Logistic Regression, SVMs, and Bayesian methods, and the intrinsic patterns of data with unsupervised techniques such as K Means and PCA.
Communicating Data Insights
(2 hours)
Communicating Data Insights
(2 hours)
This course covers the key statistical and technical tools needed to convey clear, actionable insights from data to senior executives, including the use of powerful visualizations such as Sankey diagrams, funnel plots, and candlestick plots.
Exploring Data in Microsoft Azure Using Kusto Query Language and Azure Data Explorer
(2 hours)
Exploring Data in Microsoft Azure Using Kusto Query Language and Azure Data Explorer
(2 hours)
You will learn about Azure's data exploration service and its integration with other services for end-to-end data analytics. This course will provide you with the skills and confidence to be a data scientist.
Building, Training, and Validating Models in Microsoft Azure
(1 hours)
Building, Training, and Validating Models in Microsoft Azure
(1 hours)
This course provides a roadmap for Microsoft Azure Data Scientists to build, train, and validate machine learning models in Azure. It covers model selection, data preparation, feature engineering, model training, and evaluation. By building a model to predict flight delays, learners will gain practical experience in applying Azure ML to real-world problems.
Preparing Data for Feature Engineering and Machine Learning in Microsoft Azure
(2 hours)
Preparing Data for Feature Engineering and Machine Learning in Microsoft Azure
(2 hours)
In this course, you will learn how to prepare, clean up, and engineer new features from data with Azure Machine Learning. This will allow the dataset to be represented in a form that's easy for the learning algorithm to learn the patterns.
Building Features from Nominal and Numeric Data in Microsoft Azure
(1 hours)
Building Features from Nominal and Numeric Data in Microsoft Azure
(1 hours)
Applying statistical techniques to your data within Azure Machine Learning Service will often boost model performance. This course will teach you the basics of data cleansing, including basic syntax and functions.
Feature Selection and Extraction in Microsoft Azure
(1 hours)
Feature Selection and Extraction in Microsoft Azure
(1 hours)
One of the most important aspects of Machine Learning is using the right data for your models. In this course, you will learn how to extract, normalize, and select the best features for your models using Azure Machine Learning Studio.
Building Features for Computer Vision in Microsoft Azure
(1 hours)
Building Features for Computer Vision in Microsoft Azure
(1 hours)
This course covers how to leverage both algorithmic and deep learning approaches for building features from image data on Microsoft Azure.
Reducing Complexity in Data in Microsoft Azure
(2 hours)
Reducing Complexity in Data in Microsoft Azure
(2 hours)
In machine learning, feature sets can quickly become complicated and unwieldy. This course will provide you with the skills needed to reduce the complexity of your feature sets to help ensure you get better and more consistent insights into your data.
Developing Models in Microsoft Azure
(1 hours)
Developing Models in Microsoft Azure
(1 hours)
It's common to spend a lot of resources on identifying an optimal machine learning model. This course will teach you how to simplify this process using various cutting edge features offered by Microsoft Azure Machine Learning service.
Evaluating Model Effectiveness in Microsoft Azure
(0 hours)
Evaluating Model Effectiveness in Microsoft Azure
(0 hours)
This course is for data science practitioners who use Azure Machine Learning Service and want to improve their ML model accuracy, efficiency, and explainability.
Deploying and Managing Models in Microsoft Azure
(0 hours)
Deploying and Managing Models in Microsoft Azure
(0 hours)
In this course, you'll learn how to manage the life cycle of models using tools in Microsoft Azure.
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Time
2572 hours
Via
Pluralsight
Institution
Instructors
Matt Milner
Xavier Morera
Tim Warner
Janani Ravi
Mike West
Benjamin Culbertson
Jared Rhodes
Axel Sirota
Neeraj Kumar
David Tucker
Saravanan Dhandapani
Emilio Melo
Michael Heydt
Bismark Adomako
Steph Locke
Paul Foran
Ravikiran Srinivasulu
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