Data Science with Microsoft Azure (Path)

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.
What you will learn
• Address the business requirements for a data science projects
• Source, collect, and transform data into shapes appropriate for data modeling and machine learning
• Extract features from complex data sources, such as documents and images
• Build and interpret statistical and machine learning models
• Glean insights from data, and communicate them back to the business
This path is intended for learners familiar with data science workflows and general principles, but who have never applied these on Microsoft Azure.
Courses in this pathA. Bringing Data Science to the Business
The courses in this section teach you how data science fits into a business, and addresses legal and ethical issues that arise in data science. In addition, this part of the path discusses the role of effective communication as part of the data science workflow.
A1. Analyzing Business Requirements for Data Science (Paul Foran, 2019)
A2. Understanding Ethical, Legal, and Security Issues in Data Science (Emilio Melo, 2020)
A3. Communicating Expectations to the Business (Benjamin Culbertson, 2019)
B. Preparing Data for Analysis and Modeling
This section teaches you how to source, clean, and shape your data for further analysis in Microsoft Azure.
B1. Representing, Processing, and Preparing Data (Janani Ravi, 2019)
B2. Sourcing Data in Microsoft Azure (Jared Rhodes, 2019)
B3. Cleaning and Preparing Data in Microsoft Azure (Jared Rhodes, 2019)
B4. Combining and Shaping Data (Janani Ravi, 2020)
C. Building Statistical Models in Microsoft Azure
The courses in this section apply descriptive and inferential statistics to data using Microsoft Azure.
C1. Summarizing Data and Deducing Probabilities (Janani Ravi, 2021)
C2. Experimental Design for Data Analysis (Janani Ravi, 2019)
C3. Interpreting Data with Statistical Models (Axel Sirota, 2020)
C4. Interpreting Data with Advanced Statistical Models (Axel Sirota, 2019)
C5. Communicating Data Insights (Janani Ravi, 2020)
D. Exploring and Modeling Data in Microsoft Azure
This part of the path teaches how to leverage Azure services as part of everyday data science, including the use of notebooks, data exploration tools, and model building.
D1. Building Your First Data Science Project in Microsoft Azure (Jared Rhodes, 2020)
D2. Exploring Data in Microsoft Azure Using Kusto Query Language and Azure Data Explorer (Neeraj Kumar, 2019)
D3. Building, Training, and Validating Models in Microsoft Azure (Bismark Adomako, 2020)
E. Feature Engineering in Microsoft Azure
Data must be represented in a manner appropriate for the analysis or model being used. This part of the path addresses feature engineering and feature extraction on Microsoft Azure.
E1. Preparing Data for Feature Engineering and Machine Learning in Microsoft Azure (Ravikiran Srinivasulu, 2019)
E2. Building Features from Nominal and Numeric Data in Microsoft Azure (Mike West, 2019)
E3. Feature Selection and Extraction in Microsoft Azure (Xavier Morera, 2019)
E4. Building Features from Text Data in Microsoft Azure (Michael Heydt, 2019)
E5. Building Features for Computer Vision in Microsoft Azure (David Tucker, 2020)
E6. Reducing Complexity in Data in Microsoft Azure (Steph Locke, 2019)
F. Building and Deploying Models in Microsoft Azure
Finally, this section of the skill addresses operational aspects of your data models, such as deploying them for use in Microsoft Azure, monitoring and evaluating their effectiveness, and communicating their insights back to the business.
F1. Developing Models in Microsoft Azure (Saravanan Dhandapani, 2020)
F2. Evaluating Model Effectiveness in Microsoft Azure (Tim Warner, 2019)
F3. Deploying and Managing Models in Microsoft Azure (Jared Rhodes, 2020)
F4. Communicating Insights from Microsoft Azure to the Business (Neeraj Kumar, 2020)

Homepage

https://www.pluralsight.com/paths/data-science-with-microsoft-azure]https://anonymz.com/?
https://www.pluralsight.com/paths/data-science-with-microsoft-azure

Screenshots

#