Data Science Bundle

confirmed_icon Izvedba potrjena Ne
duration_icon Trajanje (dni) 5
price_icon Cena brez DDV 1.350,00 €
num_of_hours_icon Število ped. ur 45


Oznaka tečaja: DSB SATV voucher: Ne

Data Science with R and SQL Server And Designing and Implementing a Data Science Solution on Azure

About this course:

R is the most popular environment and language for statistical analyses, data mining, and machine learning. Python is quickly getting popularity. Managed and scalable version of R and Python run in SQL Server, Power BI, and Azure ML. The delegates learn how to use both languages for data science applications and how to integrate them in many tools available in MS BI suite, including SQL Server, Power BI, and Azure ML. In the second part of the course, attendees learn how to operate machine learning solutions at cloud scale using Azure Machine Learning. This course also teaches delegates to leverage their existing knowledge to manage data ingestion and preparation, model training and deployment, and machine learning solution monitoring in Microsoft Azure.

Audience profile:

This course is designed for data scientists with some basic knowledge of Python and / or R languages, who want to build and operate machine learning solutions on premises and in the cloud.


Attendees should have basic understanding of data analysis and basic familiarity with SQL Server tools. A fundamental knowledge of Microsoft Azure is requested as well.

Experience of writing Python code to work with data, using libraries such as Numpy, Pandas, and Matplotlib, is very welcome. Any knowledge of writing R code helps as well.

Course Outline:

  • Introducing data science and R
  • Introducing Python
  • Data overview
  • Data preparation
  • Associations between two variables and visualizations of associations
  • Feature selection and matrix operations
  • Unsupervised learning
  • Modern topics
  • R in SQL Server and MS BI
  • Introduction to Azure Machine Learning
  • No-Code Machine Learning with Designer
  • Running Experiments and Training Models
  • Working with Data
  • Compute Contexts
  • Orchestrating Operations with Pipelines
  • Deploying and Consuming Models
  • Training Optimal Models
  • Interpreting Models
  • Monitoring Models

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