Core Data Pro
Skill Me Up expert on-demand training for data professionals. Get started in your data professional career. Learn core technical skills such as R, Python, SQL Server, T-SQL and More!Live Course Schedule
This learning path is designed to teach you the fundamentals of relational databases using Microsoft SQL Server. You will learn core concepts such as tables, indexes, and building relationships with foreign keys. From there, you will learn how to write queries using T-SQL to query data as well as make changes to your database.
In this learning path, you will learn the basics of data science including what data science is, some of the common programming languages used in data science (R and Python) as well as an introduction to machine learning.
This course is an introduction to Microsoft Azure Machine Learning Services. In this course you will learn to navigate the AML Services interface, create notebook servers, create compute clusters, manage AML Services from a notebook, deploy models, and create an Automated Machine Learning experiment.
The course will teach you the fundamentals of the relational database model and how to access data stored in relational databases. The course will give students an understanding of relational database concepts and teach the practical application of these concepts through the T-SQL programming language for Microsoft SQL Server and Azure SQL Database.
This course is an introduction to Python. In this course you will learn which IDE is right for you, print statements, data types, control flow, Python functions and anonymous functions, methods, file io, and an introduction to Python packages.
This course covers introduction to the R Language. We start off with an introduction to R verisons and R Editions then move to R the language. From there will dive into one of R’s strongest features, Graphics. Using base R graphics and GGPlot you will learn how to get started, and learn how to create your own visualizations.
This course builds on your Power BI skills and walks you through the interfaces of both the Online and Desktop offerings before embarking on a journey that will show you how to ingest data, transform data, create reports and dashboards before publishing and using your data sets, reports and dashboards in the Power BI online tenant.The course will help prepare students to take the Microsoft 70-778, Analyzing and Visualizing Data with Power BI certification exam.
This course serves as an introduction to the T-SQL programming language. This course is designed to give students a strong foundation in the T-SQL language which is used by all variants of SQL Server both on-premises and in the cloud.
In this lab, you will explore columnstore indexes in SQL Server 2016. You will evaluate the performance improvements you will get when you implement columnstore indexes on tables for your analytical workloads.
In this lab, you will learn the basics of importing CSV files into a Power BI model and adjusting query and column properties to prepare them for your data model. Then you will set table and column properties in the data model and create relationships between tables.
In this lab, you will explore the new SQL Server 2016 In-Memory OLTP feature. You will evaluate the performance improvements you will get when you migrate disk-based tables and interpreted T-SQL stored procedures into memory-optimized tables and natively-compiled stored procedures, respectively.
In this lab, you will configure and manage the query store in SQL Server 2016 to collect runtime statistics, queries, query plan history and other workload history within the database to assist with troubleshooting query performance issues, you will then identify and resolve poor performing queries in your database using SQL Server 2016 Query Store. You will also identify query plan regressions and how to address them with information gathered from the Query Store.
In this lab, you will learn the basics of time intelligence measures to show year-to-date, quarter-to-date, and month-to-date totals. Then you will use DAX to enhance the data model and build a table that lets users choose which measures they want to see.