In this session, we will cut through the marketing buzzwords to share experiences, tips, and tricks on how to be successful with Data Science and Analytics in the real world. Tune in to hear the team share real-world experience and get takeaways from industry insiders on real projects with impact. We will also discuss the ethics and fairness of Data Science and Analytics projects and how we can be more inclusive from a technology, people, and process standpoint.
The video is not available to view online.
Only the curious will learn and only the resolute overcome the obstacles to learning.
Building a Data Quality implementation in Purview using the Glossary and Databricks
This session will discuss a custom solution we have built to solve the problem of row level encryption in a highly complex data lake.
The art of choosing the perfect set of actions to reach the ultimate value that can be achieved.
Curious what Azure Synapse Analytics brings to the table? Bring your scuba gear, as we take a dive and explore everything it has to offer!
An introduction to becoming a Data Engineer, Anna, Mikey and Ust will introduce the technology stack, tools and development skills needed for data engineering and show you how and where to go to learn them. We'll also show you how the skills you already have can kickstart your journey to becoming a Data Engineer.
This session is designed to explore some of the powerful features that made Databricks the leader in the Gartner 2021 Quadrant for Data Science and Machine Learning.
The video is not available to view online.
Tinkered with Powershell but not with Python yet? Let's get you started, and we'll do that with some practical examples focussed around talking to SQL Server and importing and exporting some data.
DBA and Data scientists should work together! Analyzing data gathered with XE and Query Store data using R or Python for better database insight and discovering hidden patterns.
The video is not available to view online.
The most challenging area of machine learning are Data acquisition, Feature extraction, Feature Selection. Almost in all data science project, 80% of time people spend in Data acquisition and Feature engineering.
AI and data is at the center of the digital feedback loops. We have invested in a comprehensive portfolio of AI tools, infrastructure and services. Come to this session to get an update of Azure AI with demos.
Taken from the 20+ years of field experiences, many common statistical and data science mistakes have been detected. Session will tackle couple of them.
The video is not available to view online.
Learn how Azure supports interactive, exploratory notebooks (e.g. Jupyter) for data processing and experimentation across a range of scales from simple single-computer work up to massively parallel Databricks clusters.
Learn to make better analytic solutions by following current thoughts on data modeling.
The video is not available to view online.
Databricks, Lakes & Parquet are a match made in heaven, but explode with extra power when using Delta Lake. This session will dive into the details of how Databricks Delta works and how to make the most of it.
In this session, we will discover how to utilize common machine learning approaches for daily SQL Server DBA tasks.
This short session will cover some scenarios of cleansing data in Power Query using M, Python, or both languages.
In this session we focus on how Spark implements Machine Learning at Scale with Spark ML.
This session is for all developers who want to learn about the new Dev features and enhancements of SQL Server 2017 and 2019