Statistical methods to detect outliers & Data Warehouse Modernization with AWS

<p>Details:<br/>QuantumBlack has agreed to sponsor our August Meetup<br/>There will be a couple of talks, lots of networking along with pizza and beer!<br/>Please bring an official ID to get into the building.</p> <p>-------------------------------------------------------------------------------------------------</p> <p>Agenda:<br/>6:00pm - 6:45pm<br/>Meet and greet - Welcome<br/>6:45pm - 7:45pm<br/>- Robustly detecting outliers in nonlinear data using the ROUT method - Talk by Deepyaman Datta<br/>- Data Warehouse Modernization with AWS - Talk by Dmitry Anoshin<br/>7:45pm - 8:15pm<br/>Networking</p> <p>-------------------------------------------------------------------------------------------------</p> <p>Speaker:<br/>- Deepyaman Datta<br/>- Dmitry Anoshin</p> <p>-------------------------------------------------------------------------------------------------</p> <p>Abstract:</p> <p>Robustly detecting outliers in nonlinear data using the ROUT method:<br/>Detecting outliers is a regular part of the data cleansing data engineers performs. However, standard outlier detection methods do not generalize to nonlinear regression. He presents an overview of the ROUT method and an application thereof, together with a comparison to other outlier detection methods on our dataset.</p> <p>Data Warehouse Modernization with AWS:<br/>With the rise of Cloud Computing and Analytics, organizations got almost unlimited capabilities to become data-driven organizations. However, it is not so easy for organizations with legacy technology stack. During this talk, he will cover the journey of Data Warehouse migration projects from an on-premise solution to AWS. Moreover, he will cover the ETL tool selection process for the Cloud DW as well as the adoption process for the end-users. Moreover, he will talk about extending of traditional Data Warehouse for Big Data use cases using EMR and Spark as well as setting Data Lake. Finally, he will make a quick demo of Cloud ETL (Matilion ETL) that accelerated cloud migration projects.</p> <p>-------------------------------------------------------------------------------------------------<br/>Speaker Bio:<br/>Deepyaman Datta is a Senior Data Engineer at QuantumBlack. He also leads Data Engineering R&amp;D for QuantumBlack Boston, with research interests in the application of machine learning to data engineering activities. Prior to QuantumBlack, Deepyaman was a Research Scientist at Schlumberger. He earned his Master’s Degree in Computer Science, specializing in Information Management and Analytics, from Stanford University.</p> <p>Dmitry is a data-centric technologist and recognized expert in building and implementing Data Analytics Solutions. He has over 10 years of experience with Data Engineering and Analytics. He worked across different industries in Europe and North America and delivered end-to-end analytics solutions. Currently, he is a Data Engineer with Alexa AI in Cambridge, MA and works remotely from Vancouver Island. His main interest now is Cloud Analytics with AWS, Azure, and GCP. Moreover, he is the author of 4 books about various BI tools with PacktPub (SAP Lumira Essentials, Learning Hunk (Splunk), Mastering BI with Microstrategy 10, Tableau Cookbook 2019) and writing couple new book: Snowflake with Apress and Tableau Certification Guide with PacktPub. He often presents at Tableau User Groups, AWS and Azure user groups and huge data conferences like Enterprise Data World. He is leading Amazon Tableau User Group with 2k+ members as well as BC Tableau User Group. Finally, he is contributing to the leading Canadian Analytics firm Rock Your Data and writing blog posts (<a href="https://medium.com/rock-your-data" class="linkified">https://medium.com/rock-your-data</a>). He has a Ph.D. and Master's Degree from Moscow State Technology University.</p> <p>-------------------------------------------------------------------------------------------------<br/>Sponsors:<br/>This event is sponsored by QuantumBlack, a McKinsey Company.</p>