Final Exam: Data Ops
1 Video | 30m 32s
- Includes Assessment
- Earns a Badge
Final Exam: Data Ops will test your knowledge and application of the topics presented throughout the Data Ops track of the Skillsoft Aspire Data Analyst to Data Scientist Journey.
WHAT YOU WILL LEARN
configure a streaming data source using Netcat and write an application to process the streamconfigure file system object auditing using Group Policyconnect a web application to AWS IoT using MQTT over WebSocketscontextual data and collective anomaly detection using scikit-learncreate an IAM role on AWS that includes the necessary permissions to interact with the Redshift and S3 servicescreate charts and dashboards using Qlikviewcreate dashboards using ELKcreate tables, load data, and run queriesdemonstrate detecting anomalies using boxplot and scatter plotdemonstrate how to detect anomalies using R, RCP, and the devtools packagedemonstrate the essential approaches of using IoT Device Simulatordemonstrate the mathematical approaches of detecting anomaliesdescribe different uses for data science visualization toolsdescribe how the use of a message transport decouples a streaming application from the sources of streaming datadescribe the cloud architectures of IoT from the perspective of Microsoft Azure, AWS, and GCPdescribe the common compliance standards that a data scientist needs to be familiar with including GDPR, HIPPA, PCI DSS, SOC 2describe the different types of data that are used in analysis and types of visualizations that can be created from the datadescribe the various smart data solution implementation frameworksdescribe what DevOps is and some of the common functionalitiesdescribe why we need data governancedifferent uses for data science analytic toolsdiscuss the five main requirements for data governanceenable Microsoft BitLocker to protect data at restgenerate streams of weather data using the MQTT messaging protocolidentify how data access can be monitored through SIEM and reportsidentify the approaches and the steps involved in setting up AWS IoT Greengrassidentify the benefits of rolling out a successful data compliance programidentify the common compliance standards that a data scientist needs to be familiar with including GDPR, HIPPA, PCI DSS, SOC 3identify the essential components that are involved in building a productive dashboardidentify the role IAM plays in a data governance framework
identify the steps involved in transforming big data to smart data using k-NNidentify the types of data that need to be governedimplement effective security controls to protect dataimplement multi-document transaction management using Replica set in MongoDBinstall the AWS command line interface and use it to create and delete Redshift clusterslist essential SQL Server change data capture featureslist SQL Server rollback mechanismslist the steps in involved in processing streaming data, the transformation of streams, and the materialization of the results of the transformationmitigate data breach events by identifying weaknessesprominent anomaly detection techniquesrecall methods of encrypting sensitive datarecognize how to implement clustering on smart datarecognize how to turn big data to smart data and how to use data volumesrecognize the critical benefits provided by leaderboards and scorecardsrecognize the differences between batch and streaming data and the types of streaming data sourcesrecognize the features of change streams in MongoDBrecognize the key aspects of working with structured streaming in Sparkrun queries on data in a Redshift cluster and use the query evaluation feature to analyze the query execution metricsspecify how to design a data governance processspecify the different types of dashboards and with their associated features and benefitsunderstand how data streams are securedunderstand how to deploy a VPN using Azure to secure data in motionunderstand key security concerns related to NoSQL databasesunderstand key security risks associated with distributed processing frameworksuse Microsoft System Center Configuration Manager to view managed device security complianceuse SQL Server to rollback databases to a specific point in timeuse the AWS console to load datasets to Amazon S3 and then load that data into a table provisioned on a Redshift clusteruse the QuickSight dashboard to generate a time series plot to visualize sales at a retailer over timeuse the Redshift Query Editor to create tables, load data, and run querieswork with Spark SQL in order to process streaming data using SQL queries
IN THIS COURSE
1.Data Ops33sUP NEXT
EARN A DIGITAL BADGE WHEN YOU COMPLETE THIS COURSE
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