Final Exam: Data Ops
- 1 Video | 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
identify the benefits of rolling out a successful data compliance programdescribe the common compliance standards that a data scientist needs to be familiar with including GDPR, HIPPA, PCI DSS, SOC 2create dashboards using ELKspecify the different types of dashboards and with their associated features and benefitsidentify the common compliance standards that a data scientist needs to be familiar with including GDPR, HIPPA, PCI DSS, SOC 3implement effective security controls to protect datadescribe the different types of data that are used in analysis and types of visualizations that can be created from the datademonstrate the essential approaches of using IoT Device Simulatoruse Microsoft System Center Configuration Manager to view managed device security compliancerecognize how to turn big data to smart data and how to use data volumesunderstand key security risks associated with distributed processing frameworksdemonstrate detecting anomalies using boxplot and scatter plotconnect a web application to AWS IoT using MQTT over WebSocketsunderstand how to deploy a VPN using Azure to secure data in motionprominent anomaly detection techniquesrecognize the features of change streams in MongoDBenable Microsoft BitLocker to protect data at restlist the steps in involved in processing streaming data, the transformation of streams, and the materialization of the results of the transformationdemonstrate how to detect anomalies using R, RCP, and the devtools packageuse the AWS console to load datasets to Amazon S3 and then load that data into a table provisioned on a Redshift clustercreate tables, load data, and run queriesuse SQL Server to rollback databases to a specific point in timeconfigure a streaming data source using Netcat and write an application to process the streamrecall methods of encrypting sensitive dataidentify the types of data that need to be governedidentify the steps involved in transforming big data to smart data using k-NNdiscuss the five main requirements for data governancework with Spark SQL in order to process streaming data using SQL queriesgenerate streams of weather data using the MQTT messaging protocolspecify how to design a data governance process
recognize the key aspects of working with structured streaming in Sparkmitigate data breach events by identifying weaknessesdescribe why we need data governancedifferent uses for data science analytic toolsidentify the role IAM plays in a data governance frameworkcreate charts and dashboards using Qlikviewcontextual data and collective anomaly detection using scikit-learndescribe different uses for data science visualization toolsimplement multi-document transaction management using Replica set in MongoDBunderstand how data streams are secureddescribe how the use of a message transport decouples a streaming application from the sources of streaming datainstall the AWS command line interface and use it to create and delete Redshift clustersidentify how data access can be monitored through SIEM and reportsunderstand key security concerns related to NoSQL databasesidentify the approaches and the steps involved in setting up AWS IoT Greengrassdemonstrate the mathematical approaches of detecting anomaliesuse the Redshift Query Editor to create tables, load data, and run queriesdescribe the various smart data solution implementation frameworkscreate an IAM role on AWS that includes the necessary permissions to interact with the Redshift and S3 serviceslist essential SQL Server change data capture featuresdescribe the cloud architectures of IoT from the perspective of Microsoft Azure, AWS, and GCPlist SQL Server rollback mechanismsconfigure file system object auditing using Group Policyrecognize how to implement clustering on smart datarecognize the differences between batch and streaming data and the types of streaming data sourcesrun queries on data in a Redshift cluster and use the query evaluation feature to analyze the query execution metricsidentify the essential components that are involved in building a productive dashboarddescribe what DevOps is and some of the common functionalitiesuse the QuickSight dashboard to generate a time series plot to visualize sales at a retailer over timerecognize the critical benefits provided by leaderboards and scorecards
IN THIS COURSE
1.Data Ops33sUP NEXT
EARN A DIGITAL BADGE WHEN YOU COMPLETE THIS COURSE
Skillsoft is providing you the opportunity to earn a digital badge upon successful completion of this course, which can be shared on any social network or business platformDigital badges are yours to keep, forever.