Final Exam: Data Ops Overview/Description Expected Duration Lesson Objectives Course Number Expertise Level Overview/Description 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.
Expected Duration (hours)
0.0
Lesson Objectives Final Exam: Data Ops
configure a streaming data source using Netcat and write an application to process the stream
configure file system object auditing using Group Policy
connect a web application to AWS IoT using MQTT over WebSockets
contextual data and collective anomaly detection using scikit-learn
create an IAM role on AWS that includes the necessary permissions to interact with the Redshift and S3 services
create charts and dashboards using Qlikview
create dashboards using ELK
create tables, load data, and run queries
demonstrate detecting anomalies using boxplot and scatter plot
demonstrate how to detect anomalies using R, RCP, and the devtools package
demonstrate the essential approaches of using IoT Device Simulator
demonstrate the mathematical approaches of detecting anomalies
describe different uses for data science visualization tools
describe how the use of a message transport decouples a streaming application from the sources of streaming data
describe the cloud architectures of IoT from the perspective of Microsoft Azure, AWS, and GCP
describe the common compliance standards that a data scientist needs to be familiar with including GDPR, HIPPA, PCI DSS, SOC 2
describe the different types of data that are used in analysis and types of visualizations that can be created from the data
describe the various smart data solution implementation frameworks
describe what DevOps is and some of the common functionalities
describe why we need data governance
different uses for data science analytic tools
discuss the five main requirements for data governance
enable Microsoft BitLocker to protect data at rest
generate streams of weather data using the MQTT messaging protocol
identify how data access can be monitored through SIEM and reports
identify the approaches and the steps involved in setting up AWS IoT Greengrass
identify the benefits of rolling out a successful data compliance program
identify the common compliance standards that a data scientist needs to be familiar with including GDPR, HIPPA, PCI DSS, SOC 3
identify the essential components that are involved in building a productive dashboard
identify the role IAM plays in a data governance framework
identify the steps involved in transforming big data to smart data using k-NN
identify the types of data that need to be governed
implement effective security controls to protect data
implement multi-document transaction management using Replica set in MongoDB
install the AWS command line interface and use it to create and delete Redshift clusters
list essential SQL Server change data capture features
list SQL Server rollback mechanisms
list the steps in involved in processing streaming data, the transformation of streams, and the materialization of the results of the transformation
mitigate data breach events by identifying weaknesses
prominent anomaly detection techniques
recall methods of encrypting sensitive data
recognize how to implement clustering on smart data
recognize how to turn big data to smart data and how to use data volumes
recognize the critical benefits provided by leaderboards and scorecards
recognize the differences between batch and streaming data and the types of streaming data sources
recognize the features of change streams in MongoDB
recognize the key aspects of working with structured streaming in Spark
run queries on data in a Redshift cluster and use the query evaluation feature to analyze the query execution metrics
specify how to design a data governance process
specify the different types of dashboards and with their associated features and benefits
understand how data streams are secured
understand how to deploy a VPN using Azure to secure data in motion
understand key security concerns related to NoSQL databases
understand key security risks associated with distributed processing frameworks
use Microsoft System Center Configuration Manager to view managed device security compliance
use SQL Server to rollback databases to a specific point in time
use the AWS console to load datasets to Amazon S3 and then load that data into a table provisioned on a Redshift cluster
use the QuickSight dashboard to generate a time series plot to visualize sales at a retailer over time
use the Redshift Query Editor to create tables, load data, and run queries
work with Spark SQL in order to process streaming data using SQL queries
Course Number: it_fedads_03_enus
Expertise Level
Intermediate