Deploying Data Tools: Data Science Tools

Deploying Data Tools: Data Science Tools

• Course Overview
• describe what a data science platform is
• describe the challenges of deploying data science tools
• identify some considerations for data science tools
• identify and describe each step of a data science workflow
• describe different uses for data science analytic tools
• describe different uses for data science visualization tools
• describe different uses for data science database tools
• list the benefits of deploying cloud-based tools
• list the challenges of deploying cloud-based tools
• describe what DevOps is and some of the common functionalities
• describe DevOps for data science
• identify different uses of data science tools

Explore a variety of new data science tools available today; the different uses for these tools; and the benefits and challenges in deploying them in this 12-video course. First, examine a data science platform, the nucleus of technologies used to perform data science tasks. You will then explore the analysis process to inspect, clean, transform, and model data. Next, the course surveys integrating and exploring data, coding, and building models using that data, deploying the models to production, and delivering results through applications or by generating reports. You will see how a great data science platform should be flexible and scalable, and it should combine multiple features and capabilities that effectively centralize data science efforts. You will learn the six sequential steps of a typical data science workflow, from defining the objective for the project to reporting the results. Finally, explore DevOps, resources that allow developers and IT to work together in harmony which includes people, processes, and infrastructure; and its typical functionalities including integration, testing, packaging, as well as deployment.

Final Exam: Data Ops

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

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.