Data Analytics Literacy / Data Science Literacy (Path)

[LeeAndro]

Janani Ravi (et al.) | Duration: 22:00 h | Video: H264 1280x720 | Audio: AAC 48 kHz 2ch | 2,81 GB | Language: English

Data Analytics is the detection, interpretation, and communication of meaningful patterns in data.​

Data science is a diverse field where scientific methods, software programming, and data analytics combine to glean insights from data, communicate those insights, and empower a business to take appropriate actions.

This skill path provides foundational knowledge behind data science, specifically with its application in Microsoft Azure.

What you will learn

Describe the general analytics workflow
Differentiate data types and identify analyses suitable for specific types of data
Detee which analysis is appropriate for a specific business problem
Apply hypothesis testing to a new business problem
Describe the key components of an RDBMS (Relational Database Management System) architecture query and process data using OLTP (Online Transactional Processing) systems write portable SQL queries against data define schemas describe common database programming constructs (stored procedures, triggers, views, etc)
Describe the components of an OLAP (Online Analytical Processing) system differentiate tabular vs cube data models writing analytical queries working with nested/repeated data dealing with streaming data in an OLAP context
Describe the components of a NoSQL (Not Only SQL) database
Differentiate columnar/wide-column databases vs document databases
Identify when each is appropriate
Describe common methods for getting data in and out of systems - scripting (including specialty languages such as Pig), bulk loading, streaming inserts
Compare and contrast the ETL (extract, transform, and load) workflow with the LET workflow (load, extract, and transform)
Describe the "four v's" of Big Data and how they are used to differentiate Big Data problems from "small data"
Describe the pros and cons of using cloud vs on-premise solutions for data management
Describe the pros and cons of using "handrolled" Hadoop/Hive/Spark vs proprietary systems like Teradata/Oracle
Identify key decision factors between services on AWS, Azure, GCP etc
Describe the general analytics workflow
Differentiate data types and identify analyses suitable for specific types of data
Detee which analysis is appropriate for a specific business problem
Apply hypothesis testing to a new business problem
Describe the key components of an RDBMS (Relational Database Management System) architecture query and process data using OLTP (Online Transactional Processing) systems
Write portable SQL queries against data
Define schemas
Describe common database programming constructs (stored procedures, triggers, views, etc)
Describe the components of an OLAP (Online Analytical Processing) system
Differentiate tabular vs cube data models
Writing analytical queries
Working with nested/repeated data
Dealing with streaming data in an OLAP context
Describe the components of a NoSQL (Not Only SQL) database
Differentiate columnar/wide-column databases vs document databases
Identify when each is appropriate
Describe common methods for getting data in and out of systems - scripting (including specialty languages such as Pig), bulk loading, streaming inserts
Compare and contrast the ETL (extract, transform, and load) workflow with the LET workflow (load, extract, and transform)
Describe the "four v's" of Big Data and how they are used to differentiate Big Data problems from "small data"
Describe the pros and cons of using cloud vs on-premise solutions for data management
Describe the pros and cons of using "handrolled" Hadoop/Hive/Spark vs proprietary systems like Teradata/Oracle
Identify key decision factors between services on AWS, Azure, GCP, etc.

HomePage:

https://anonymz.com/https://www.pluralsight.com/paths/data-analytics-literacy

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