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3 Top Books Every Analytics Engineer to Read

Many of the analytics jobs nowadays are for the financial domain. The top financial domains are Banking, Payments, and credit cards. 
The Best Books are on:
SASUNIXPython

The skills you need to work in data analytics are SAS, UNIX, Python, and JavaScript.  I have selected three books for beginners of data analysts. 

1. SAS best book 
I found one best book that is little SAS. This post covers almost all examples and critical macros you need for your job.

The best-selling Little SAS Book just got even better. Readers worldwide study this easy-to-follow book to help them learn the basics of SAS programming.

Now Rebecca Ottesen has teamed up with the original authors, Lora Delwiche, and Susan Slaughter, to provide a new way to challenge and improve your SAS skills through thought-provoking questions, exercises, and projects.
2. UNIX best book
The basic commands you will get everywhere. The way of executing Macros or shell scripts is really you need. This is a good book so that you can automate…

Data Vault Top benefits Useful to Your Project

Data Vault 2.0 (DV2) is a system of business intelligence that includes: modeling, methodology, architecture, and implementation best practices.
The benefits of Data Vault
The components, also known as the pillars of DV2 are identified as follows:
data vault
  • DV2 Modeling (changes to the model for performance and scalability)
  • DV2 Methodology (following Scrum and agile best practices)
  • DV2 Architecture (including NoSQL systems and Big Data systems)
  • DV2 Implementation (pattern-based, automation, generation Capability Maturity Model Integration [CMMI] level 5)
There are many special aspects of Data Vault, including the modeling style for the enterprise data warehouse. The methodology takes commonsense lessons from software development best practices such as CMMI, Six Sigma, total quality management (TQM), Lean initiatives, and cycle-time reduction and applies these notions for repeatability, consistency, automation, and error reduction.

Each of these components plays a key role in the overall success of an enterprise data warehousing project. These components are combined with industry-known and time-tested best practices ranging from CMMI to Six Sigma, TQM (total quality management) to Project Management Professional (PMP).

Data Vault 1.0

Data Vault 1.0 is highly focused on just the data modeling section, while DV2 encompasses the effort of business intelligence. The evolution of Data Vault extends beyond the data model and enables teams to execute in parallel while leveraging Scrum agile best practices.

Data Vault 2.0

DV2 architecture is designed to include NoSQL (think: Big Data, unstructured, multistructured, and structured data sets). Seamless integration points in the model and well-defined standards for implementation offer guidance to the project teams.

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