Pluralsight - SQL Data Wrangling in Oracle: Table Data [repost]

Pluralsight - SQL Data Wrangling in Oracle: Table Data [repost]

Pluralsight - SQL Data Wrangling in Oracle: Table Data
MP4 | AVC 211kbps | English | 1024x768 | 15fps | 5h 18mins | AAC stereo 102kbps | 624 MB
Genre: Video Training

This course describes Oracle functionality useful to the SQL-savvy data analyst, and features topics such as ordering your data with NULLs appearing first or last, using FETCH to subset your data, using SAMPLE to randomly sample from a table, using CROSS APPLY and OUTER APPLY, how to use the MODEL feature to access the rows and columns of a database table like a spreadsheet, generating random numbers, computing occurrences, using nested tables, and much, more.

Pluralsight - SQL Data Wrangling in Oracle: Table Data

Pluralsight - SQL Data Wrangling in Oracle: Table Data

Pluralsight - SQL Data Wrangling in Oracle: Table Data
27 Nov 2014 | .MP4, x264, 1024x768 | English, AAC, 2 Ch | 5 hrs 18 mins | 624 MB
Instructor: Scott Hecht | Level: Intermediate

Azure SQL Data Warehouse: First Look (2016)

Azure SQL Data Warehouse: First Look (2016)

Azure SQL Data Warehouse: First Look
August 2016 | MP4 | Video: AVC 1280x720 | Audio: AAC 44KHz 2ch | Duration: 1H 45M | 329 MB
Genre: eLearning | Language: English

Azure SQL Data Warehouse is Microsoft Azure's Database as a Service offering. This course teaches the differences between this service and SQL Server, designing your database for the service, loading and migrating data, scaling and basic monitoring.

NoSQL and SQL Data Modeling: Bringing Together Data, Semantics, and Software

NoSQL and SQL Data Modeling: Bringing Together Data, Semantics, and Software

NoSQL and SQL Data Modeling: Bringing Together Data, Semantics, and Software by Ted Hills
English | 11 Mar. 2016 | ISBN: 1634621093 | 258 Pages | AZW3/MOBI/EPUB/PDF (conv) | 26.05 MB

How do we design for data when traditional design techniques cannot extend to new database technologies?

SQL For Anyone: A Complete Beginner’s Guide to Structured Query Language

SQL For Anyone: A Complete Beginner’s Guide to Structured Query Language

SQL For Anyone: A Complete Beginner's Guide to Structured Query Language by Mark Dinkel
English | Feb 21, 2016 | ASIN: B01C26KNIS | 67 Pages | AZW4/PDF (True) | 2.09 MB

An explanation of SQL data and statements to use the SQL data. It covers SELECT, UPDATE, DELETE, JOINS, UNION, NULL and aggregation using GROUP BY.

Coursera - Web Intelligence and Big Data [repost]

Coursera - Web Intelligence and Big Data [repost]

Coursera - Web Intelligence and Big Data
MP4 | AVC 74kbps | English | 960x540 | 30fps | 13h 33mins | AAC stereo 128kbps | 1.19 GB
Genre: Video Training

The past decade has witnessed the successful of application of many AI techniques used at `web-scale’, on what are popularly referred to as big data platforms based on the map-reduce parallel computing paradigm and associated technologies such as distributed file systems, no-SQL databases and stream computing engines. Online advertising, machine translation, natural language understanding, sentiment mining, personalized medicine, and national security are some examples of such AI-based web-intelligence applications that are already in the public eye. Others, though less apparent, impact the operations of large enterprises from sales and marketing to manufacturing and supply chains. In this course we explore some such applications, the AI/statistical techniques that make them possible, along with parallel implementations using map-reduce and related platforms.

Coursera - Introduction to Data Science

Coursera - Introduction to Data Science

Coursera - Introduction to Data Science
MP4 | AVC 88kbps | English | 960x540 | 30fps | 16h 03mins | AAC stereo 113kbps | 3.88 GB
Genre: Video Training

Commerce and research is being transformed by data-driven discovery and prediction. Skills required for data analytics at massive levels – scalable data management on and off the cloud, parallel algorithms, statistical modeling, and proficiency with a complex ecosystem of tools and platforms – span a variety of disciplines and are not easy to obtain through conventional curricula. Tour the basic techniques of data science, including both SQL and NoSQL solutions for massive data management (e.g., MapReduce and contemporaries), algorithms for data mining (e.g., clustering and association rule mining), and basic statistical modeling (e.g., linear and non-linear regression).

Coursera - Web Intelligence and Big Data (2013)

Coursera - Web Intelligence and Big Data (2013)

Coursera - Web Intelligence and Big Data (2013)
English | MP4 | 960 x 540 | AVC ~36.4 kbps | 30 fps
AAC | 123 Kbps | 44.1 KHz | 2 channels | Subs: English (srt) | 13:33:03 | 1.37 GB
Genre: eLearning Video / Computer Science: Artificial Intelligence

The past decade has witnessed the successful of application of many AI techniques used at `web-scale’, on what are popularly referred to as big data platforms based on the map-reduce parallel computing paradigm and associated technologies such as distributed file systems, no-SQL databases and stream computing engines. Online advertising, machine translation, natural language understanding, sentiment mining, personalized medicine, and national security are some examples of such AI-based web-intelligence applications that are already in the public eye.

Coursera - Process Mining (2015)

Coursera - Process Mining (2015)

Coursera - Process Mining (2015)
MP4 | AVC 161kbps | English | 960x540 | 29.97fps | 14 hours | AAC stereo 128kbps | 1.68 GB
Genre: Video Training

Process mining is the missing link between model-based process analysis and data-oriented analysis techniques. Through concrete data sets and easy to use software the course provides data science knowledge that can be applied directly to analyze and improve processes in a variety of domains. Data science is the profession of the future, because organizations that are unable to use (big) data in a smart way will not survive. It is not sufficient to focus on data storage and data analysis. The data scientist also needs to relate data to process analysis. Process mining bridges the gap between traditional model-based process analysis (e.g., simulation and other business process management techniques) and data-centric analysis techniques such as machine learning and data mining.

Coursera - The Caltech-JPL Summer School on Big Data Analytics

Coursera - The Caltech-JPL Summer School on Big Data Analytics

Coursera - The Caltech-JPL Summer School on Big Data Analytics
WEBRip | English | MP4 + PDF Guides | 960 x 540 | AVC ~98.3 kbps | 29.970 fps
AAC | 128 Kbps | 44.1 KHz | 2 channels | Subs: English (.srt) | 18:43:52 | 2.02 GB
Genre: eLearning Video / Statistics & Data Analysis, Databases

This is an intensive, advanced summer school (in the sense used by scientists) in some of the methods of computational, data-intensive science. It covers a variety of topics from applied computer science and engineering, and statistics, and it requires a strong background in computing, statistics, and data-intensive research.

Beginning SQL Server R Services: Analytics for Data Scientists - Bradley Beard (Repost)

Beginning SQL Server R Services: Analytics for Data Scientists - Bradley Beard (Repost)

Beginning SQL Server R Services: Analytics for Data Scientists - Bradley Beard
English | 2016 | 261 Pages | ISBN: 1484222970 | PDF | 21.54 MB

Learn how to develop powerful data analytics applications quickly for SQL Server database administrators and developers. Organizations will be able to sift data and derive the business intelligence needed to drive business decisions and profit. The addition of R to SQL Server 2016 places a powerful analytical processor into an environment most developers are already comfortable with – Visual Studio. This book walks even the newest of users through the creation process of a powerful R-language tool set for use in analyzing and reporting on your data…

Coursera - Process Mining: Data Science in Action [repost]

Coursera - Process Mining: Data Science in Action [repost]

Coursera - Process Mining: Data Science in Action
WEBRip | English | MP4 | 960 x 540 | AVC ~151 kbps | 29.970 fps
AAC | 128 Kbps | 44.1 KHz | 2 channels | 13:23:25 | 1.68 GB
Genre: eLearning Video / Computer Science, Engineering and Technology

Process mining is the missing link between model-based process analysis and data-oriented analysis techniques. Through concrete data sets and easy to use software the course provides data science knowledge that can be applied directly to analyze and improve processes in a variety of domains. Data science is the profession of the future, because organizations that are unable to use (big) data in a smart way will not survive. It is not sufficient to focus on data storage and data analysis. The data scientist also needs to relate data to process analysis. Process mining bridges the gap between traditional model-based process analysis (e.g., simulation and other business process management techniques) and data-centric analysis techniques such as machine learning and data mining.

SQL on Big Data: Technology, Architecture, and Innovation

SQL on Big Data: Technology, Architecture, and Innovation

SQL on Big Data: Technology, Architecture, and Innovation by SUMIT PAL
English | 6 Dec. 2016 | ISBN: 1484222466 | 180 Pages | PDF | 6.25 MB

Learn various commercial and open source products that perform SQL on Big Data platforms. You will understand the architectures of the various SQL engines being used and how the tools work internally in terms of execution, data movement, latency, scalability, performance, and system requirements.

Beginning SQL Server R Services: Analytics for Data Scientists

Beginning SQL Server R Services: Analytics for Data Scientists

Beginning SQL Server R Services: Analytics for Data Scientists by Bradley Beard
English | 18 Nov. 2016 | ISBN: 1484222970 | 284 Pages | EPUB | 7.8 MB

Learn how to develop powerful data analytics applications quickly for SQL Server database administrators and developers. Organizations will be able to sift data and derive the business intelligence needed to drive business decisions and profit. The addition of R to SQL Server 2016 places a powerful analytical processor into an environment most developers are already comfortable with – Visual Studio.

Coursera - Data Analysis (2013)

Coursera - Data Analysis (2013)

Coursera - Data Analysis (2013)
MP4 | AVC 41kbps | English | 960x540 | 30fps | 12h 36mins | AAC stereo 111kbps | 949 MB
Genre: Video Training

This course is an applied statistics course focusing on data analysis. The course will begin with an overview of how to organize, perform, and write-up data analyses. Then we will cover some of the most popular and widely used statistical methods like linear regression, principal components analysis, cross-validation, and p-values. Instead of focusing on mathematical details, the lectures will be designed to help you apply these techniques to real data using the R statistical programming language, interpret the results, and diagnose potential problems in your analysis. You will also have the opportunity to critique and assist your fellow classmates with their data analyses.

Coursera - Data Analysis [repost]

Coursera - Data Analysis [repost]

Coursera - Data Analysis
WEBRip | English | MP4 + PDF Slides | 960 x 540 | AVC ~33.8 kbps | 30 fps
AAC | 128 Kbps | 44.1 KHz | 2 channels | Subs: English (.srt) | 12h 36mn | 949 MB
Genre: eLearning Video / Programming

This course is an applied statistics course focusing on data analysis. The course will begin with an overview of how to organize, perform, and write-up data analyses. Then we will cover some of the most popular and widely used statistical methods like linear regression, principal components analysis, cross-validation, and p-values. Instead of focusing on mathematical details, the lectures will be designed to help you apply these techniques to real data using the R statistical programming language, interpret the results, and diagnose potential problems in your analysis. You will also have the opportunity to critique and assist your fellow classmates with their data analyses.

Coursera - Genomic Data Science Specialization (2016)

Coursera - Genomic Data Science Specialization (2016)

Coursera - Genomic Data Science Specialization (2016)
WEBRip | English | MP4 + PDF Guides + work files | 960 x 540 | AVC ~63.6 kbps | 29.970 fps
AAC | 128 Kbps | 44.1 KHz | 2 channels | Subs: English (.srt) | ~32 hours | 3.79 GB
Genre: eLearning Video / Medicine, Biology and Genetics, Science

This specialization covers the concepts and tools to understand, analyze, and interpret data from next generation sequencing experiments. It teaches the most common tools used in genomic data science including how to use the command line, Python, R, Bioconductor, and Galaxy. The sequence is a stand alone introduction to genomic data science or a perfect compliment to a primary degree or postdoc in biology, molecular biology, or genetics.

SQL Server 2012 Data Integration Recipes: Solutions for Integration Services and Other ETL Tools (repost)

SQL Server 2012 Data Integration Recipes: Solutions for Integration Services and Other ETL Tools (repost)

SQL Server 2012 Data Integration Recipes: Solutions for Integration Services and Other ETL Tools (Expert's Voice in SQL Server) by Adam Aspin
English | Nov 14, 2012 | ISBN: 1430247916 | 1056 Pages | PDF | 44 MB

SQL Server 2012 Data Integration Recipes provides focused and practical solutions to real world problems of data integration. Need to import data into SQL Server from an outside source? Need to export data and send it to another system? SQL Server 2012 Data Integration Recipes has your back. You'll find solutions for importing from Microsoft Office data stores such as Excel and Access, from text files such as CSV files, from XML, from other database brands such as Oracle and MySQL, and even from other SQL Server databases. You'll learn techniques for managing metadata, transforming data to meet the needs of the target system, handling exceptions and errors, and much more.

SQL Server 2012 Data Integration Recipes: Solutions for Integration Services (Repost)

SQL Server 2012 Data Integration Recipes: Solutions for Integration Services (Repost)

SQL Server 2012 Data Integration Recipes: Solutions for Integration Services and Other ETL Tools - Adam Aspin
2012 | ISBN: 1430247916 | PDF/EPUB | 1056 pages | 64 Mb

SQL Server 2012 Data Integration Recipes provides focused and practical solutions to real world problems of data integration. Need to import data into SQL Server from an outside source? Need to export data and send it to another system? SQL Server 2012 Data Integration Recipes has your back. You'll find solutions for importing from Microsoft Office data stores such as Excel and Access, from text files such as CSV files, from XML, from other database brands such as Oracle and MySQL, and even from other SQL Server databases. You'll learn techniques for managing metadata, transforming data to meet the needs of the target system, handling exceptions and errors, and much more.

Data Mining with Microsoft SQL Server 2000 Technical Reference

Data Mining with Microsoft SQL Server 2000 Technical Reference

Claude Seidman, «Data Mining with Microsoft SQL Server 2000 Technical Reference»
Microsoft Press | ISBN: 0735612714 | 2001 | CHM | 400 pages | 1.43 MB

With its state-of-the-art capabilities for rapidly processing and retrieving huge quantities of data, Microsoft SQL Server 2000 is quickly growing in popularity among large corporations. But learning how to take advantage of the powerful, built-in data-mining services in SQL Server to turn all that data into meaningful information takes time and effort. Data Mining with SQL Server 2000 Technical Reference is the ideal, in-depth reference guide for any database developer, administrator, or IT professional who needs comprehensive information about these powerful new data-mining services. In particular, it fully examines the data-warehousing architecture in SQL Server 2000 to show how to take full advantage of the data-mining services in this RDBMS. This is the only Microsoft-approved technical guide to the data mining services in SQL Server 2000.