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#HOW TO DOWNLOAD SAS VISUAL ANALYTICS HOW TO#
#HOW TO DOWNLOAD SAS VISUAL ANALYTICS SERIES#

Integrate open source languages and agile technology with the capabilities of SAS analytics.

SAS provides application and enterprise developers, data scientists, and analysts with access to SAS services. Chapter 9, “Process Considerations for Data Quality,” finally shows the process aspect of data quality and also discusses aspects like data relevancy and possible alternatives.Open Source SAS Open source development resources for developers.Chapter 8, “Analytics for Data Quality,” shows additional requirements of interdependences of analytical methods and the data.Chapter 7, “Predictive Modeling,” discusses special requirements of predictive modeling methods.Chapter 6, “Data Correctness,” checks whether the available data are correct with respect to its definition.Chapter 5, “Data Completeness,” deals with the fact that available data fields may contain missing values.Chapter 4, “Data Quantity,” examines whether the amount of data are sufficient for the analysis.Chapter 3, “Data Availability,” starts with the question as to whether data are available in general.The data quality criteria that are defined in this book in chapters 3–9 are the following. Consequences of Poor Data Quality-Simulation Studies.The naming and ordering of these three parts and the respective chapters follow a process as well, but also consider a segmentation of the content of this book into well-defined parts and a good readable sequence of topics and chapters. This book is divided into three main parts.

#HOW TO DOWNLOAD SAS VISUAL ANALYTICS SERIES#

Simulation studies and consequences of poor data quality for predictive modeling and time series forecasting.

#HOW TO DOWNLOAD SAS VISUAL ANALYTICS HOW TO#

Methods in SAS on how to perform advanced profiling of the data quality status and what SAS can offer for the improvement of data quality.

Data correction with focus on analytic profiling of outliers and complex data validation.

Data relevancy with respect to the definition of the data and the requirements of the analysis.

Consideration of the operational, data management, and analysis process point of view.

Data completeness in terms of profiling of the number and the structure of missing values, finding special types of missing values, replacement of missing values, advanced imputation methods.

Types of research are mainly observational studies, where existing data of the company or organization itself is used for the analysis (partly also controlled studies where the required data for the analysis is retrieved in a controlled way).

Typical analyses: predictive analytics and statistics, including time series analysis and time series forecasting.

Ensuring sufficient data quantity in terms of number of observations, number of event cases, and length of data history.

Explaining why analytical methods need historic data and also historic snapshots of the data.

Usability and availability of data for analysis.

The following points are the main focus of this book: And these aspects are important to differentiate whether data are suitable for analysis or not. However, there are many aspects of data that are specific to analytics.

It will not be said here that these topics are not important for analytics on the contrary, they build the foundation of data for analysis. However, most of the initiatives, publications, and papers on data quality focus on classical data quality topics, such as elimination of duplicates, standardization of data, lists of values, value ranges, and plausibility checks. My intention was not to write another book on data quality in general, but to write the first book that deals with data quality from the viewpoint of a data scientist, statistician, data miner, engineer, operations researcher, or other analytically minded problem-solver.ĭata quality is getting a lot of attention in the market. Main focus of this book -> Analytics and Data Science Other books from Gerhard Svolba at SAS Press.ĭata Preparation for Data Science Using SASĪpplying Data Science - Business Case Studies Using SAS “Rosetta Stone” - The most important text sample in history and the role of labeled data in machine learning

Replace MISSING VALUES in TIMESERIES DATA using PROC EXPAND and PROC TIMESERIES.

Using the TIMESERIES procedure to check the continuity of your timeseries data.

The structure of MISSING VALUES in your data - get a clearer picture with the %MV_PROFILING macro.

SGF-Paper: Want an Early Picture of the Data Quality Status of Your Analysis Data? Companion and Download Site for the SAS Press Book "Data Quality for Analytics Using SAS" by Gerhard Svolba. Considerations and best practices for data quality in the context of data science, analytics and machine learning and how to implement this with SAS Software.