SAS Visual Business Analytics Professional Certificate

Start Date: 02/23/2020

Course Type: Specialization Course

Course Link:

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About Course

Using SAS Visual Analytics, you will learn to access and manipulate data, analyze data with a variety of interactive reports and graphics, and design and share dashboards to visualize your data. SAS Visual Analytics is a useful skill in a variety of careers, including business analyst, researcher, statistician, or data scientist.

Course Syllabus

Getting Started with SAS Visual Analytics
Data Analysis and Reporting in SAS Visual Analytics
Using Data for Geographic Mapping and Forecasting in SAS Visual Analytics
Performing Network, Path, and Text Analyses in SAS Visual Analytics

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Course Introduction

Launch Your Career with a SAS® Credential. Master the skills required for the SAS® Visual Business Analyst Certification SAS Visual Business Analytics Professional Certificate The aim of this course is to provide a foundation for the advanced skills needed in a professional data analysis professional. The course is designed for learners who have some prior technical proficiency in Excel or similar software, but who want to expand their knowledge of visualization and analysis in order to become more professional data analysts. Learners who successfully complete the prerequisite courses will be able to move to the advanced skills area of the certificate track. This is the third and last course in the Specialisation, the other two courses being ‘Visualization and Analysis of Censored Data’ and ‘Business Analytics and Analytics with Machine Learning Technologies. The final course is an in-depth analysis of software tools used to prepare the final dataset for analysis. The final dataset consists of a) all relevant files and b) logical sections of any file. This course is designed for learners who have some prior technical proficiency in Excel or similar software, but who want to expand their knowledge of data visualization, tabular data, and advanced algorithms, in order to become more professional data analysts. Technical Knowledge required: Learners who have basic math skills such as multiplication and division, can use free software to explore and print data, or purchase calculators and software to explore and plot data. Software to download: Visualization Software (Amadeus) DataFinder (ZipSoft) Tabular Data Explorer (Visualization) Advanced Slidesh

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SAS (software) SAS (Statistical Analysis System) is a software suite developed by SAS Institute for advanced analytics, multivariate analyses, business intelligence, data management, and predictive analytics.
Visual analytics Visual analytics is a multidisciplinary field that includes the following focus areas:
Visual analytics Visual analytics is an outgrowth of the fields of information visualization and scientific visualization that focuses on analytical reasoning facilitated by interactive visual interfaces.
Visual analytics Visual analytics is "the science of analytical reasoning facilitated by interactive visual interfaces." It can attack certain problems whose size, complexity, and need for closely coupled human and machine analysis may make them otherwise intractable. Visual analytics advances science and technology developments in analytical reasoning, interaction, data transformations and representations for computation and visualization, analytic reporting, and technology transition. As a research agenda, visual analytics brings together several scientific and technical communities from computer science, information visualization, cognitive and perceptual sciences, interactive design, graphic design, and social sciences.
Visual analytics Analytical reasoning techniques are the method by which users obtain deep insights that directly support situation assessment, planning, and decision making. Visual analytics must facilitate high-quality human judgment with a limited investment of the analysts’ time. Visual analytics tools must enable diverse analytical tasks such as:
Business analytics In later years the business analytics have exploded with the introduction to computers. This change has brought analytics to a whole new level and has brought about endless possibilities. As far as analytics has come in history, and what the current field of analytics is today, many people would never think that analytics started in the early 1900s with Mr. Ford himself.
Visual analytics More formally the visual analytics process is a transformation "F: S → I", whereas "F" is a concatenation of functions "f ∈ {D, V, H, U}" defined as follows:
Business analytics Business analytics (BA) refers to the skills, technologies, practices for continuous iterative exploration and investigation of past business performance to gain insight and drive business planning. Business analytics focuses on developing new insights and understanding of business performance based on data and statistical methods. In contrast, business intelligence traditionally focuses on using a consistent set of metrics to both measure past performance and guide business planning, which is also based on data and statistical methods.(citation needed)
Business analytics Thomas Davenport, professor of information technology and management at Babson College argues that businesses can optimize a distinct business capability via analytics and thus better compete. He identifies these characteristics of an organization that are apt to compete on analytics:
SAS Institute SAS Institute (or SAS, pronounced "sass") is an American multinational developer of analytics software based in Cary, North Carolina. SAS develops and markets a suite of analytics software (also called SAS), which helps access, manage, analyze and report on data to aid in decision-making. The company is the world's largest privately held software business and its software is used by most of the Fortune 500.
SAS Business Opportunities SAS Business Opportunities is owned by the SAS Group and comprises five business areas formerly part of Scandinavian Airlines; SAS Flightshop, onboard sales of goods,
Visual analytics Visual analytics integrates new computational and theory-based tools with innovative interactive techniques and visual representations to enable human-information discourse. The design of the tools and techniques is based on cognitive, design, and perceptual principles. This science of analytical reasoning provides the reasoning framework upon which one can build both strategic and tactical visual analytics technologies for threat analysis, prevention, and response. Analytical reasoning is central to the analyst’s task of applying human judgments to reach conclusions from a combination of evidence and assumptions.
Business analytics Business analytics makes extensive use of statistical analysis, including explanatory and predictive modeling, and fact-based management to drive decision making. It is therefore closely related to management science. Analytics may be used as input for human decisions or may drive fully automated decisions. Business intelligence is querying, reporting, online analytical processing (OLAP), and "alerts".
Visual analytics Visual analytics has some overlapping goals and techniques with information visualization and scientific visualization. There is currently no clear consensus on the boundaries between these fields, but broadly speaking the three areas can be distinguished as follows:
Visual analytics Visual analytics seeks to marry techniques from information visualization with techniques from computational transformation and analysis of data. Information visualization forms part of the direct interface between user and machine, amplifying human cognitive capabilities in six basic ways:
Cultural analytics The term "cultural analytics" was coined by Lev Manovich in 2007. Cultural analytics shares many ideas and approaches with visual analytics ("the science of analytical reasoning facilitated by visual interactive interfaces") and visual data analysis:
Master of Science in Business Analytics A Master of Science in Business Analytics (MSBA) is an interdisciplinary STEM graduate professional degree that blends concepts from data science, computer science, statistics, business intelligence, and information theory geared towards commercial applications. Students generally come from a variety of backgrounds including computer science, engineering, mathematics, economics, and business. University programs mandate coding proficiency in at least one language. The languages most commonly used include R, Python, SAS, and SQL. Applicants generally have technical proficiency before starting the program. Analytics concentrations in MBA programs are less technical and focus on developing working knowledge of statistical applications rather than proficiency.
Visual analytics The input for the data sets used in the visual analytics process are heterogeneous data sources (i.e., the internet, newspapers, books, scientific experiments, expert systems). From these rich sources, the data sets "S = S, ..., S" are chosen, whereas each "S , i ∈ (1, ..., m)" consists of attributes A, ..., A. The goal or output of the process is insight "I". Insight is either directly obtained from the set of created visualizations "V" or through confirmation of hypotheses "H" as the results of automated analysis methods. This formalization of the visual analytics process is illustrated in the following figure. Arrows represent the transitions from one set to another one.
Analytics Organizations may apply analytics to business data to describe, predict, and improve business performance. Specifically, areas within analytics include predictive analytics, prescriptive analytics, enterprise decision management, retail analytics, store assortment and stock-keeping unit optimization, marketing optimization and marketing mix modeling, web analytics, sales force sizing and optimization, price and promotion modeling, predictive science, credit risk analysis, and fraud analytics. Since analytics can require extensive computation (see big data), the algorithms and software used for analytics harness the most current methods in computer science, statistics, and mathematics.
Predictive analytics The most popular commercial predictive analytics software packages according to the Rexer Analytics Survey for 2013 are IBM SPSS Modeler, SAS Enterprise Miner, and Dell Statistica.