Accounting Data Analytics Specialization

Start Date: 11/29/2020

Course Type: Specialization Course

Course Link:

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About Course

Machine LearningPython ProgrammingData Visualization (DataViz)Data PreparationExploratory Data AnalysisData AnalysisPredictive AnalyticsData ArchitecturecodingLinear RegressionSQLText Analysis

Course Syllabus

Introduction to Accounting Data Analytics and Visualization
Accounting Data Analytics with Python
Machine Learning for Accounting with Python
Data Analytics in Accounting Capstone

Deep Learning Specialization on Coursera

Course Introduction

Develop Data Analytics Skills for Accountants. This specialization develops students’ skills of data preparation, data visualization, data analysis, data interpretation, and machine learning algorithms and their applications to real-world problems.

Course Tag

Related Wiki Topic

Article Example
Meter data analytics Meter Data Analytics refers to the analysis of data emitted by electric smart meters that record consumption of electric energy.
Data Analytics Acceleration Library Intel Data Analytics Acceleration Library (Intel DAAL) is a library of optimized algorithmic building blocks for data analysis stages most commonly associated with solving Big Data problems.
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.
Accounting analyst The accounting analyst will most likely hold an accounting qualification. The analyst may have an MBA degree with an Accounting specialization.
Data Analytics Acceleration Library Intel launched the Data Analytics Acceleration Library on August 25, 2015 and called it Intel Data Analytics Acceleration Library 2016 (Intel DAAL 2016). DAAL is bundled with Intel Parallel Studio XE as a commercial product. A standalone version is available commercially or freely, the only difference being support and maintenance related.
Accounting An accounting information system is a part of an organisation's information system that focuses on processing accounting data.
Analytics Analytics is the discovery, interpretation, and communication of meaningful patterns in data. Especially valuable in areas rich with recorded information, analytics relies on the simultaneous application of statistics, computer programming and operations research to quantify performance.
Meter data analytics According to a 2012 web posting, data that is required for complete meter data analytics may not reside in the same database. Instead, it might reside in disparate databases among various departments of utility companies.
Analytics Analytics is multidisciplinary. There is extensive use of mathematics and statistics, the use of descriptive techniques and predictive models to gain valuable knowledge from datadata analysis. The insights from data are used to recommend action or to guide decision making rooted in business context. Thus, analytics is not so much concerned with individual analyses or analysis steps, but with the entire methodology. There is a pronounced tendency to use the term "analytics" in business settings e.g. text analytics vs. the more generic text mining to emphasize this broader perspective. . There is an increasing use of the term "advanced analytics", typically used to describe the technical aspects of analytics, especially in the emerging fields such as the use of machine learning techniques like neural networks to do predictive modeling.
Analytics Analytics is increasingly used in education, particularly at the district and government office levels. However, the complexity of student performance measures presents challenges when educators try to understand and use analytics to discern patterns in student performance, predict graduation likelihood, improve chances of student success, etc. For example, in a study involving districts known for strong data use, 48% of teachers had difficulty posing questions prompted by data, 36% did not comprehend given data, and 52% incorrectly interpreted data. To combat this, some analytics tools for educators adhere to an over-the-counter data format (embedding labels, supplemental documentation, and a help system, and making key package/display and content decisions) to improve educators’ understanding and use of the analytics being displayed.
Continuous analytics Continuous analytics then is the extension of the continuous delivery software development model to the big data analytics development team. The goal of the continuous analytics practitioner then is to find ways to incorporate writing analytics code and installing big data software into the agile development model of automatically running unit and functional tests and building the environment system with automated tools.
Currency analytics Currency analytics allow companies to mitigate cash flow risk by uncovering accounting exposures to match the economic exposures so the company can hedge the accounting exposure as a proxy. Currency analytics enable what/if scenario analysis so companies can model how volatility in particular currencies could impact their revenue and expenses in the future.
Learning analytics The ethics of data collection, analytics, reporting and accountability has been raised as a potential concern for Learning Analytics (e.g.,), with concerns raised regarding:
Continuous analytics Analytics is the application of mathematics and statistics to big data. Data scientists write analytics programs to look for solutions to business problems, like forecasting demand or setting an optimal price.
Google Analytics Google Analytics is a freemium web analytics service offered by Google that tracks and reports website traffic. Google launched the service in November 2005 after acquiring Urchin. Google Analytics is now the most widely used web analytics service on the Internet. Google Analytics is offered also in two additional versions: the subscription based Google Analytics 360, previously Google Analytics Premium, targeted at enterprise users and Google Analytics for Mobile Apps, an SDK that allows gathering usage data from iOS and Android Apps.
Google Analytics 360 Suite Google Data Studio 360 is a web analytics dashboard report service that summarizes analytics data into reports. Google has released its free version for individuals and small business.
IT operations analytics IT operations analytics (ITOA) (also known as advanced operational analytics, or IT data analytics) technologies are primarily used to discover complex patterns in high volumes of often "noisy" IT system availability and performance data. Forrester Research defined IT analytics as "The use of mathematical algorithms and other innovations to extract meaningful information from the sea of raw data collected by management and monitoring technologies."
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:
Software analytics Software analytics represents a base component of software diagnosis that generally aims at generating findings, conclusions, and evaluations about software systems and their implementation, composition, behavior, and evolution. Software analytics frequently uses and combines approaches and techniques from statistics, prediction analysis, data mining, and scientific visualization. For example, software analytics can map data by means of software maps that allow for interactive exploration.
Big data There are advantages as well as disadvantages to shared storage in big data analytics, but big data analytics practitioners did not favour it.