DEN201 – Enterprise Architecture ICT205- Data Analytics Week 3 Chapter 2 Descriptive Analytics I: Nature of Data, Statistical Modeling, and Visualization TEQSA: PRV14311 CRICOS: 03836J Agenda TEQSA:...

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DEN201 – Enterprise Architecture ICT205- Data Analytics Week 3 Chapter 2 Descriptive Analytics I: Nature of Data, Statistical Modeling, and Visualization TEQSA: PRV14311 CRICOS: 03836J Agenda TEQSA: PRV14311 CRICOS: 03836J Introduction Keywords Review Questions Topics for This Week Topics covered in previous week Topics covered in previous week TEQSA: PRV14311 CRICOS: 03836J 3 Data Unstructured data Data Pre-processing Descriptive Statistics Inferential Statistics Taxonomy of Data Structured data Measures of Dispersion Business report Topics for this week After completing this chapter you will be able to: Understand the importance of data/information visualization Learn different types of visualization techniques Appreciate the value that visual analytics brings to business analytics Know the capabilities and limitations of dashboards TEQSA: PRV14311 CRICOS: 03836J Data Visualization “The use of visual representations to explore, make sense of, and communicate data.” Data visualization vs. Information visualization Information = aggregation, summarization, and contextualization of data Related to information graphics, scientific visualization, and statistical graphics Often includes charts, graphs, illustrations, … Slide 1- 5 Slide 2-5 TEQSA: PRV14311 CRICOS: 03836J 5 Visual Analytics A recently coined term Information visualization + predictive analytics Information visualization Descriptive, backward focused “what happened” “what is happening” Predictive analytics Predictive, future focused “what will happen” “why will it happen” There is a strong move toward visual analytics Slide 1- 6 Slide 2-6 TEQSA: PRV14311 CRICOS: 03836J Keywords TEQSA: PRV14311 CRICOS: 03836J 7 Data Unstructured data Data Pre-processing Descriptive Statistics Inferential Statistics Taxonomy of Data Structured data Measures of Dispersion Business report Review Questions What is Data visualisation? What is Visual Analytics? What is Dashboard? What are the best practices in dashboard design? TEQSA: PRV14311 CRICOS: 03836J Preparation for your Webinar Read chapter 2 Read Practical Lab Week 3 manual Attempt the following questions: What is Data visualisation? What is Visual Analytics? What is Dashboard? What are the best practices in dashboard design? TEQSA: PRV14311 CRICOS: 03836J Dashboard design using KNIME TEQSA: PRV14311 CRICOS: 03836J Any questions? TEQSA: PRV14311 CRICOS: 03836J Reference Sharda, R., Delen, D., & Turban, E. (2016). Business intelligence, analytics, and data science: a managerial perspective. Pearson. https://www.knime.com/downloads/download-knime KNIME Getting Started Guide: https://www.knime.com/getting-started-guide TEQSA: PRV14311 CRICOS: 03836J DEN201 – Enterprise Architecture ICT205- Data Analytics Week 4 Chapter 3 Descriptive Analytics II: Business Intelligence and Data Warehousing TEQSA: PRV14311 CRICOS: 03836J Agenda TEQSA: PRV14311 CRICOS: 03836J Introduction Keywords Review Questions Topics for This Week Topics covered in previous week Topics covered in previous week TEQSA: PRV14311 CRICOS: 03836J 3 Data visualisation Visual Analytics Performance Dashboard Best Practices in Dashboard Design Charts, Graphs Topics for this week After completing this chapter you will be able to: Understand the basic definitions and concepts of data warehousing Understand data warehousing architectures Describe the processes used in developing and managing data warehouses Explain data warehousing operations Explain the role of data warehouses in decision support Explain data integration and the extraction, transformation, and load (ETL) processes TEQSA: PRV14311 CRICOS: 03836J Business Intelligence and Data Warehousing BI used to be everything related to use of data for managerial decision support Now, it is a part of Business Analytics BI = Descriptive Analytics Slide 3-5 TEQSA: PRV14311 CRICOS: 03836J Data Warehouse in 4 minutes TEQSA: PRV14311 CRICOS: 03836J What is a Data Warehouse? A physical repository where relational data are specially organized to provide enterprise-wide, cleansed data in a standardized format A relational database? (so what is the difference?) “The data warehouse is a collection of integrated, subject-oriented databases designed to support DSS functions, where each unit of data is non-volatile and relevant to some moment in time” Slide 3-7 TEQSA: PRV14311 CRICOS: 03836J ETL Process TEQSA: PRV14311 CRICOS: 03836J Data Integration and the Extraction, Transformation, and Load Process ETL = Extract Transform Load Data integration Integration that comprises three major processes: data access, data federation, and change capture. Enterprise application integration (EAI) A technology that provides a vehicle for pushing data from source systems into a data warehouse Enterprise information integration (EII) An evolving tool space that promises real-time data integration from a variety of sources, such as relational or multidimensional databases, Web services, etc. Slide 3-9 TEQSA: PRV14311 CRICOS: 03836J Keywords TEQSA: PRV14311 CRICOS: 03836J 10 Data Warehouse Data Mart DW Framework DW Architecture ETL Process Multidimensionality OLAP Data Lakes Review Questions What is data warehouse? What are the characteristics of data warehouse? What is data mart? What are the ten factors that potentially affect the DW architecture selection decision? Describe your understanding on ETL process. What is OLAP? What are the differences between OLAP and OLTP? What is Data Lake? TEQSA: PRV14311 CRICOS: 03836J Preparation for your Webinar Read chapter 3 Attempt Practical Lab Week 4 manual Attempt the following questions: What is data warehouse? What are the characteristics of data warehouse? What is data mart? What are the ten factors that potentially affect the DW architecture selection decision? Describe your understanding on ETL process. What is OLAP? What are the differences between OLAP and OLTP? What is Data Lake? TEQSA: PRV14311 CRICOS: 03836J Any questions? TEQSA: PRV14311 CRICOS: 03836J Reference Sharda, R., Delen, D., & Turban, E. (2016). Business intelligence, analytics, and data science: a managerial perspective. Pearson. https://www.knime.com/downloads/download-knime KNIME Getting Started Guide: https://www.knime.com/getting-started-guide TEQSA: PRV14311 CRICOS: 03836J What happened? What is happening? What will happen? Why will it happen? What should I do? Why should I do it? üBusiness reporting üDashboards üScorecards üData warehousing üData mining üText mining üWeb/media mining üForecasting üOptimization üSimulation üDecision modeling üExpert systems Well defined business problems and opportunities Accurate projections of future events and outcomes Best possible business decisions and actions Q u e s t i o n s E n a b l e r s O u t c o m e s DescriptivePredictivePrescriptive Business Analytics Business Intelligence Advanced Analytics DEN201 – Enterprise Architecture ICT205- Data Analytics Week 2 Chapter 2 Descriptive Analytics I: Nature of Data, Statistical Modeling, and Visualization TEQSA: PRV14311 CRICOS: 03836J Agenda TEQSA: PRV14311 CRICOS: 03836J Introduction Keywords Review Questions Topics for This Week Topics covered in previous week Topics covered in previous week TEQSA: PRV14311 CRICOS: 03836J 3 Data Analytics OLTP OLAP Descriptive Analytics Predictive Analytics Business Intelligence Real-time BI Prescriptive Analytics Analytics Ecosystem Topics for this week After completing this chapter you will be able to: Understand the nature of data as it relates to business intelligence (BI) and analytics Learn the methods used to make real-world data analytics ready Describe statistical modeling and its relationship to business analytics Learn about descriptive and inferential statistics Define business reporting, and understand its historical evolution TEQSA: PRV14311 CRICOS: 03836J The Nature of Data Data: a collection of facts usually obtained as the result of experiences, observations, or experiments Data may consist of numbers, words, images, … Data is the lowest level of abstraction (from which information and knowledge are derived) Data is the source for information and knowledge Data quality and data integrity  critical to analytics Slide 2-5 TEQSA: PRV14311 CRICOS: 03836J DIKW Hierarchy TEQSA: PRV14311 CRICOS: 03836J The Art and Science of Data Preprocessing Data reduction Variables Dimensional reduction Variable selection Cases/samples Sampling Balancing / stratification Slide 2-7 TEQSA: PRV14311 CRICOS: 03836J Statistical Modeling for Business Analytics Statistics A collection of mathematical techniques to characterize and interpret data Descriptive Statistics Describing the data (as it is) Inferential statistics Drawing inferences about the population based on sample data Descriptive statistics for descriptive analytics Slide 2-8 TEQSA: PRV14311 CRICOS: 03836J Descriptive Statistics Measures of Centrality Tendency Arithmetic mean Median The number in the middle Mode The most frequent observation Slide 2-9 TEQSA: PRV14311 CRICOS: 03836J Keywords TEQSA: PRV14311 CRICOS: 03836J 10 Data Unstructured data Data Pre-processing Descriptive Statistics Inferential Statistics Taxonomy of Data Structured data Measures of Dispersion Business report Review Questions What is business intelligence? What are the critical BI System Considerations? What are the three types of Analytics? What is Big Data? TEQSA: PRV14311 CRICOS: 03836J Preparation for your Webinar Read chapter 2 Read Practical Lab Week 2 manual Attempt the following questions: Discuss the taxonomy of data. What is data pre-processing? What are the tasks and methods? What is descriptive statistics? What is inferential statistics? What is business report? TEQSA: PRV14311 CRICOS: 03836J Any questions? TEQSA: PRV14311 CRICOS: 03836J Reference Sharda, R., Delen, D., & Turban, E. (2016). Business intelligence, analytics, and data science: a managerial perspective. Pearson. https://www.knime.com/downloads/download-knime KNIME Getting Started Guide: https://www.knime.com/getting-started-guide TEQSA: PRV14311 CRICOS: 03836J this is the exam tommorow at 10 o' clock to 12 o' clock tommorow according to the sydney time ( IST – 5am to 7am) around 5 question ( subject name ICT205 - DATA ANALYTICS) tommorrow i can send u the question
Answered 1 days AfterOct 10, 2021

Answer To: DEN201 – Enterprise Architecture ICT205- Data Analytics Week 3 Chapter 2 Descriptive Analytics I:...

Swapnil answered on Oct 11 2021
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1A
    The following are the different types of data available in data analytics.
· Descriptive Analytics: The descriptive analysis describes the raw information from the different data sources to give the outcomes whi
ch is wrong or right without clarifying it.
· Diagnostic Analytics: The diagnostic analytics is nothing but it can be classifying for the data to acknowledge to the specific issue.
· Predictive Analytics: The predictive analytics sis basically tells about the what is going to happen. It can be used for the descriptive and diagnostic analytics to identifying the future values which will significantly estimate the values.
· Prescriptive Analytics: The prescriptive analytics used to prescribe the future issues which will eliminate the advanced tools and technologies for the machine learning. It also works on the managing the actual algorithms for the outer data of information.
Data mining methods between classification and regression depending in the data type structures:
Classification:
· The model or function where it is used for the mapping the objects that is done into the predefined classes.
· It can give the discrete values for prediction.
· Basically algorithms we can use here are decision tree, logistic regression etc.
· Accuracy will be calculated for the calculation method.
· It gives the predicted data as an unordered data
Regression:
· The model can be used to map the objects that is done into the values.
· It can give the prediction of continuous values.
· The algorithms are used basically are linear regression, regression tree algorithm.
· The data will be measured in RMSE (Root Mean Square Error).
· It gives the predicted data as an ordered data.
    1B
    Evaluating association rule mining: The association rule can be help us to the giving the probability relationship between the data items within the large data sets from the various databases. The association rule mining will be gives the number of transactional data from the data sets.
Example: The medicine example gives the association rule mining where the doctors can be use the association rules to help for the diagnosed patients. So...
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