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Reading Notes Weeks 4-6

by: Alexandra Tilton

Reading Notes Weeks 4-6 INFO 1010

Marketplace > University of Denver > Business > INFO 1010 > Reading Notes Weeks 4 6
Alexandra Tilton
GPA 4.0
Analytics 1: Data Management and Analysis
Professor Gisella Bassani

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These are the reading notes that were assigned in weeks 4 through 6
Analytics 1: Data Management and Analysis
Professor Gisella Bassani
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This 24 page Bundle was uploaded by Alexandra Tilton on Friday October 23, 2015. The Bundle belongs to INFO 1010 at University of Denver taught by Professor Gisella Bassani in Fall 2015. Since its upload, it has received 68 views. For similar materials see Analytics 1: Data Management and Analysis in Business at University of Denver.


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Date Created: 10/23/15
INFO 1010 Data Management and Analysis Reading Notes Weeks 46 Sections 24 8 25 from INFO Textbook 24 Summarising Data for Two Variables Using Graphical Displays 0 A graphical display is usually more useful for recognising patters and trends in the data 0 Displaying data in creative ways can allow us to make commonsense inferences Scatter Diagram and Trendline Scatter diagram 0 Graphical display of the relationship between two quantitative variables 0 Trendline 0 A line that provides an approximation of the relationship 0 To add trend line to scatter plot rightclick on one of the dots and select add trendline SidebySide and Stacked Bar Charts 0 Scatter diagram 0 A graphical display for depicting multiple bar charts on the same display Alexandra Tilton Page 1 of 24 0 Stacked bar chart 0 A bar chart in which each bar is broken into segments of a different colour showing the relative frequency of each class like a pie chart 0 Convert the frequency data into column percentages by dividing each element in a column by the total for that column 25 Data Visualisation Best Practices in Creating Effective Graphical Displays 0 Data visualisation describes the graphical displays to summarise and present information about a data set Creating Effective Graphical Displays 0 Keys to creating a good graphical display 0 Give the display a clear concise title Keep the display simple and 2D Clearly label axis and units Colours should be distinct for different categories Use a legend to de ne how colours or line types are used Choosing the Type of Graphical Display 0 Displays used to show distribution of data 0 Bar Chart For frequency relative frequency distribution for categorical data 0 Pie Chart For relative percent frequency for categorical data 0 Dot Plot For quantitative data over the entire range of the data 0 Histogram Used to show frequency distribution for quantitative data over a set of class intervals 0 StemandLeaf Display Used to show both rank and order and shape of the distribution of quantitative data Alexandra Tilton Page 2 of 24 0 Displays used to make comparisons 0 Sideby Side Bar Chart For comparing two variables 0 Stacked Bar Chart Compare relative frequency or percent frequency of two categorial variables 0 Displays used to show relationships 0 Scatter diagram Show the relationship between two quantitative variables 0 Trendline Approximate the relationship of data in a scatter diagram Data Dashboards 0 Data Dashboard 0 A set of visual displays that organises and presents information that is used to monitor the performance of a company or organisation 0 Easy to read understand interpret nouuctu h quot I h m an tun huh 0 0m Q nhh I amen an um m O ll m mun O m It H39I O I vquot 39339 I nun39 no ut I b h I I I 0 0 nur 0 van v ul v39vu 0 vs u nn u can t I n th 0 39 quot39 OLE l l l I 39 Alexandra Tilton Page 3 of 24 Chapter 14 from Information Systems Background 0 Security of information systems has always been important to C10 0 Media attention has brought IS security issues to public view 0 Consolidation of IS security results from several factors 0 Increased dependance on information and communication technologies 0 Exploitation of information and communication technologies capabilities in the business arena 0 Increased mobility with need to access information by diverse means 0 New emphasis on establishing responsibility integrity of people trustworthiness and ethicality 0 Information handling can be undertaken at three levels technical formal and informal 0 Ex antivirus software security policies good management practices 0 Many managers tend to think disaster recovery planning is irrelevant and focus on revenueproducing ventures Principles 0 1 Managing the Informal Aspects of IS Security 0 Security of IS is dependant upon people that form the system 0 Security culture shared by organisational members is critical 0 Education training and awareness although important are not suf cient for maintaining information security A focus on developing a security culture goes a long way in developing and sustaining a secure environment Alexandra Tilton Page 4 of 24 0 Managers tend to be complacent in taking any proactive steps 0 Monitoring employee behaviour is essential to maintain integrity of IS 0 2 Responsibility integrity trust and ethicality are the cornerstones for maintaining a secure environment 0 Fundamentals of confidentiality integrity and availability are good for hierarchical structures but falls short for networked organisations 0 Confidentiality is not as applicable since data is becoming more and more accessible 0 Integrity can be interpreted in different waysneeds specification within the organisation 0 New set of RITE principles 0 Responsibility Members need to know their respective roles and responsibilities within an organisation Does not need to be static should allow for exibility within organisational structure 0 Integrity Personnel integrity should be a requirement of membership in an organisation References should be checked Majority of security breaches come from existing employees 0 Trust Organisations are shifting from external control and supervision to selfcontrol and responsibility Physically dispersed organisations make supervision less possible 0 Ethicality Ensure members will act according to a set of working norms embedded in ethical standards Without ethical foundations organisations will confront serious IS security issues Alexandra Tilton Page 5 of 24 0 4 Rules for managing information security have little relevance unless they are contextualised 0 Need to consider two formal controls security policies and structures of responsibility and authority 0 The content and form of a security policy is casespecific 0 Context of structure and members needs to be taken into account in order to be successful 0 5 In managing the security of technical systems a rationally planned grandiose strategy will fall short of achieving the purpose 0 An exclusive emphasis on topdown stepwise strategy procedures may be counterproductive 0 New technology and constant innovation are stepping away from hierarchical structures of management and strategy 0 6 Formal models for maintaining the confidentiality integrity and availability CIA of information cannot be applied to commercial organisations on a grand scale Micromanagement for achieving CIA is the way forward 0 All three aspects of security must be clearly understood 0 To new ways to achieve confidentiality integrity and availability are to create newer models for particular aspects of the business for which information security needs to be designed Alexandra Tilton Page 6 of 24 Chapter 12 from Business Fundamentals What is Ethics 0 Branch of philosophy concerned with the meaning of all aspects of human behaviour 0 Theoretical Normative ethics is about discovering right from wrong 0 Business ethics 2 applied ethics 0 Business ethics 2 practice of discipline within context of the enterprise of creating wealth 0 Three parts personal professional corporate 39 Personal 0 Includes impartiality rationality consistency reversibility 0 Utilitarian How will my actions affect other 0 Deontological The study of duty what we ought to do 0 Virtue Excellence of a thing what it is meant to do 39 Professionalism 0 Prelearned knowledge is necessary to perform 0 Profession of duties to the public 39 Corporate Social Responsibility 0 Ethical analysis involving corporation must begin and end in law and public policy 0 Corporations survive solely by their ability to return value to their shareholders 0 Ethical responsibility of corporations is not based on intent but responsibility 0 Social Contract between society and multinational corporations is being negotiated 0 Fundamental ideas are being changed by innovation Alexandra Tilton Page 7 of 24 Privacy Security Ethics Readings Ethics 0 Ethical issues confronting IT managers Does information availability justify its use How much effort and expense should managers insure in considering questions of data access and privacy What can employers expect from employees with regard to nondisclosure when going to work for another rm What part of an information asset belongs to an organisation and what is simply part of an employee s general knowledge Do employees know how much of their behaviour is monitored Does data gathered violate employee privacy rights Is accuracy an explicit part of someones responsibility Have the implications of potential error been anticipated Have systems been reviewed for the most likely sources of security breach What is the liability exposure of managers and the organisation 0 CISPA Cyber Intelligence Sharing and Protection Act 0 Vague language of CISPA would allow federal government to override every online privacy law 0 Some businesses use data mining to target potential new customers some say ethics breach 0 Companies have started using data mining to predict your next move 0 Some foreign customers may decide US supervision outweighs bene t of using US cloud computing Alexandra Tilton Page 8 of 24 Security Social networking is a potential personal security threat due to new technology Smartphones are getting rid of the idea of a right to privacy Federal government can monitor many types of communication Three main groups of attackers online criminals hackers motivated by opinion laughs etc and nation states making attacks Rising number of online scams affect millions of users Privacy Companies can track your purchases to make predictions about what you will continue to buy Science of habit forming is becoming a major field of research Companies are using all sorts of information that was once considered private to make predictions about your habits and behaviour Government issues with NSA on the privacy rights of calllog data Sections 31 8 32 from INFO Textbook 31 Measures of Location Alexandra Tilton Mean average value Provides a measure of central location for the data If the data is for a sample you use X and population uses it 0 To nd the mean add up all data points and divide by the number of data points Page 9 of 24 0 Median the value in the middle when the data is arranged in ascending order smallest to largest 0 If number of values is odd the median is the middle value 0 If number of values is even the median is the average of the two middle values 0 Mode the value that occurs with the greatest frequency 0 Weighted Mean each observation is given a weight that re ects its important 0 Example GPA 0 Geometric Mean a measure of location calculated by nding the nth root of the product of n values 0 Used to nd the mean rate of change over several successive periods 0 Percentile provides information about how the data are spread over the interval from the smallest value to the largest value 0 the pth percentile divides the data into two parts what is greater or less than that percentile 0 Example SAT scores 0 Quartiles Data set divided into four parts 0 Q1 first quartile or 25th percentile 0 Q2 second quartile or 50th percentile also median 0 Q3 third quartile or 75th percentile 32 Measures of Variability 0 Range Largest value smallest value 0 Only based on two values so highly in uenced by extreme values outliers 0 Range Q3 Q1 0 Overcomes dependancy on extreme values 0 The range of the middle 50 of the data Alexandra Tilton Page 10 of 24 Variance Measure of variability that uses all the data 0 based on the difference between the value of each observation and the mean Standard Deviation the positive square root of the variance 0 5 denotes sample and 0 denotes population Coefficient of Variation how large the standard deviation is relative to the mean of the data 0 standard deviation divided by the mean then multiplied by one hundred Chapter 7 from Information Systems Hardware Progress Information technology is improving at an exponential rate Storage technology is also improving 1000 old punch cards held about 48 thousand characters a thumb drive holds 8 billion characters Every type of data is stored in bits short for binary digits Progress in Communication Technology Telegraph was first electronic communication technology Improvement in LAN technology with ethernet being the most common Software Progress First computers worked primarily on numeric data First came numeric then alphanumeric then text images speech music video Computers used to be much more expensive due to batch processing and the human labor it required Alexandra Tilton Page 11 of 24 Time sharing several terminals were connected to a single computer running a timesharing operating system MITS Altair 1975 was the rst lowcost personal computer Graphical User Interface was introduced in 1984 by Apple Local Area Network LAN allowed hardware and software communication to connect computers in an of ce Chapter 15 from Information Systems Introduction Management must recognise the potential downside of relying on information systems IS failures can have financial legal and moral consequences Key Concepts IT Components computers system software and networking hardware software and communications links Human IT Infrastructure the human resources required to con gure operate and maintain the IT components and applications Shared IT Services and Applications shared IT services oriented more toward enabling the organisation to function more effectively ex email SPAM ltering ERPS Information Technology Infrastructure Library ITIL provides framework of best management practices developed by the Of ce of Government Commerce UK Alexandra Tilton Page 12 of 24 What Constitutes System Failure Unlike tangible assets information does not disappear when it has been stolen Failure can also happen if information can no longer be trusted Failure can happen if there is a denial of access to authorised users Potential Causes of Systems Failure Accidental behaviour by organisational members Accidental behaviour by tech support personnel Accidental behaviour by other authorised users Malicious behaviour by organisational insider Malicious behaviour by theft sabotage extortion Natural ood fire tornado etc Environmental Utility failure chemical spill etc Technical Hardware or software failure Operational Faulty process that unintentionally compromises information confidentiality integrity or availability Mitigating Risk Actions designed to counter identi ed threats Management controls processes which identify organisational requirements for system confidentiality integrity availability Operational controls daytoday processes more directly associated with the actual delivery of the information services Technical controls technical capabilities incorporated into the IT infrastructure speci cally to support increased confidentiality integrity and availability of information services Alexandra Tilton Page 13 of 24 Mitigating Risk with Management Controls 0 Creation of policies standards and requirements that directly relate to IS security 0 Performance of risk analyses to evaluate potential risk 0 Management of IS change Information Assurance Policies Procedures Standards and Education 0 Policies are highlevel statements communicating an organisation s goals objectives and the general means for their accomplishment 0 Procedures and standards more speci cally elaborate What needs to be done 0 These must have support of upper management 0 Must be clear concise wellwritten 0 Clearly delineate responsibilities 0 Living changing documents 0 Specify enforcement provisions and exceptions Risk assessment New systems and old 0 Clearly identifying organisational information assets 0 Understanding vulnerabilities 0 Identifying threats Managing change and system con gurations 0 An effective change management process includes Selection of an appropriate and quali ed team Formal change requests and tracking system Regularly scheduled meetings Ensuring that approved changes are communicated to stakeholders Alexandra Tilton Page 14 of 24 0 Regularly scheduled system audits to ensure change management practices are being followed Mitigating risks with operational controls 0 Three operational controls commonly associated with maintaining system availability are 0 System monitoring and incident response 0 Performing system backups 0 Planning for disaster recovery 0 Must be able to detect incidents and respond to them quickly 0 Organisation must decide if it wants to invest in proactive or reactive system monitoring System backups 0 There are two types of system backups 0 full system backups 0 incremental backups 0 Maintaining multiple backup copies provides several benefits Planning for disaster recovery and business continuity 0 Disaster recovery plan should provide detailed guidance concerning the actions to be taken in the event of a disaster 0 This requires clear established priorities 0 Hot backup site essential systems have been duplicated at the alternative facility and are fully con gured to pickup operations if primary site fails 0 Warm site has many but not all capabilities of the hot site 0 Purchasing redundant hardware in case rst set fails 0 Management must consider all potential causes of failure in prioritising its investments in technical and systems controls Alexandra Tilton Page 15 of 24 Section 33 from INFO Textbook 33 Measures of Distribution Shape Relative Location and Detecting Outliers Alexandra Tilton Skewness Helps describe the shape of the data 0 Skewness is negative 2 left skew 0 Skewness is positive 2 right skew 0 Skewness is O data is symmetric 0 Symmetric distribution 2 mean and median are equal zScore Measure of the relative location of the data away from the mean 0 Can be interpreted as the number of standard deviations x1 is away from the mean Chebyshev s Theorem At least 11zA2 of the data values must be within 2 standard deviations of the mean where z is any value greater than 1 Empirical rule used to determine the percentage of data values that must be within a speci ed number of standard deviations from the mean Detecting Outliers Outliers are the extreme values of the data Any data with a zscore less than 3 or greater than 3 is identified as an outlier 60 Minutes quotEWaste Video Town in China can t breath air or drink water Wasteland of electronics that have been thrown away Many harmful substances within our electronic devices EWaste is fastest growing component of waste stream We throw out about 100 million cellphones a year in the US Some electronic recyclers ship waste overseas for pro t Page 16 of 24 0 Low income workers oversees do not have the means to protect themselves against harmful substances in electronics 0 Against the law to export import ewaste 0 Burning computers to extract precious metals 0 This town has the highest level of cancercausing toxins in the world 0 Workers are paid about 8 per day 0 Executive Recycling caught exporting CRT computer parts Sections 34 8 35 from INFO Textbook 34 FiveNumber Summaries and Box Plots 0 FiveNumber Summary 0 Smallest value First quartile Median second quartile Third quartile Largest value 0 Box plot Graphical display of data based on a fivenumber summary 0 box drawn with ends on first and third quartile 0 vertical line is drawn at median 0 horizontal lines extend to show range 35 Measures of Association Between Two Variables Covariance shows the strength of the linear relationship between the data Alexandra Tilton Page 17 of 24 0 Correlation coefficient measure of the relationship between two variables that is not affected by the units of measurement for x and y 0 If correlation coefficient is 1 there is a perfect positive linear relationship 0 If correlation coefficient is 1 there is a perfect negative linear relationship 0 If it is 0 there is no linear relationship Chapter 8 from Information Systems Organisational Decision Making Rational View 0 Different ways of how decision making does occur within organisations 0 Rational view the ideal situation 0 Three proposed phases of rational decision making from Herbert Simon Intelligence phase collecting information and data Design phase Generate possible alternative solutions Choice phase select the best alternative solution Implementation perform the necessary steps Decision Environments 0 Need to understand the difference between big and small decisions 0 Consider the degree of structure when making a decision Decision making systems view 0 The way of perceiving an organisation is typically referred to as the systems view Alexandra Tilton Page 18 of 24 IS as an InputProcessOutput Model 0 Data goes in computer people transform and complete it in order to then produce a nal output 0 This View can make it easier to identify different components of the system Controlling Controlling on the part of managers is essential to ensure the safety of the IS 0 Must distinguish who has responsibility Automating decisions 0 Whenever possible organisations want to automate certain types of decisions 0 Framework for categorising applications that are being used for automating decisions 0 Solution configuration 0 Yield optimisation 0 Routing or segmentation decisions 0 Corporate or regulatory compliance 0 Fraud detection 0 Dynamic forecasting 0 Operational control Supporting Complex Decisions 0 When mathematical model fits well it may be possible to create an IS to help automate decisions Knowledge management 0 Try to facilitate communications among knowledge workers within an organisation Alexandra Tilton Page 19 of 24 0 Try to make the expertise of a few available to many 0 Ensuring the knowledge of some experts is available to many within the company Business Intelligence 0 Describe a type of information system which is designed to help decisionmakers spot trends and relationships within large volumes of data 0 Typically used with large databases or data warehouses 0 Quality of the data must be very good Section 36 from INFO Textbook 36 Data Dashboards Adding Numerical Measures to Improve Effectiveness 0 Data dashboard is a set of visual displays that organises and presents information that is used to monitor the performance of an organisation 0 KPI Key Performance Indicators in data dashboard 0 Key components of a dashboard Stacked bar chart 0 Pie chart 0 Bar charts 0 Histogram Alexandra Tilton Page 20 of 24 Chapter 9 from Information Systems The Role of Data Management Technologies in Achieving Organisational Ef ciencies 0 Important bene t of information systems is the ability to manage data at rates and quantities that humans are incapable of 0 Basic responsibilities of data management technologies 0 Data models allow clients to specify what data are to be managed and certain constraints 0 Storage management providing mechanisms for storing data in a logically coherent and space efficient manner 0 Access methods allow location of desired data 0 Query processing and data manipulation allow people or software to create examine change and delete data 0 Security provide mechanisms for making data secure 0 Transaction processing providing mechanisms which allow multiple clients to simultaneously examine and change data without destroying its logical coherence 0 Application program interface allow other software systems to make use of data management system Representing reality through data management 0 Aspect of reality that is chosen to be represented depends on two determinants 39 domain 0 objects physical or conceptual entities 0 events types of occurrences such as transactions or deposits Alexandra Tilton Page 21 of 24 0 organisations tracking objects within a business such as people events and processes 0 physical phenomena any observable occurrences such as weather or astronomical processes 0 multiple domains management of data across several domains Data management domain examples Weather monitoring WM Motor Vehicle Registration MVR Library management LM Train reservation TR Manufacturing MAN What are data a datum is either a mathematical quantity set of symbols or some combination of the two to represent a fact Form in what form must a datum exist Meaning what does a datum actually mean datum is fact meaning we derive from it is information Knowledge constitutes the additional level of meaning we derive from information through some process data can only hope to represent some relatively simple aspect of reality data modelling is used to represent reality with data schema is a representation scheme entity is the basic unit of representation attributes are the characteristics of the data Alexandra Tilton Page 22 of 24 Granularity Level of speci city at which something is represented A lot of detail is said to be fine grained data Less detail means coarse grained Coarse grained is also called highlevel Identity Each entity must have an identity or identifier to give users a way of specifying with entities are of interest to them when they perform tasks with a collection of data Uniqueness Uniqueness can be created one way by using a combination of existing attributes whose data values taken together are unique Assigning values Typically data management can handle supporting the storage of various basic data types Text allows the use of character or string data Derived data values are values that are transported to other systems after being created in one system Representing composite entities Composite entities are comprised of other whole entities Constituent entities comprise composite entities Sometimes called parent and child entities These are called hasa relationship Alexandra Tilton Page 23 of 24 Metadata 0 Metadata are date about data 0 Physical storage description of data 2 physical schema 0 Logical schema describes the data model entities attributes etc 0 Semantic data models can provide human descriptions of attributes and define the value system Constraints 0 Limits or rules on what data you will accept 0 Cardinality constraints restrict the types of relationship cardinalities that exist between entities Making Analytics Work for You 0 Datadriven strategies will be key to competitive advantage 0 Need to be able to interpret any data your company gets 0 Choose the right data for your mission 0 Source the data in clever ways to get responses 0 Build models that predict and optimise business outcomes 0 Create simple and understandable tools for those who are going to implement decisions made by management Alexandra Tilton Page 24 of 24


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