4. Introduction
When justifying the conclusions or results of your project, you need to establish what you
measured them against.
Setting the level for success (e.g., benchmark) helps you to decide on an acceptable measure for
the program.
This step of justifying conclusions involves more than just analyzing and comparing data.
It also includes interpreting your findings and judging them against an established basis.
Not much can be conveyed by simple presenting a huge collection data in your report. Hence, data
analysis is important.
5. Data Analysis
• Art of finding patterns
• Begins with open questions that progressively narrow
• Checking reliability of assumptions
• Integration of different types of data
6. Synthesis
• “Synthesis is the process of making meaning through inference-
based sensemaking” (J. Kolko)
• Synthesis goals:
• Make sense of the data
• Understand intent
• Move towards insights
8. Analyzing data
• Quantitative analysis uses mathematical operations to investigate the properties
of data.
• The best way to conduct quantitative analysis is by using statistics.
• One of the primary purposes of scientific investigation is to discover relationships
between variables so that we can explain, predict and control them.
• Statistical methods serve as a valuable tool for discovering and quantifying these
relationships.
• You do not have to be a mathematician to use this special language, as there are
various user-friendly computer packages (e.g. SPSS, Matlab, MS Excel, PowerBI)
will do all the calculations for you.
9. Analyzing data
• Qualitative analysis expresses the nature of elements and is
represented as a new concepts and theory.
• The analysis tools for this include:
• Categorisation and theme- based analysis
• Quantitative analysis on text based- data using statistical packages such as
SPSS, etc.
10. Synthesizing Data
• After, data analysis, you will need to present the findings of your study.
• This should be done effectively in the Results and Discussion chapter of your final
report.
• It is important to present your results appropriately using words and graphics.
• You will need to:
• Properly use text and visual aids
• Interpret the results
• Use proper language of reporting
• Refer to figures or tables correctly (Always cite the Figures/ Tables that you are
explaining)
16. SADT
• A model of the problem is constructed, which is composed of hierarchy of
diagrams.
• Each diagram is composed of boxes and arrows.
• The topmost diagram, called the context diagram, defines the problem most
abstractly.
• As the problem is refined into sub-problems, this refinement is documented
into other diagrams.
• Boxes should be given unique names that should always be verb phrases,
because they represent the functions.
22. The Transition to Systems Design
• Preparing for Systems Design Tasks
– It is essential to have an accurate and understandable system requirements
document
• Logical and Physical Design
– The logical design defines the functions and features of the system and the
relationships among its components
– The physical design of an information system is a plan for the actual
implementation of the system
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23. Steps in Design Phase
•Select design strategy
•Design architecture
•Select hardware and software
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24. Systems Design Guidelines
• Overview
• A system is effective if it supports business requirements and meets user
needs
• A system is reliable if it handles input errors, processing errors, hardware
failures, or human mistakes
• A system is maintainable if it is flexible, scalable, and easily modified
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25. Systems Design Guidelines
• Overview
• User Considerations
• Carefully consider any point where users receive output from, or provide input
• Anticipate future needs
• Provide flexibility
• Parameter, default
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26. Systems Design Guidelines
• Overview
– Data Considerations
• Enter data as soon as possible
• Verify data as it is entered
• Use automated methods of data
entry whenever possible
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27. Systems Design Guidelines
• Overview
• Data Considerations
• Control data entry access and report all entries or changes to critical
values – audit trail
• Log every instance of data entry and changes
• Enter data once
• Avoid data duplication
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28. Systems Design Guidelines
• Overview
• Architecture considerations
• Use a modular design
• Design modules that perform a single function are easier to understand, implement, and
maintain
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29. Systems Design Guidelines
• Design Trade-Offs
• Design goals often conflict with each other
• Most design trade-off decisions that you will face come down to the basic
conflict of quality versus cost
• Avoid decisions that achieve short-term savings but might mean higher costs
later
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SADT (Structured Analysis and Design Technique)
OOADT (object orientated analysis and design technique)
Not much can be conveyed by simple presenting a huge collection data in your report. Hence, it is important to analyse your data using appropriate techniques.
You are wasting your time and effort if: if carry out analysis which is not relevant to the objectives of your study. And you collect your data which you are not able to analyse because you have either too much, or because you have insufficient or inappropriate analytical skills to make the analysis.
Qualitative or Categorical Data
Qualitative data, also known as the categorical data, describes the data that fits into the categories. Qualitative data are not numerical. The categorical information involves categorical variables that describe the features such as a person’s gender, home town etc. Categorical measures are defined in terms of natural language specifications, but not in terms of numbers.
Sometimes categorical data can hold numerical values (quantitative value), but those values do not have a mathematical sense. Examples of the categorical data are birthdate, favourite sport, school postcode. Here, the birthdate and school postcode hold the quantitative value, but it does not give numerical meaning.
Nominal Data
Nominal data is one of the types of qualitative information which helps to label the variables without providing the numerical value. Nominal data is also called the nominal scale. It cannot be ordered and measured. But sometimes, the data can be qualitative and quantitative. Examples of nominal data are letters, symbols, words, gender etc.
The nominal data are examined using the grouping method. In this method, the data are grouped into categories, and then the frequency or the percentage of the data can be calculated. These data are visually represented using the pie charts.
Ordinal Data
Ordinal data/variable is a type of data that follows a natural order. The significant feature of the nominal data is that the difference between the data values is not determined. This variable is mostly found in surveys, finance, economics, questionnaires, and so on.
The ordinal data is commonly represented using a bar chart. These data are investigated and interpreted through many visualisation tools. The information may be expressed using tables in which each row in the table shows the distinct category.
Quantitative or Numerical Data
Quantitative data is also known as numerical data which represents the numerical value (i.e., how much, how often, how many). Numerical data gives information about the quantities of a specific thing. Some examples of numerical data are height, length, size, weight, and so on. The quantitative data can be classified into two different types based on the data sets. The two different classifications of numerical data are discrete data and continuous data.
Discrete Data
Discrete data can take only discrete values. Discrete information contains only a finite number of possible values. Those values cannot be subdivided meaningfully. Here, things can be counted in whole numbers.
Example: Number of students in the class
Continuous Data
Continuous data is data that can be calculated. It has an infinite number of probable values that can be selected within a given specific range.
Example: Temperature range
Properly use text and visual aids
Present your findings in words with the aid of tables, charts and graphs in order to make your findings clear and easy to understand.
Remember, that you should write a report not to draw a report.
The text is primary
The graphics are only to support the text that you have written.
Interpret the results
You not only need to report data, but you also need to interpret the data, which is to say what the data means in relation to your research question.
Use proper language of reporting
Do not use spoken language, use technical writing style
Refer to figures or tables correctly (Always cite the Figures/ Tables that you are explaining)
If you include any figure or table in your report, you should:
Number it and give it a concise and accurate caption.
Draw the reader’s attention to the figure or table in your text.
Make sure that the figure or table is located after the text that mentions it.
The left side is an example of good chart and right is a bad chart. Bad because it is not labelled. The left pie chart would be better if the percentage for each category is labelled.
SADT (Structured Analysis and Design Technique)
Structured analysis and design technique (SADT) is a systems engineering and software engineering methodology for describing systems as a hierarchy of functions. SADT is a structured analysis modelling language, which uses two types of diagrams: activity models and data models.
A main box where the name of the process or the action is specified
On the left-hand side of this box, incoming arrows: inputs of the action.
On the upper part, the incoming arrows: data necessary for the action.
On the bottom of the box, incoming arrows: means used for the action.
On the right-hand side of the box, outgoing arrows: outputs of the action.
The topmost diagram, called the context diagram, defines the problem most abstractly.
As the problem is refined into sub-problems, this refinement is documented into other diagrams.
Boxes should be given unique names that should always be verb phrases, because they represent the functions.
OOADT (object orientated analysis and design technique)
Object-oriented analysis and design (OOAD) is a software engineering approach that models a system as a group of interacting objects. Each object represents some entity of interest in the system being modeled, and is characterised by its class, its state (data elements), and its behavior. Various models can be created to show the static structure, dynamic behavior, and run-time deployment of these collaborating objects. There are a number of different notations for representing these models, such as the Unified Modeling Language (UML). Object-oriented analysis (OOA) applies object-modeling techniques to analyze the functional requirements for a system. Object-oriented design (OOD) elaborates the analysis models to produce implementation specifications. OOA focuses on what the system does, OOD on how the system does it. Examples are: use case and sequence diagram.
An important component of the design phase is the architecture design, which describes that system’s hardware, software and network requirement. The architecture design flows primarily from the nonfunctional requirements, such as operational, performance, security, cultural and political requirements.
Operational requirements (system integration, portability, maintainability)
Performance requirement (speed, capacity, availability/reliability)
Security requirement (high system value, access control requirement, encryption/authentication req, virus control req)
Cultural/political req (multilingual, customization, making instated norms explicit,legal req)
The deliverables from architecture design include the architecture design and the hardware and software specification.
Hardware: capacity of disk drive, processor)
Software (OS, need of having special software such as oracle/java)
