Competency F
Conduct informatics analysis and visualization applied to different real-world fields, such as health science and sports.
Personal Definition and Importance
This competency is about presenting the value provided by data and information. As can be seen by all of the previous competencies, informatics is a multifaceted discipline. Ultimately, informatics is about providing information to end-users who can then make informed decisions. Data and information need to be analyzed and there need to be ways to meaningfully present such information. I do believe that visualization can be a precursor or a result of analysis. A geographic map or network topology can be presented and analysis of an efficient path or optimal defense can arise from such visualizations. Analysis and visualization is a back and forth, iterative process. If a visualization springs from analysis, then analysis of whether a certain visualization presents information accurately and clearly is needed; and vice-versa. I think being able to present analysis and visualization succinctly and accurately are what gives value derived from data and information provide the most benefit to a wider array of interested individuals.
Supporting Informatics Courses
The entirety of SJSU Informatics is about informatics analysis, every single course I took was about this in some form. For the cross-section between analysis and visualization the courses are many but can be pared down to much more than the three courses I am discussing here. INFM 203 Big Data Analytics and Management is about the massive amount of data that exists and how to derive valuable information from it, along with how to process and wrangle such data. INFM 215 Network Security (and most every other cybersecurity and privacy course) had labs that presented different network topologies to defend and attack for analysis; but we were tasked with drawing some topologies in this specific course. INFM 213 Epidemiological Methods is about public health information and how to analyze studies and present population level data with 2X2 matrices and how to convert visual charts and mathematical results into plain human language.
Evidence
Evidence 1: INFM 203 Project Demo
I selected this video presentation of my mini-project demo because it visually walks through where I procured stock information from and how I was able to more efficiently collect it using Python and Jupyter Notebooks. This presentation shows how data comes in using libraries to collect that data. Initially the data is raw and has to be manipulated to present tables and graphs. I also present and analyze how it is easier to access specific information using Python versus manually gaining chunks of the data via Excel files. There is a granular control that is available to visualize only the data sought after. Granularity could be a chosen time frame, filtered by minutes, months, or years; or specific data such as dividends or splits in stocks.
Evidence 2: INFM 215 Network Topology
I selected this project because it shows how analysis can begin from a visual topology of a network. There many different network topologies and understanding how they are related conceptually or physically placed provide for different types of analysis. The project analyzes star, bus, and hybrid topologies presented to determine which allow for easier expansion, and which allow for least expensive expansions. For informatics and cybersecurity it is probably most important to understand where data travels and can be accessed and moved both for value to stakeholders and for defense of the data itself. Topologies have multiple uses and can benefit many aspects of an informatics program, including understanding where data flows and is stored, for compliance and security, and for justifying expansion and reconfiguration in the business operations sense.
Evidence 3: INFM 215 Homework 4
I selected this homework because it presents the classification of studies, 2x2 epidemiological matrices, and formulas that can be converted to plain language. This document presents analysis of different studies about pregnancy induced hypertension, deep vein thrombosis, pre-exposure prophylaxis, and diet/cholesterol relationships. The studies had to be classified and/or analyzed based on their classification; Included were cohort studies, case-control studies, and cross-sectional studies. Other work in the course considered many other types of studies. Then there are concepts such as prevalence (existing cases) and incidence or cumulative incidence (cases that develop over time). Once a study is classified then the appropriate statistical math is applied and needs to be presented in simple language for public understanding. There are measures of disease frequency, measures of disease association, and measures of effect as samples of the purposes of different mathematical operations for analysis
Professional Application Value of Skill
The evidence presented shows my ability to understand the interrelationship between analysis and visualization in various contexts. I can recognize both graphical (graphs, topography) visualization and symbolic (math) visualization and how to represent them for analysis and reconfigure for dissemination to decision makers; such as turning symbolic results of formulas into natural language sentences. While it is understandable to believe, and in many cases is true, that visualizations are the result of analysis; it is just as true that analysis follows and depends upon visualization in many situations. I have presented a big data stock market view, a cybersecurity view, and an epidemiological view of how to analyze data and apply visualization to informatics problems.