Data Analysis and Visualization Lecture PDF

Data Analysis and Visualization for Teaching and Learning Dr. Eman Omar Eldawy Basis for Student Evaluation Category Points Participation and Formative Assessment 10 Ongoing Assessment...

Data Analysis and Visualization for Teaching and Learning Dr. Eman Omar Eldawy Basis for Student Evaluation Category Points Participation and Formative Assessment 10 Ongoing Assessment 10 Inquiry, Lab or Field Study Product 20 Case Study, Research or Problem Analysis 10 Presentation or Teaching Presentation 10 Tests of Concept or Pedagogical Knowledge 30 Culminating Applied Product 10 Total 100 2 Tools for Lab The course covers in detail the plotting systems in R as well as some of the basic principles of constructing data graphics. 3 4 Data analysis Data analysis is the process of collecting, modeling, and analyzing data using various statistical and logical methods and techniques. Data analysis aims to find actionable insights that can inform decision-making. All these various methods are largely based on two core areas: 1. Quantitative and 2. Qualitative research. 5 The primary objectives of Data Analysis 1. Understanding Data: Examining the characteristics and structure of data to gain insights into its nature. 2. Data Cleaning and Preprocessing: Identifying and handling missing or inconsistent data, outliers, and other anomalies to ensure data quality. 3. Exploratory Data Analysis (EDA): Employing statistical and graphical methods to summarize, visualize, and understand the main features of a dataset. 4. Descriptive Statistics: Utilizing measures such as mean, median, mode, and standard deviation to describe the main features of a dataset. 6 The primary objectives of Data Analysis (cont.) 5. Inferential Statistics: Making inferences and predictions about a population based on a sample of data, often involving hypothesis testing and confidence intervals. 6. Data Visualization: Representing data visually through charts, graphs, and other graphical elements to facilitate understanding and interpretation. 7. Pattern Recognition: Identifying trends, patterns, and relationships within the data through statistical and machine learning techniques. 7 The primary objectives of data analysis (cont.) 8. Predictive Modeling: Building models to make predictions or classifications based on historical data. 9. Decision Support: Providing actionable insights to support informed decision-making in various domains, such as business, healthcare, finance, and research. 8 What Is The Data Analysis Process? 9 What Is The Data Analysis Process? The analysis process consists of 5 key stages. 1. Identify: The identification is the stage in which you establish the questions you will need to answer. This involves identifying the purpose of the analysis, the data required, and the intended outcome. For example, what type of packaging is more engaging to our potential customers? 2. Collect: The collection of data can come in different forms such as internal or external sources, surveys, interviews, questionnaires, and focus groups, among others. 10 What Is The Data Analysis Process? (cont.) 3. Clean: Not all the data you collect will be useful. You need to make sure to erase any white spaces, duplicate records, or formatting errors. This way you avoid hurting your analysis with bad-quality data. 4. Analyze: With the help of various techniques such as statistical analysis you can start analyzing and manipulating your data to extract relevant conclusions. At this stage, you find trends, correlations, variations, and patterns that can help you answer the questions you first thought of in the identify stage. 11 What Is The Data Analysis Process? (cont.) 5. Interpret: This stage is where the researcher comes up with courses of action based on the findings. For example, here you would understand if your clients prefer packaging that is red or green, plastic or paper, etc. Additionally, at this stage, you can also find some limitations and work on them. 12 Data Analysis Tools Microsoft Excel R SQL Python SPSS Matlab 13 Applications of Data Analysis Business: includes customer segmentation, sales forecasting, and market research. Healthcare: identify patterns and trends in patient data, improve patient outcomes, and optimize healthcare operations. Education: Data analysis is used to measure student performance, evaluate teaching effectiveness, and improve educational programs. 14 Data Analysis Methods 1. Quantitative Methods: methods that use numerical data or data that can be turned into numbers (e.g., category variables like gender, age, etc.) to extract valuable insights. 2. Qualitative Methods: defined as the observation of non-numerical data that is gathered and produced using methods of observation such as interviews, focus groups, questionnaires, and more. 15 What is Quantitative Data? Quantitative data is the value of data in the form of counts or numbers where each data set has a unique numerical value. This data can use for mathematical calculations and statistical analysis to make real-life decisions. For example, “How much did that laptop cost?” is a question that will collect quantitative data. Values are associated with most measuring parameters, such as pounds or kilograms for weight, dollars for cost, etc. 16 Types of Quantitative Data 17 Types of Quantitative Data with Examples Counter: Count equated with entities—for example, the number of people downloading a particular application from the App Store. Measurement of physical objects: Calculating measurement of any physical thing. For example, the HR executive carefully measures the size of each cubicle assigned to the newly joined employees. Sensory calculation: Mechanism to naturally “sense” the measured parameters to create a constant source of information. For example, a digital camera converts electromagnetic information to a string of numerical data. 18 Types of Quantitative Data with Examples (cont.) Projection of data: Future data projections can be made using algorithms and other mathematical analysis tools. For example, a marketer will predict an increase in sales after launching a new product with a complete analysis. Quantification of qualitative entities: Identify numbers to qualitative information. For example, asking respondents of an online survey to share the likelihood of recommendation on a scale of 0-10. 19 Quantitative Data: Collection Methods As quantitative data is in the form of numbers, mathematical and statistical analysis of these numbers can lead to establishing some conclusive results. There are two main Quantitative Data Collection Methods: 1. Surveys 2. One-on-one Interviews 20 Surveys 21 Surveys Traditionally, surveys were conducted using paper- based methods and have gradually evolved into online mediums. Closed-ended questions form a major part of these surveys as they are more effective in collecting data. The survey includes answer options they think are the most appropriate for a particular question. 22 One-on-one Interviews 23 One-on-one Interviews This quantitative data collection method was also traditionally conducted face-to-face but has shifted to telephonic and online platforms. Interviews offer a marketer the opportunity to gather extensive data from the participants. Quantitative interviews are immensely structured and play a key role in collecting information. 24 Data Visualization Data Visualization is the process of taking a set of data and representing it in a visual format. Data visualization is the graphical representation of information and data by using visual elements (like charts, graphs, and maps). Data visualization tools provide an accessible way to see and understand trends, outliers, and patterns in data. 25 Data Visualization 26 Task #1 Great a Group of 1, or 2, or 3 or 4. Install R studio Search about dataset related to Teaching and Learning 27