MKTG55

• Data analysis is the process of inspecting, cleaning, transforming, and modeling data to discover useful information, draw conclusions, and support decision-making.
• Data analysis plays a crucial role in research by providing insights and validating hypotheses.
• Quantitative data is information expressed in numbers and measurable quantities, such as survey responses and experimental data.
• Qualitative data is descriptive data that cannot be easily measured or expressed numerically, involving observations, interviews, and focus groups.
• Mixed methods data is a combination of quantitative and qualitative data to gain a comprehensive understanding of the research topic.
• Descriptive analysis involves summarizing and describing the main characteristics of data, such as mean, median, and standard deviation.
• Inferential analysis uses statistical techniques to make predictions and generalizations about a larger population based on a sample of data.
• Exploratory analysis identifies patterns, relationships, and trends to generate new hypotheses and insights.
• Data analysis software tools like R, Python, and Excel provide powerful capabilities for processing, analyzing, and visualizing data.
• Data visualization is the graphical representation of data using charts, maps, and graphs to help gain insights and communicate findings effectively.
• Graphical representation is a form of visually displaying data through various methods like graphs, diagrams, charts, and plots, helping in sorting, visualizing, and presenting data in a clear manner through different types of graphs.
• Statistics mainly use graphical representation to show data.
• Data quality in data analysis involves ensuring the accuracy, completeness, and reliability of data by addressing missing values, outliers, and inconsistencies.
• Data privacy in data analysis involves protecting sensitive information and adhering to ethical guidelines in handling and sharing data.
• Data visualization is a powerful tool that helps researchers explore and communicate their findings more effectively through charts, graphs, and infographics, making patterns and trends in the data more accessible and understandable to a wider audience.
• Data visualization enhances the impact of research and facilitates decision-making processes.
• Data analysis is the process of inspecting, cleaning, transforming, and modeling data to discover useful information, draw conclusions, and support decision-making.
• Data analysis plays a crucial role in research by providing insights and validating hypotheses.
• Quantitative data is information expressed in numbers and measurable quantities, such as survey responses and experimental data.
• Qualitative data is descriptive data that cannot be easily measured or expressed numerically, involving observations, interviews, and focus groups.
• Mixed methods data is a combination of quantitative and qualitative data to gain a comprehensive understanding of the research topic.
• Descriptive analysis involves summarizing and describing the main characteristics of data, such as mean, median, and standard deviation.
• Inferential analysis uses statistical techniques to make predictions and generalizations about a larger population based on a sample of data.
• Exploratory analysis identifies patterns, relationships, and trends to generate new hypotheses and insights.
• Data analysis software tools like R, Python, and Excel provide powerful capabilities for processing, analyzing, and visualizing data.
• Data visualization is the graphical representation of data using charts, maps, and graphs to help gain insights and communicate findings effectively.
• Graphical representation is a form of visually displaying data through various methods like graphs, diagrams, charts, and plots, helping in sorting, visualizing, and presenting data in a clear manner through different types of graphs.
• Statistics mainly use graphical representation to show data.
• Data quality in data analysis involves ensuring the accuracy, completeness, and reliability of data by addressing missing values, outliers, and inconsistencies.
• Data privacy in data analysis involves protecting sensitive information and adhering to ethical guidelines in handling and sharing data.
• Data visualization is a powerful tool that helps researchers explore and communicate their findings more effectively through charts, graphs, and infographics, making patterns and trends in the data accessible and understandable to a wider audience.
• Data visualization enhances the impact of research and facilitates decision-making processes.
• bar chart compare numerical data and demonstrate growth
• pie/donut chart show how individual parts make up a whole
• line chart demonstrate change and progress
• bubble chart visualize relationship among numerical variables
• map represents any data that has to do with geo-location
• table compare data, show prices for service, create reports, etc.