RM1

• Data refers to facts, observations, or information collected for analysis or reference.
• Sturctured Data is the data organized in a fixed format with a defined schema such as databases or spreadsheet.
• Unstructured Data is the data that does not have a predefined structure or format such as text documents, images, videos and social media posts.
• Quantitative data is the numerical data that can be measured and counted such as temperature readings, stock prices, or survey responses.
• Qualitative Data is non-numerical data that describes qualities or characteristics, such as interview transcripts, observational notes, or open-ended survey responses.
• Big data is large and complex datasets that require advanced tools and techniques for storage, processing, and analysis due to their volume velocity and variety.
• Data plays a crucial role in various fields, including science, business, healthcare, finance, and education.
• Analyzing and interpreting data can provide valuable insights, support decision-making and drive innovation and discovery.
• Qualitative data is information that describes qualities or characteristic.
• Qualitative Data is non numerical and often subjective.
• Qualitative Data is gathered through methods like interviews, observations, and open-ended surveys.
• Interview transcripts are quotes and descriptions from interviews that provide insights into people's thoughts, feelings, and experiences.
• Observational notes are the descriptions of behaviors, interactions, and events observed in a natural setting.
• Open-ended survey responses are written answers to survey questions that allow respondents to express their opinions and experiences in their own words.
• Field notes are detailed records of observations, reflections and impressions made by researches during fieldwork.
• Qualitative data analysis involves techniques like coding, categorizing, to identify patterns themes and insights within the data.
• Quantitative Data deals with numbers and measurable quantities.
• Quantitative Data often involves statistical analysis.
• Quantitative Data is collected through methods like surveys, experiments, and measurements.
• Quantitative Data analysis involves statistical methods such as descriptive statistics, inferential statistics and draw conclusions from the data.
• Quantitative Data is useful for identifying trends, making predictions, and testing hypotheses in research and decision-making processes.
• Statistical methods are techniques used to analyze and interpret data in order to make sense of patterns, relationships, and variability within the data.
• Descriptive Statistics are methods summarizing and describing the main features of a dataset
• Inferential statistics are methods involving making inferences or predictions about a population based on sample data.
• Probability Theory provides a framework for quantifying uncertainty and randomness in data.
• Correlation and regression analysis are methods examining the relationship between variables in a dataset.
• Experimental Design involves planning and conducting experiments to test hypotheses and determine causality.
• Statistical methods provide a systematic framework for data analysis and inference, helping to uncover insights and support evidence-based decision-making.
• Quantitative methods are set of techniques and approach used to collect, analyze and interpret numerical data.
• Quantitative methods are characterized by their emphasis on numerical data and the use of mathematical and statistical tools to analyze patterns, relationships, and variability within the data.
• Surveys involve collecting data from a sample of individuals or entities using structured questionnaires or interviews.
• Experiments involve manipulating one or more variables and observing the effect on another variable.
• Statistical analysis are used to summarize, analyze, and interpret numerical data.
• Econometrics applies statistical methods to economic data to analyze economic relationship, test economic theories and make predictions about economic phenomena.
• Financial modeling uses quantitative methods to analayze financial data and make forecasts about future financial performance.
• Quantitative methods provide a systematic and rigorous framework for data analysis, allowing researchers, analyst, and decision-makers to make evidence-based decisions and draw reliable conclusions from numerical data.