III 2

• refers to the process of making sense of data by analyzing and drawing conclusions from it.

  data interpretation
• data interpretation can be used to make informed decisions and solve problems across wide range of fields, including

  business

  science

  science

  social sciences
• steps involved in data interpretation

  define the research question

  collect the data

  clean and organize the data

  analyze the data

  interpret the result

  communicate the findings
• the first step in data interpretation is to clearly define the ____. this will help you focus your anaysis, and ensure that you are interpreting the data in a way that is relevant to your research objectives.

  define the research question
• this can be done through a variety of methods such as survey, interviews, or secondary data

  collect the data
• this involves checking for errors, inconsistencies and missing data.

  clean and organize the data
• this involves using statistical softwares or other tools to calculate summary statistics, create graphs and charts and identify pattern in the data.

  analyze the data
• this involves looking for patterns, trends, and relationships in a data. it involves drawing conclusions based on the results of the analysis

  interpret the result
• this involves creating reports, presentations, or visualization that summarize the key fndings of the analysis.

  communicate the findings
• types of data interpretation

  descriptive

  inferential

  predictive

  exploratory

  causal
• summarizing and describing the key features of the data.

  descriptive
• measurement involved in calculating in descriptive

  measure of central tendency

  measures of dispersion

  creating visualization
• central tendency

  mean

  median

  mode
• measure of dispersion

  range

  variance

  standard deviation
• creating visualization

  histograms

  box plots

  scatterplots
• a type that involves making inferences about a larger population based on a sample of data. this involves hypothesis testing

  inferential
• a type that involves using data to make predictions about future outcomes

  predictive
• involves exploring the data to identify pattern and relationships that were not previously known.

  exploratory
• data mining technique

  clustering

  analysis

  principal component

  analysis

  associate rule mining
• a type that involves indentifying causal relationship between variables in the data. this involves experimental design, such as randomized controlled trials

  causal
• data interpretation methods

  statistical analysis

  data visualization

  text analysis

  machine learning

  qualitative analysis

  geospatial analysis
• method that involves using statistical techniques to analyze data

  statistical analysis
• statistical analysis involves

  descriptive statistics

  inferential statistics

  predictive modeling
• such as measures of central tendency and dispersion

  descriptive statistics
• such as hypothesis testing and confidence interval estimation

  inferential statistics
• such as regression analysis and time series analysis

  predictive modeling
• a method that involves using visual representations of the data, to identify pattern and trends.

  data visualization
• a method that involves analyzing of text data, such as survey responses or social media posts to identify patterns and themes

  text analysis
• a method that involves using algorithms to identify patterns in the data and make predictions or classifications

  machine learning
• machine learning techniques


  decision trees

  neutral networks

  random forest
• a method that involves analyzing non-numerical data, such as interviews, or focus group discussion to identify themes and patterns

  qualitative analysis
• qualitative analysis techniques


  content analysis

  ground theory

  narrative analysis
• a method that involves analyzing spatial data such as maps or gps coordinates to identify patterns and relationships

  geospatial analysis
• geospatial analysis techniques


  spatial autocorrelations

  hot spot analysis

  clustering
• applications of data interpretations

  business

  healthcare

  education

  social science

  sports
• when to use data interpretation?

  when dealing with large datasets or when trying to identify patterns or trends in data
• data interpretations examples


  social media analytics

  healthcare analytics

  financial analysis

  environmental monitoring

  traffic managements