Reference no: EM133785015
Assignment: Fundamentals of Data Science
Part I: Understanding Data and Measurement
A. Data and Information Hierarchy: Describe the difference between data, information, knowledge, and wisdom, explaining the hierarchical relationship among them. Provide a specific, real-world example to illustrate each level of the hierarchy.
B. Variables and Measurement Scales: Explain the different types of variables (nominal, ordinal, interval, and ratio), and describe the associated scales of measurement. Provide a specific example of each type of variable and explain why it is classified as such.
Part II: Descriptive Statistics and Bivariate Analysis
A. Frequency Distribution and Summary Measures: Select a dataset (this could be publicly available data or a dataset from your workplace). Create a frequency distribution for a chosen variable, calculate common summary measures (mean, median, mode, range, variance, and standard deviation), and provide a short interpretation of these measures.
B. Bivariate Analysis: With the same dataset or a different one, conduct a bivariate analysis that includes both an association between two qualitative variables and a correlation between two quantitative variables. Interpret your findings.
Part III: Probability and Distributions
A. Probability: Discuss the basic rules of probability, conditional probability, and Bayes' theorem. Illustrate your discussion with unique examples.
B. Random Variables and Probability Distributions: Define discrete and continuous random variables. Give a real-world example of each and describe the associated probability distribution for each variable.
Part IV: Sampling Techniques
A. Sampling: Define and differentiate random and non-random sampling. Discuss how to determine an appropriate sample size for a given study. Include an illustrative example from a real or hypothetical research study.
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