Practical CSV and Jupyter Data Extraction with Pandas
Case 2 — Data Extraction and Transformation
Basics of CSV & Jupyter
CSV – Comma Separated Values. Commas separate columns; missing values appear as blank entries or NaN. Jupyter Notebook rules – If running a cell in the middle, run all previous cells first. Keep the kernel up-to-date with earlier variable definitions and avoid running cells out of order to prevent NameError issues.
Volatility and Simple Calculations
Volatility: Defined as values above the median; the median splits the distribution
Read MoreBiostatistics for Biotechnology: Data, Probability & Analysis
🔵 Unit I — Introduction to Biostatistics
Biostatistics: Definition and Role
Biostatistics is a specialized branch of statistics concerned with the application of statistical principles and methods to biological, medical, and life-sciences data. In modern biological sciences, experiments and observations generate large volumes of data that cannot be interpreted accurately without proper statistical tools. Biostatistics provides a scientific framework to plan experiments, analyze experimental
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Understanding Statistical Dispersion
Dispersion is the extent to which data values in a dataset are spread out or scattered around a central value, such as the mean or median. It quantifies the variability or consistency within the data, complementing measures of central tendency (which describe the center of the data). A high dispersion indicates widely scattered data, while low dispersion suggests data points clustered closely together.
Measures of dispersion are essential for understanding data
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Descriptive Statistics
Descriptive statistics is a branch of statistics used to summarize, organize, and describe the main features of a dataset. It helps in understanding data using numerical measures like mean, median, mode, variance, and standard deviation.
1. Mean
- Mean is the average value of a dataset.
- It is calculated by dividing the sum of all observations by the total number of observations.
- It gives a general idea about the overall data value.
- Formula: Mean = (Sum of all values) / (Number of
Mastering Statistical Methods and Data Analysis
Sampling Methods
- Simple Random Sampling: Every subject has an equal probability of being selected. This provides a good representation but may be subject to non-response bias.
- Systematic Sampling: This involves applying a selection interval k from a random starting point. While every subject has an equal probability of being selected, it is simple but may not provide a good representation if there is a pattern in the way subjects are lined up.
- Stratified Sampling: The sampling frame is divided into
Sampling, Correlation, and Multivariate Methods for Research
Sampling: Population, Sample, Census
Population, sample, census: The population of interest is the entire group researchers want to generalize to. A sample is the smaller group that is actually observed or measured. A census collects data from every single member of the population. Population = who you care about. Sample = who you study. Census = everyone in the population.
Representative vs. Biased Samples
Representative vs. biased samples: A representative sample (unbiased) gives every member of
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