ELECTIVE MIDMIDDD

Data Analysis

The process of inspecting, cleaning, transforming, and interpreting data to discover meaningful insights, patterns, and trends.

Data Collection

The process of collecting relevant data from various sources such as surveys, experiments, sensors, databases, websites, etc.

Data Cleaning

The process of cleaning and preprocessing raw data to ensure its quality and reliability by removing errors, inconsistencies, and handling missing values.

Data Transformation

The process of transforming data into a suitable format for analysis, including reshaping data, encoding categorical variables, and scaling numeric variables.

Exploratory Data Analysis (EDA)

The process of visualizing and summarizing data to understand its characteristics, including creating histograms, scatter plots, box plots, and calculating descriptive statistics.

Statistical Analysis

The use of statistical techniques to analyze data and test hypotheses, including hypothesis testing, regression analysis, ANOVA, and other statistical tests.

Machine Learning

The application of machine learning models to data for predictive modeling or pattern recognition tasks, such as making predictions, classifying data, or clustering data points.

Data Visualization

The use of charts, graphs, and other visualizations to present findings in a clear and accessible manner, aiding in conveying complex information to a broader audience.

Interpretation

The process of interpreting the results of data analysis and drawing conclusions, providing insights and recommendations based on the findings.

Reporting

The documentation of data analysis results in reports, presentations, or dashboards, effectively communicating findings to decision-makers and stakeholders.

Continuous Improvement

The iterative nature of data analysis, where analysts revisit methods and models, refine their approach, and incorporate new data to continually improve their analysis.

Pivot Table

A data processing tool used in spreadsheet software to summarize and analyze large datasets, allowing for rearranging and summarizing data into a more compact and organized format for analysis and reporting.

Mean (Average)

A measure of central tendency calculated by summing up all values in a dataset and dividing by the number of values, representing the "typical" or "average" value.

Median

A measure of central tendency in a dataset, representing the middle value when the data is arranged in ascending or descending order.

Mode

The value that appears most frequently in a dataset, useful for identifying the most common value or category.

Standard Deviation

A measure of dispersion or variability in a dataset, quantifying how much individual data points deviate from the mean.

Variance

A measure of the spread or dispersion of data points in a dataset, quantifying how far individual data points are from the mean.

Range

A simple measure of spread representing the difference between the maximum and minimum values in a dataset.

Population

Any specific collection of objects of interest.

Sample

Any subset or subcollection of the population.

Measurement

A number or attribute computed for each member of a population or sample.

Parameter

A number that summarizes some aspect of the population as a whole.

Statistic

A number computed from the sample data.

Statistics

The study of data, describing properties of the data, which is called descriptive statistics.

Descriptive Statistics

The branch of statistics that involves organizing, displaying, and describing data.

Inferential Statistics

The branch of statistics that involves drawing conclusions about a population based on information contained in a sample taken from that population.

Data Matrix

A tabular format representation of cases and variables in a statistical study, where each row represents a case and each column represents a variable.

When to Use What Measurement of Central Tendency

·         If data is categorical (nominal or ordinal), use mode.

·         If data is quantitative, use mean or median.

·         If data has influential outliers or is highly skewed, use median. Otherwise, use mean.

Range

·         the difference between the highest and lowest value in a dataset.

Interquartile Range

·         difference between the third and first quartile in a dataset. It leaves out the extreme values.

Box Plot

·         a graph that shows the median, quartiles, and outliers of a dataset. It is useful for detecting outliers and comparing distributions.

Standard Deviation

·         measures how spread out the data points are from the mean in a dataset. It is the square root of the variance.

Variance

·         the average of the squared differences between each data point and the mean in a dataset.