Applied Statistics is the application of statistical methods to solve real-world problems in fields such as biology, engineering, social sciences, and business. It involves designing experiments, collecting and analyzing data, and making predictions or drawing conclusions based on the results. The skills needed for applied statistics include proficiency in statistical software, critical thinking and problem-solving, and clear communication.

Causal inference is the process of identifying the cause-and-effect relationship between two phenomena. It involves a specialized set of statistical and analytical techniques that allow researchers to analyze observational data and draw conclusions about causality. This field combines elements of statistics, computer science, and domain-specific knowledge to investigate causal relationships and formulate evidence-based interventions. It is an important skill for researchers, policymakers, and practitioners alike, as it can inform decision-making and improve outcomes in a range of fields.

Computational Statistics refers to the application of computational techniques to analyze and interpret statistical data. This skill encompasses the use of algorithms, simulations, and numerical methods to solve complex statistical problems and to perform data analysis. Knowledge of Computational Statistics is used to enhance the understanding of data patterns, model uncertainty, and make informed decisions based on statistical evidence in various fields such as finance, healthcare, and social sciences.

Logistic Regression refers to a statistical method used for binary classification that models the relationship between a dependent variable and one or more independent variables. This skill involves estimating the probability that a given input belongs to a particular category, typically through the use of a logistic function. Knowledge of Logistic Regression is applied in various fields, including medicine, finance, and social sciences, to predict outcomes such as disease presence, credit risk, and customer behavior based on input data.

Multivariate statistics involves analyzing complex data sets with multiple variables to identify patterns, relationships, and correlations between them. This specialized skill involves advanced statistical techniques such as factor analysis, cluster analysis, and regression analysis to draw insights and conclusions from the data. Multivariate statisticians uses software such as R, Python or SPSS to conduct analysis on provided data sets. They play a crucial role in various fields such as market research, social sciences, biology, finance, and engineering.