**Machine Learning Techniques**: Machine learning techniques refer to the algorithms and methods used to train models that can automatically learn and improve from data without being explicitly programmed. This skill should be measured in the test as it is a fundamental component of data science, enabling data scientists to develop predictive models and make data-driven decisions.

**Data Visualization**: Data visualization involves creating visual representations of data to effectively communicate insights and patterns. This skill should be measured in the test as it is essential for data scientists to present complex data in a meaningful and understandable way, facilitating better decision-making and communication.

**Analytics with R or Python**: Analytics with R or Python refers to using programming languages such as R or Python to perform data analysis, statistical modeling, and machine learning tasks. This skill should be measured in the test as it assesses a candidate's ability to apply programming skills in data science projects, demonstrating their proficiency in handling data and implementing analytics algorithms.

**Exploratory Data Analysis**: Exploratory data analysis involves examining and transforming data to understand its main characteristics, patterns, and relationships. This skill should be measured in the test as it showcases a candidate's ability to extract meaningful insights from raw data, identify potential issues, and generate hypotheses for further analysis.

**Data Manipulation**: Data manipulation refers to the process of transforming, reformatting, or cleansing data to make it suitable for analysis. This skill should be measured in the test as it assesses a candidate's proficiency in handling and preparing data, which is a crucial step in the data science workflow before performing analytics or modeling tasks.

**Statistics**: Statistics involves the collection, analysis, interpretation, presentation, and organization of data. This skill should be measured in the test as it tests a candidate's understanding and application of statistical concepts and techniques, which are essential for conducting robust and valid data analysis.

**Regression Analysis**: Regression analysis is a statistical technique used to model the relationship between a dependent variable and one or more independent variables. This skill should be measured in the test as it evaluates a candidate's ability to perform regression analysis, which is widely used in predictive modeling and understanding the impact of variables on an outcome of interest.

**Data Cleansing**: Data cleansing involves identifying and correcting or removing errors, inconsistencies, or inaccuracies in datasets. This skill should be measured in the test as it assesses a candidate's capability to ensure data quality, which is crucial for obtaining reliable and accurate results in data analysis and modeling tasks.

**Predictive Modeling**: Predictive modeling is the process of developing and deploying mathematical models to predict future events or outcomes based on historical data. This skill should be measured in the test as it evaluates a candidate's ability to build predictive models using appropriate algorithms and evaluate their performance, which is a key component of many data science projects.