V3_L1: Problem to approach
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Analytic Approach
The process of selecting the appropriate method or path to address a specific data science question or problem.
Analytics
The systematic analysis of data using statistical, mathematical, and computational techniques to uncover insights, patterns, and trends.
Business Understanding
The initial phase of data science methodology involves seeking clarification and understanding the goals, objectives, and requirements of a given task or problem.
Clustering Association
An approach used to learn about human behavior and identify patterns and associations in data.
Cohort
A group of individuals who share a common characteristic or experience is studied or analyzed as a unit.
Cohort study
An observational study where a group of individuals with a specific characteristic or exposure is followed over time to determine the incidence of outcomes or the relationship between exposures and outcomes.
Congestive Heart Failure (CHF)
A chronic condition in which the heart cannot pump enough blood to meet the body’s needs, resulting in fluid buildup and symptoms such as shortness of breath and fatigue.
CRISP-DM
Cross-Industry Standard Process for Data Mining is a widely used methodology for data mining and analytics projects encompassing six phases: business understanding, data understanding, data preparation, modeling, evaluation, and deployment.
Data analysis
The process of inspecting, cleaning, transforming, and modeling data to discover useful information, draw conclusions, and support decision-making.
Data cleansing
The process of identifying and correcting or removing errors, inconsistencies, or inaccuracies in a dataset to improve its quality and reliability
Data science
An interdisciplinary field that combines scientific methods, processes, algorithms, and systems to extract knowledge and insights from structured and unstructured data.
Data science methodology
A structured approach to solving business problems using data analysis and data-driven insights.
Data scientist
A professional using scientific methods, algorithms, and tools to analyze data, extract insights, and develop models or solutions to complex business problems.
Data scientists
Professionals with data science and analytics expertise who apply their skills to solve business problems.
Data-Driven Insights
Insights derived from analyzing and interpreting data to inform decision-making
Decision tree
A supervised machine learning algorithm that uses a tree-like structure of decisions and their possible consequences to make predictions or classify instances.
Decision Tree Classification Model
A model that uses a tree-like structure to classify data based on conditions and thresholds provides predicted outcomes and associated probabilities.
Decision Tree Classifier
A classification model that uses a decision tree to determine outcomes based on specific conditions and thresholds.
Decision-Tree Model
A model used to review scenarios and identify relationships in data, such as the reasons for patient readmissions
Descriptive approach
An approach used to show relationships and identify clusters of similar activities based on events and preferences
Descriptive modeling
Modeling technique that focuses on describing and summarizing data, often through statistical analysis and visualization, without making predictions or inferences
Domain knowledge
Expertise and understanding of a specific subject area or field, including its concepts, principles, and relevant data
Goals and objectives
The sought-after outcomes and specific objectives that support the overall goal of the task or problem.
Iteration
A single cycle or repetition of a process often involves refining or modifying a solution based on feedback or new information.