Module 1: Introduction to Data Analytics Flashcards

Vocabulary flashcards covering core concepts, analytical types, workflow components, statistical measures, machine learning, roles, and ethics from Module 1: Introduction to Data Analytics.

Data Analytics

The process of examining, cleaning, transforming, and modeling raw data to discover useful information, draw conclusions, and support decision-making.

Descriptive Analytics

An analytics approach focused on summarizing and visualizing data to better understand its characteristics, distributions, and relationships using data aggregation and basic statistical measures.

Diagnostic Analytics

An analytics approach focused on investigating the causes and reasons behind observed patterns and anomalies in data through techniques like hypothesis testing, regression analysis, and anomaly detection.

Predictive Analytics

An analytics approach aimed at forecasting future outcomes or behaviors using historical data and statistical modeling techniques such as time series analysis and machine learning.

Prescriptive Analytics

An analytics approach that provides recommendations or actions to optimize decisions and outcomes by combining historical data, predictive models, and optimization techniques.

Data Interpretation

The skill of examining data to find meaning or an explanation, requiring strong analytical and critical thinking skills to identify patterns and understand implications.

Statistical Analysis

The application of statistical methods to analyze data, interpret data, and make inferences to uncover patterns, relationships, and trends.

Domain Knowledge

Understanding the business context, domain-specific terminology, and industry trends related to the specific field in which a data analyst works.

SQL (Structured Query Language)

A language used by data analysts for extracting, manipulating, and querying data from relational databases.

Exploratory Data Analysis (EDA)

A crucial step in the data analytics process involving examining and understanding data to gain insights, identify patterns, assess data quality, and formulate hypotheses.

Summary Statistics

Concise numerical summaries of data that provide information about central tendency, spread, and distribution.

Cross-Tabulations and Pivot Tables

EDA techniques used to analyze relationships between categorical variables by summarizing frequencies or proportions across categories.

Correlation Analysis

An EDA technique that measures the strength and direction of the linear relationship between two continuous variables.

Distribution Analysis

An EDA technique examining the distributional properties of variables using tools like histograms, kernel density plots, and Q-Q plots.

Mean

A measure of central tendency representing the arithmetic average of values in a dataset, calculated by summing all values and dividing by the number of observations.

Median

A measure of central tendency representing the middle value in an ordered dataset, dividing the data into two equal halves.

Mode

A measure of central tendency representing the most frequently occurring value or values in a dataset.

Range

A measure of dispersion representing the difference between the maximum value and the minimum value in a dataset.

Variance

A measure of dispersion representing the average of the squared differences between each data point and the mean.

Standard Deviation

A measure of dispersion calculated as the square root of the variance, representing the average deviation of data points from the mean.

Interquartile Range (IQR)

A measure of dispersion representing the range between the first quartile (25th percentile) and the third quartile (75th percentile).

Machine Learning

A subfield of artificial intelligence focused on developing algorithms and models that enable computers to learn from data and make predictions or decisions without being explicitly programmed.

Supervised Learning

A machine learning approach that uses labeled training data to build a model that predicts labels or target values for unseen data.

Unsupervised Learning

A machine learning approach that uses unlabeled data to discover patterns, structures, and relationships without predefined labels or guidance.

Business Intelligence Analyst

A data analytics career role focused on analyzing complex datasets, providing insights, developing reports and dashboards, and supporting organizational decision-making.

Data Engineer

A data analytics career role focused on designing, building, and maintaining data infrastructure, data pipelines, and system architecture.

General Data Protection Regulation (GDPR)

A privacy regulation cited as a legal compliance framework that analysts must follow to protect individuals' privacy and handle sensitive data securely.

Health Insurance Portability and Accountability Act (HIPAA)

An industry-specific privacy regulation cited for governing the protection and security of healthcare data.