Free Google Data Analytics Exam Flashcards

The process of collecting, transforming, and organizing data to draw conclusions, make predictions, and drive informed decision-making. Data analysts use various tools and techniques to find patterns and insights in data that help businesses solve problems and achieve their goals. The field combines statistics, programming, and domain knowledge to extract actionable insights.

Using facts, metrics, and data to guide strategic business decisions that align with goals and objectives. This approach replaces intuition-based decisions with evidence-based conclusions, reducing bias and increasing accuracy. Organizations that embrace data-driven decision making are more likely to acquire customers, achieve profitability, and retain employees.

Ask (define the problem), Prepare (collect and store data), Process (clean and transform data), Analyze (identify patterns and relationships), Share (communicate findings), and Act (implement recommendations). This framework guides analysts through the complete lifecycle of a data project, ensuring systematic and thorough analysis.

Specific, objective measures of numerical facts that can be counted, measured, or easily expressed with numbers. Examples include sales figures, website traffic, customer counts, and test scores. Quantitative data answers questions like 'how many' or 'how much' and is essential for statistical analysis and trend identification.

Subjective or explanatory measures of qualities and characteristics that cannot be measured numerically. Examples include survey responses, interview transcripts, customer reviews, and user feedback. Qualitative data answers 'why' questions and provides context and deeper understanding of numerical patterns.

People who have invested time, interest, or resources into a project and will be affected by its outcome. In data analytics, stakeholders include executives, managers, clients, and team members who need data insights to make decisions. Effective analysts communicate findings clearly and tailor presentations to stakeholder needs and technical levels.

Quantifiable measures used to evaluate the success of an organization, project, or individual in meeting objectives. Examples include customer retention rate, revenue growth, and customer satisfaction scores. Data analysts track KPIs to measure progress and identify areas for improvement.

The various elements that interact with one another to produce, manage, store, organize, analyze, and share data. This includes hardware, software, databases, data warehouses, and the people who use them. Understanding the data ecosystem helps analysts locate data sources and understand how data flows through an organization.

The accuracy, completeness, consistency, and trustworthiness of data throughout its lifecycle. Maintaining data integrity involves following proper collection procedures, implementing validation rules, and establishing data governance policies. Poor data integrity leads to unreliable analysis results and flawed business decisions.

The process of identifying and correcting errors, inconsistencies, and inaccuracies in datasets. Common tasks include removing duplicates, handling missing values, fixing structural errors, standardizing formats, and validating data against business rules. Clean data is essential for accurate analysis and reliable insights.

Values that are absent from a dataset, which can occur due to data entry errors, system failures, or respondent non-response. Handling strategies include deletion (removing rows or columns), imputation (filling with mean, median, or predicted values), or keeping as-is with appropriate notation. The chosen approach depends on the amount of missing data and its pattern.

A systematic error in data collection, analysis, or interpretation that skews results in a particular direction. Types include sampling bias (non-representative samples), confirmation bias (favoring expected results), and selection bias (non-random participant selection). Recognizing and mitigating bias is crucial for fair and accurate analysis.

Well-founded standards of right and wrong that dictate how data is collected, shared, and used. Key principles include informed consent, data privacy, transparency, and avoiding harm. Ethical data practices build trust with customers and protect organizations from legal and reputational risks.

Data that describes other data, providing information about its content, context, quality, and structure. Examples include file creation dates, column names, data types, and author information. Metadata helps analysts understand datasets quickly and ensures data is properly documented for future use.

The process of checking data for accuracy and quality before analysis. Techniques include range checks (values within expected bounds), format checks (consistent data formats), and consistency checks (logical relationships between fields). Validation prevents errors from propagating through analysis and corrupting results.

The number of observations or data points included in a sample from a larger population. Adequate sample size ensures statistical significance and reliable conclusions. Too small samples may not represent the population accurately, while unnecessarily large samples waste resources. Sample size calculations consider confidence level, margin of error, and population variability.

A standard programming language used to communicate with relational databases. SQL allows analysts to query, insert, update, and delete data, as well as create and modify database structures. It is one of the most essential skills for data analysts, enabling efficient data extraction and manipulation across various database systems.

The SQL command used to retrieve data from one or more tables. Basic syntax is SELECT column_names FROM table_name. Use SELECT * to retrieve all columns, or specify column names separated by commas. The SELECT statement is the foundation of SQL queries and is combined with other clauses for filtering, sorting, and grouping data.

A SQL clause used to filter records based on specified conditions. Syntax is SELECT columns FROM table WHERE condition. Supports comparison operators (=, <>, <, >, <=, >=), logical operators (AND, OR, NOT), and pattern matching (LIKE). WHERE limits results to rows matching the criteria, reducing data volume and focusing analysis.

SQL operations that combine rows from two or more tables based on related columns. INNER JOIN returns matching rows, LEFT JOIN includes all rows from the left table, RIGHT JOIN includes all rows from the right table, and FULL OUTER JOIN includes all rows from both tables. Joins are essential for analyzing data stored across multiple related tables.

A SQL clause that groups rows sharing common values into summary rows, typically used with aggregate functions. Syntax is SELECT column, AGGREGATE_FUNCTION(column) FROM table GROUP BY column. For example, GROUP BY can calculate total sales by region or average order value by customer segment.

SQL functions that perform calculations on multiple rows and return a single value. Common functions include COUNT (number of rows), SUM (total of values), AVG (average), MIN (smallest value), and MAX (largest value). Aggregate functions are essential for summarizing data and calculating statistics across datasets.

A SQL clause that sorts query results based on one or more columns. Syntax is SELECT columns FROM table ORDER BY column ASC|DESC. ASC sorts in ascending order (default), DESC sorts in descending order. Multiple columns can be specified for multi-level sorting, enabling organized and readable output.

A SQL clause used to filter grouped records based on aggregate function results. HAVING is similar to WHERE but works with grouped data. Syntax is SELECT column, AGGREGATE_FUNCTION(column) FROM table GROUP BY column HAVING condition. For example, HAVING SUM(sales) > 10000 returns only groups exceeding the sales threshold.

A query nested inside another query, enclosed in parentheses. Subqueries can be used in SELECT, FROM, or WHERE clauses to create complex filters or derive intermediate results. They enable multi-step analysis within a single query and help break down complex problems into manageable parts.

A primary key uniquely identifies each record in a table and cannot contain NULL values. A foreign key is a column that references the primary key of another table, establishing relationships between tables. Understanding these keys is essential for database design and writing JOIN queries to combine related data.

A spreadsheet function that searches for a value in the leftmost column of a range and returns a value from a specified column. Syntax is VLOOKUP(search_key, range, index, is_sorted). Essential for combining data from different sheets or looking up related information, such as finding product prices from a product ID.

Interactive tables that summarize, sort, count, and aggregate data from larger datasets. Pivot tables allow analysts to reorganize data dynamically by dragging fields into rows, columns, and values areas. They are powerful tools for exploring data patterns, creating reports, and answering ad-hoc business questions without writing formulas.

A spreadsheet feature that changes cell appearance based on specified rules or conditions. Examples include highlighting cells above a threshold, color-coding negative values, or creating data bars. Conditional formatting makes patterns and outliers visually apparent, helping analysts quickly identify trends and anomalies in data.

Functions that perform calculations on cells meeting specified criteria. SUMIF adds values in a range where conditions are met; COUNTIF counts cells meeting criteria. Extended versions SUMIFS and COUNTIFS support multiple conditions. These functions enable targeted aggregation without filtering or restructuring data.

Spreadsheet operations for organizing and focusing on relevant data. Filtering temporarily hides rows not meeting criteria, while sorting arranges data in ascending or descending order by column values. Both operations help analysts focus on specific subsets and identify patterns in large datasets without modifying underlying data.

Spreadsheet functions for manipulating text data including CONCATENATE (join strings), SPLIT (separate text), TRIM (remove extra spaces), LEFT/RIGHT/MID (extract substrings), and UPPER/LOWER (change case). These functions are essential for cleaning messy text data and standardizing formats before analysis.

An open-source programming language and environment designed for statistical computing and graphics. R offers extensive libraries for data manipulation, visualization, and statistical analysis. It is widely used in data science, academia, and industry for its powerful analysis capabilities and reproducible research features.

A collection of R packages designed for data science, sharing common design philosophy and grammar. Core packages include ggplot2 (visualization), dplyr (manipulation), tidyr (tidying), readr (importing), and tibble (data frames). Tidyverse provides a consistent, intuitive approach to data analysis workflows.

An R package providing a grammar for data manipulation with key functions: filter() (row selection), select() (column selection), mutate() (create new variables), arrange() (sorting), summarize() (aggregation), and group_by() (grouped operations). dplyr uses the pipe operator (%>%) to chain operations for readable code.

An R package for creating elegant data visualizations based on the Grammar of Graphics. Charts are built in layers: data, aesthetic mappings (aes), geometric objects (geom), and additional customizations. ggplot2 produces publication-quality graphics and supports a wide variety of chart types.

The fundamental data structure in R for storing tabular data, similar to spreadsheet tables or SQL tables. Data frames contain rows (observations) and columns (variables) of different data types. They are created using data.frame() or by importing data from files. Most R analysis functions work with data frames.

A dplyr/magrittr operator that passes the output of one function as the input to the next function. Instead of nesting functions, pipes create readable left-to-right code flow. For example: data %>% filter(x > 5) %>% summarize(mean = mean(y)) reads as 'take data, filter where x > 5, then summarize.'

A document format that combines R code, output, and narrative text into a single reproducible document. R Markdown supports multiple output formats including HTML, PDF, and Word. It enables analysts to create reports, presentations, and dashboards that automatically update when data changes.

An R package for transforming messy data into tidy format where each variable is a column, each observation is a row, and each value is a cell. Key functions include pivot_longer() (columns to rows), pivot_wider() (rows to columns), separate() (split columns), and unite() (combine columns).

The graphical representation of information and data using visual elements like charts, graphs, and maps. Effective visualization makes complex data accessible, reveals patterns and trends, and communicates insights to stakeholders. Good visualizations are clear, accurate, and appropriate for the data type and audience.

Selecting appropriate visualizations based on data and communication goals. Bar charts compare categories, line charts show trends over time, scatter plots reveal relationships between variables, pie charts display proportions, and histograms show distributions. The wrong chart type can obscure insights or mislead viewers.

Guidelines for creating effective visualizations including: maximize data-ink ratio (reduce clutter), maintain appropriate aspect ratios, use consistent color schemes, label axes clearly, and avoid 3D effects that distort perception. Good design ensures visualizations communicate insights accurately without misleading or confusing the audience.

Creating interactive displays that present key metrics and data visualizations in a single view. Effective dashboards prioritize important information, use appropriate chart types, maintain visual consistency, and enable user interaction. Dashboards help stakeholders monitor performance and make data-driven decisions quickly.

The practice of communicating data insights through compelling narratives that engage audiences and drive action. Effective data storytelling combines accurate analysis, clear visualizations, and persuasive narrative structure. Stories provide context, highlight key findings, and explain why the data matters to stakeholders.

Using color strategically to enhance comprehension and highlight insights. Sequential palettes show ordered data (light to dark), diverging palettes highlight deviation from a midpoint, and categorical palettes distinguish discrete groups. Consider colorblind accessibility and cultural color associations when designing visualizations.

A leading data visualization and business intelligence tool that transforms raw data into interactive, shareable dashboards. Tableau connects to various data sources, enables drag-and-drop visualization creation, and supports real-time data analysis. It is widely used in industry for its powerful features and user-friendly interface.

Custom formulas created in Tableau to compute new data from existing fields. Calculated fields can perform mathematical operations, text manipulation, date calculations, and logical conditions. They extend analysis capabilities beyond the original data structure without modifying the data source.

Dimensions are categorical fields (names, dates, geographic locations) that segment data into categories for analysis. Measures are quantitative fields (sales, counts, percentages) that can be aggregated mathematically. Understanding this distinction is fundamental to building effective Tableau visualizations and analyses.

Interactive controls that allow users to focus on specific data subsets. Filters restrict which data appears in visualizations based on dimension or measure values. Parameters are user-defined variables that can dynamically change calculations, filters, or reference lines, enabling flexible, interactive dashboard experiences.