Google Data Analytics Foundations

The Google Data Analytics certificate teaches a practical workflow: Ask, Prepare, Process, Analyze, Share, and Act. Ask defines the business problem and the questions that will guide the work. Prepare gathers the right data, keeps it secure, and checks that it is reliable. Process cleans and validates the data, fixing missing values, duplicates, and inconsistent formats. Analyze explores and transforms data to find patterns and draw conclusions. Share communicates the findings clearly, and Act helps stakeholders use the insights. Good analysts also think about primary versus secondary data, structured versus unstructured data, and data ethics. Bias can enter during collection, sampling, or labeling, so always ask whether the data fairly represents the population. In this course, you will practice each stage with the linked question bank.

Spreadsheets are the most common first tool for data analysts. Use cells, ranges, named ranges, and formulas to explore data quickly. Cleaning is a core skill: TRIM removes extra spaces, CLEAN removes nonprintable characters, UPPER standardizes case, and SPLIT separates text into columns. COUNTIF, SUMIF, and AVERAGEIF summarize data by condition. Pivot tables reorganize large datasets so you can compare categories. Conditional formatting highlights outliers and missing values. Data validation prevents bad input by restricting values. XLOOKUP and VLOOKUP join information from different sheets. Before analysis, check data types, remove duplicates, and make sure every column has a clear meaning. Spreadsheets also support simple charts for early exploration, but dashboards and Tableau are better for polished reports.

SQL is the standard language for querying relational databases. Start with SELECT to choose columns and FROM to name the table. WHERE filters rows, ORDER BY sorts results, and LIMIT limits how many rows are returned. DISTINCT removes duplicate values, and GROUP BY groups rows so aggregate functions such as COUNT, SUM, and AVG can be applied to each group. INNER JOIN returns rows that match in both tables, while LEFT JOIN keeps all rows from the left table. A primary key uniquely identifies each row, and a foreign key links tables. CASE adds conditional logic to query results. When you receive a query, first understand the tables and their keys, then filter and aggregate carefully. Run small checks to verify row counts and expected values before trusting the result. SQL skills are essential for working with business databases and for preparing data used in dashboards.

R is a programming language built for statistics and data analysis. Data frames store tabular data, and vectors hold one-dimensional values. The tidyverse makes data work more consistent: dplyr verbs filter(), select(), mutate(), arrange(), summarize(), and group_by() clean and transform data frames. ggplot2 creates plots using aesthetics such as x, y, color, and size, plus geometric layers such as points, bars, and lines. Use read.csv() to load CSV files and write.csv() to export results. Keep data tidy: each variable is a column, each observation is a row, and each value is a cell. Reproducible scripts let you rerun the same analysis when data changes. For polished interactive views, export cleaned data to Tableau or another visualization tool. Practice with the question bank to reinforce the difference between filtering rows, selecting columns, mutating fields, and summarizing groups.

A dashboard turns analysis into a decision tool. Its purpose is to summarize key performance indicators clearly, so choose only the metrics that matter to the audience. Use line charts for trends, bar charts for comparisons, and appropriate charts for each message. Tableau is a powerful tool for this: worksheets build views, dimensions group data, measures aggregate values, calculated fields add formulas, and dashboards combine worksheets with interactive filters. Tooltips provide context on hover, and stories guide users through a narrative. Data storytelling combines evidence, visuals, and a clear message: state the situation, show the pattern, explain why it matters, and recommend a next step. When sharing, consider the audience level, provide context and limitations, and keep the design clean and accessible. The question bank helps you practice choosing charts, building dashboards, and communicating results.