Google Data Analytics study plan

Define data analytics and distinguish the four types of analysis (descriptive, diagnostic, predictive, prescriptive) using one original example of each

Map the six phases of the data analysis process (Ask, Prepare, Process, Analyze, Share, Act) onto a workflow you could run on a real dataset

Compare structured and unstructured data, listing three concrete examples of each from your own work or study context

Differentiate the roles of data analyst, data scientist, and data engineer in a side-by-side comparison

Convert two vague business questions into SMART, measurable questions with defined metrics and time frames

Identify the stakeholders for a hypothetical project and draft five interviewing questions that surface their real objectives

Distinguish quantitative from qualitative data and justify a data source choice for a stated question

Apply the five-whys technique and a gap analysis to a sample problem statement

Compare primary versus secondary and internal versus external data sources, then justify a selection for a given question

Apply a data-ethics checklist covering consent, privacy, ownership, and bias to a hypothetical dataset

Write eight SQL queries against a public sample database using SELECT, WHERE, ORDER BY, GROUP BY, and JOIN

Evaluate a dataset's structure, metadata, and organization, and list three improvements for reproducibility

Diagnose and correct five common data errors: duplicates, missing values, inconsistent formats, outliers, and typos

Apply spreadsheet functions (TRIM, CLEAN, text splitting, lookups, pivot tables) to standardize a messy dataset

Verify data cleanliness with sort, filter, and count checks before and after each fix

Document every modification so another analyst could reproduce the cleaned file

Organize a dataset using sorting, filtering, and pivot tables to expose patterns relevant to a business question

Write aggregate SQL queries (COUNT, SUM, AVG, GROUP BY, HAVING) that answer three specific questions

Calculate and interpret descriptive statistics (mean, median, mode, range, standard deviation) for a chosen variable

Distinguish correlation from causation and critique three misleading statistical claims

Match five different data stories to appropriate chart types and justify each selection in one sentence

Build a three-chart dashboard in a spreadsheet tool or Tableau Public with a shared, clearly labeled data source

Apply design principles: remove chart junk, label axes and units, use consistent color, and write an action-oriented headline

Structure a five-slide narrative that moves from the original question to a recommendation

Write Python fundamentals from scratch: variables, lists, dictionaries, conditionals, loops, and functions

Load a CSV with pandas and inspect it using head(), info(), shape, and describe()

Clean a DataFrame by handling missing values, converting types, filtering rows, and creating a derived column

Produce a grouped summary table and render at least two charts with pandas or matplotlib

Select a public dataset and execute the full Ask, Prepare, Process, Analyze, Share, Act cycle end to end

Document each phase so a reviewer can reproduce every step from raw data to final chart

Present a five-slide case study that answers your original business question with a data-backed recommendation

Complete a timed full-length practice exam and rework every missed item until you can explain the correct answer