Google Data Analytics Professional 2026 Course

Real case study on cleaning messy datasets using functions like TRIM, CLEAN, and data validation.

Step-by-step guide to SELECT, WHERE, and ORDER BY clauses for basic data extraction.

Use pivot tables, charts, and summary statistics to uncover patterns in spreadsheet data.

Learn chart types, design best practices, and how to choose visuals for different data stories.

Create interactive dashboards and reports by connecting data sources in Google Data Studio.

Practice GROUP BY, HAVING, and aggregate functions like SUM and AVG for summarized insights.

Problem-solving session on identifying and addressing gaps or anomalies in datasets.

Master INNER JOIN, LEFT JOIN, and other methods to combine related tables for analysis.

Build bar charts, line graphs, and scatter plots with clear labels and annotations.

Craft narratives from data using visuals and summaries to communicate findings clearly.

Use window functions, CTEs, and nested queries for complex data transformations.

Set up R, learn basic syntax, and perform data manipulation with vectors and data frames.

Calculate descriptive statistics, correlations, and distributions using R packages.

Step-by-step guide to filtering, selecting, and mutating data with dplyr functions.

Analyze trends and seasonality in time-based data with R tools and examples.

Understand key probability concepts and apply common distributions to real datasets.

Conduct t-tests and chi-square tests to make data-driven decisions in case studies.

Create multi-layered plots and custom themes using ggplot2 for detailed insights.

Techniques for handling big data in SQL and R, including optimization and sampling.

Apply SQL and R to analyze sales data, identify trends, and make business recommendations.

Set up automated data flows using Google Cloud tools for efficient processing.

Introduction to supervised learning models and how to use them for predictive tasks.

Build and evaluate linear regression models in R for forecasting outcomes.

Use clustering techniques in R to group customers based on behavior and demographics.

Write R scripts to generate scheduled reports and update dashboards automatically.

Session on translating data insights into actionable strategies for organizations.

Extract data from web APIs using R and process it for analysis in projects.

Analyze patient data ethically, using SQL and R to improve healthcare outcomes.

Create interactive plots and geospatial maps for complex data presentations.

Implement checks and validation steps to ensure data accuracy and consistency.

Plan and execute data analytics projects with timelines, resources, and deliverables.

Review key topics, exam format, and time management strategies for test day.

Full-length practice exam with detailed explanations for each question type.

Create a portfolio showcasing projects from this course to impress employers.

Tailor your resume and LinkedIn profile with data skills and certification details.

Find and engage with professional groups, forums, and events for opportunities.

Common interview questions, case studies, and tips for technical and behavioral rounds.

List of books, online courses, and tools to keep skills current after certification.

Follow industry news, research papers, and emerging technologies in data field.

Deep dive into bias, fairness, and accountability in data projects and models.

Complete project from data collection to presentation, applying all learned skills.

Work with others to critique and improve data analysis reports and visualizations.

Configure a local environment with databases and R for independent practice.

Explore advanced paths like Google Cloud Data Engineer or machine learning specializations.