Not all eight courses in the Google Data Analytics Certificate are created equal. Some are approachable for total beginners. Others stump even people with technical backgrounds. This guide identifies the parts that cause the most difficulty — and gives you specific strategies to push through each one.
Course 5: Analyze Data — The SQL Gauntlet
Course 5 is where the SQL introduced in Courses 3 and 4 gets significantly more complex. JOINs, subqueries, aggregations with HAVING, and multi-table analysis all come together here. Students who only watched the video explanations without writing their own queries hit a wall.
Strategy: Before starting Course 5, spend a few hours on SQLZoo.net completing the SELECT quizzes independently. The repetition of writing queries yourself — not following along — is what builds the skill.
Course 7: R Programming
R is genuinely hard if you've never programmed before. The certificate's R course is introductory, but even the basics of RStudio, packages, and ggplot2 syntax can feel overwhelming. Many students try to memorize syntax instead of understanding the logic.
Strategy: Focus on three things: loading data with read.csv(), basic tidyverse manipulations (filter, select, mutate), and creating a ggplot2 scatter and bar chart. Everything else in the course builds on these foundations. Master those three and the rest becomes much more approachable.
The Capstone: Starting With a Blank Page
The capstone isn't technically hard, but it's open-ended — and open-ended assignments stall many learners. The absence of step-by-step instructions that have guided the first seven courses can be paralyzing.
Strategy: Use the GDACertPrep study guide's capstone template to structure your analysis before you touch any data. Having a skeleton to fill in is significantly less daunting than starting from nothing.