Google Data Analytics

Professional Certificate Review

1. The Courses 📚

Now, let’s deep dive into this certificate course structure and topics covered. Since this is a professional certificate it contains multiple courses to teach you all the related skills you need to succeed as Data Analyst.

This is an in-depth certification and there are 8 courses on this including a project which you need to complete at the end of this program to get the certificate.


1. Foundations: Data, Data, Everywhere:
This is a great course for beginners who want to learn about data analytics. The course provides a solid foundation for data analytics by covering essential topics such as data collection, storage, and processing. Additionally, students get to learn about cleaning and processing data using spreadsheets and SQL, which is an important skill for data analysts. The course is divided into three modules:

1. Welcome to Data Analytics: This module provides an overview of the course and introduces students to the basics of data analytics.
2. Data Collection: This module covers the various methods of data collection, including surveys, interviews, and web analytics.
3. Cleaning and Processing Data: This module teaches students how to clean and process data using tools such as spreadsheets and SQL.

The course is self-paced and can be completed in approximately 14 hours. It includes quizzes and hands-on exercises to reinforce learning.



2. Ask Questions to Make Data-Driven Decisions:
This course is a great continuation of the first course in the program, as it builds on the foundation of data analytics and covers important topics such as asking the right questions and analyzing data. The practical exercises and quizzes help reinforce learning, and the ability to work at your own pace makes it a great option for individuals looking to learn data analytics. Overall, the course is highly recommended for anyone looking to expand their data analytics skills and knowledge.

The course is divided into four modules:

1. Welcome to Data-Driven Decision Making: This module introduces students to the course and teaches them the importance of data-driven decision-making.
2. Asking the Right Questions: This module covers the process of asking the right questions to get the information needed to make data-driven decisions.
3. Data Collection: This module teaches students how to collect data using various methods, including surveys and web analytics.
4. From Data to Decisions: This module teaches students how to analyze and interpret data to make informed decisions.

The course is self-paced and can be completed in approximately 13 hours. It includes quizzes and hands-on exercises to reinforce learning.



3. Prepare Data for Exploration:
This course provides an in-depth understanding of the process of preparing data for analysis. The course is divided into four modules, each of which covers a different aspect of data preparation.

The first module, "Welcome to Data Preparation," provides an overview of the course and teaches the importance of data preparation. This module sets the foundation for the rest of the course and highlights the importance of data preparation in the data analytics field.

The second module, "Data Cleaning," covers the process of cleaning data, including identifying and handling missing values, outliers, and duplicates. This module is a crucial part of data preparation, as it ensures that the data being used for analysis is accurate and reliable.

The third module, "Data Wrangling," teaches students how to transform and reshape data to make it more suitable for analysis. Data wrangling involves using a variety of tools and techniques to manipulate data so that it can be used to answer specific questions or address particular issues.

Finally, the fourth module, "Data Validation," covers the process of validating data to ensure that it is accurate and reliable. This module is essential for ensuring that the data being used for analysis is of high quality and can be relied upon to inform decisions.

The course is self-paced and can be completed in approximately 13 hours. It includes quizzes and hands-on exercises to reinforce learning.




4. Process Data from Dirty to Clean:
This course is all about how to use spreadsheets and SQL to clean and organize the data you’ll be analyzing later. Over 23 hours, four quizzes, and a hands-on course challenge exercise, you’ll learn how to clean data from start to finish. The course will walk you through some hands-on examples, so it should be fairly straightforward.

The course is divided into four modules, each of which covers a different aspect of data preparation:

1. Welcome to Data Preparation
2. Data Cleaning
3. Data Wrangling
4. Data Validation



5. Analyze Data to Answer Questions:
We’re halfway through the course at this point. And now, we get to the actual analysis part. Finally, you’ll be using advanced formulas and SQL queries to perform complex data calculations.

This course covers:

- Organizing data
- Formatting and adjusting data
- Aggregating data
- Doing data calculations


The course is self-paced, allowing students to complete it in approximately 14 hours. It includes quizzes and hands-on exercises that reinforce learning and has received high ratings from students, with an average rating of 4.8 out of 5 stars.



6. Share Data Through the Art of Visualization:
Visualization is critical for any data analyst. When you give stakeholders plain numbers, it’s tough for non-experts to grasp the importance. But a nice graph or chart? Now they get it.

This course focuses more on Tableau, which is a data visualization platform. Tableau is a useful skill to have in any case, so don’t skip this course.

You’ll walk through: how to visualize data generally, how to create data visualizations with Tableau specifically, how to use data to tell a story, and how to develop slideshows and presentations.




7. Data Analysis with R Programming:
SQL and Tableau have their place, but R is the first and only “real” coding language you’ll learn in this course. Consider this course an intro to R.

This is by far the most time-intensive course, with an expected 38 hours required to complete it. It’s also the most complex. R is a powerful language, and this course only really scratches the surface. The six graded assignments will prove not that you’re an R master, but that you know enough R to become Google Data Analytics certified.

The first portion of this course alone is 8 hours of intro to programming and data analytics with R. Then you’ll get into programming using R’s IDE, RStudio. Then you’ll learn how to work with data in R. Finally, you’ll cover visualizations, documentation, and reports in R.

It’s geared toward total beginners, so don’t get intimidated! But do expect to take your time as you walk through this course.



8. Google Data Analytics Capstone: Complete a Case Study:
The capstone project involves completing a case study that requires students to analyze a large data set and provide recommendations based on their findings. The project is self-paced, and students are expected to work on it independently. However, there is a discussion forum where students can ask questions and receive guidance from their peers and instructors.

The capstone project is an opportunity for students to showcase their analytical skills, critical thinking, and problem-solving abilities. It is a comprehensive project that tests their understanding of the concepts and tools covered in the previous courses of the program.

The project is divided into several phases, including data collection, cleaning, analysis, and visualization. Students will learn how to identify and address data quality issues, manipulate and analyze data, and use data visualization techniques to communicate their findings effectively.