Google Data Analytics
Professional Certificate Review

APRIL 4, 2023


The Google Data Analytics Professional Certificate is a highly sought-after MOOC offered through Coursera. With over 800,000 enrollments on Coursera, this is one of the most popular Data Analytics courses on the platform. And why not? It is created by none other than Google itself. The course is well-structured, up-to-date, and covers all essential Data Analytics skills taught by Google experts.

The certificate program comprises six courses expertly designed to impart the necessary skills for a career in data analytics. The courses cover a wide range of topics, including data collection, analysis, visualization, and presentation. The program also includes practical project work that enables students to apply what they have learned in real-world situations. The six-month-long certificate program is priced at an affordable $39 per month. The program is self-paced, designed by Google experts, and offers hands-on experience, making it a highly desirable and valuable program. Although the program may not be comprehensive enough for advanced students, and does not provide direct job placement, it is still a great choice for individuals looking to learn data analytics skills and start a career in the field.

In this post, we will conduct a comprehensive review of The Google Data Analytics Professional Certificate. At the end of the post, we will even discuss the ever-growing concern: Is this program worth it? 

1. The Courses đź“š

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.

2. Who should (and shouldn't) take this course

This certificate is a great introductory course to Data Analytics. We’ve had several Moocable users recommend this program. If you are a non-CS / non-IT professional, this is a really good fit for you. The certificate takes you from having 0 knowledge about Data Analytics, to acquiring sufficient knowledge and experience to get your first job. Having said that, it’s also important to note that this certificate is not good for every learner.

Who should take this course:

• You are completely new to Data Analytics: Then this is the right program for you. The Google Data Analytics certificate is beginner friendly, and assumes that you have no prior knowledge of Data Analytics.

• If you are looking for a flexible & affordable option: If you are looking for a structured program that is really flexible and affordable, then you should enroll in this program. There aren’t many alternatives for learning Data Analytics for the same pricing. Since it’s a self-paced course, learners have complete freedom in studying according to their convenience.

• Not sure if Data Analytics is for you, but want to give it a shot: If you are considering getting into Data Analytics, but aren’t 100% certain, then you try this program. Again, given the format & pricing, you can conveniently learn about this domain risk-free.

Who should not take this course:

• If you are already working in Data Analytics domain: You are not going to find much value from this program, if you already working in the analytics domain.

• Looking for advanced Data Analytics: As mentioned, this program is well suited for beginners. Most of the curriculum and projects are beginner-to-intermediate level. If you are looking for advanced topics, you should consider some other course.

• Prefer non-video learning style: This program is video-focused. Meaning, most of the content is taught via pre-recorded videos. We’ve had students on Moocable who did not find the format engaging. If you prefer text-based course, then you might consider other programs.

3. Prior Requirements

The great thing about Google Professional Certificates is that no degree or experience is required to study them. All you need is high-school level math and a curiosity about how things work.

4. Pros

1. Free & Accessible
2. Well structured
3. Suited for beginners
4. Prepares you for a job

5. Cons

1. R Bias
2. Getting a job
3. Not for advanced learners

6. So, is it worth it?

Absolutely! Given the format, quality, structure, and pricing, we strongly recommend Google Data Analytics Professional Certificate.

We had a Moocable member, few months ago, who enrolled in this program. She had graduated with a Bachelor’s degree in Commerce, and wanted to get into Data Analytics. After 6-7 months of self study, she got her first job as a Jr. Data Analyst.

Regardless of your background and goal, this program is totally worth it.

7. Alternatives

1. Applied Data Science with Python: If you are looking for an Analytics course using Python, then check this program out. This program is more coding focused (but still suitable for non-CS folks).

2. Excel to MySQL: Analytic Techniques for Business Specialization: If you are leaning more towards business development, then this program is better suited for you.

3. R Programming Specialization: This Specialization covers the concepts and tools you'll need throughout the entire data science pipeline. Check it out if you want to learn Data Science in more details