Key Takeaways From Coursera Google Data Analytics Course 3 and 4

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By Maria B

My journey with the Google Data Analytics Certification program for beginners continues, and I’m happy to report that I’ve completed four out of the eight courses. Courses 3 and 4 focused on how to process data for exploration and how to clean the data. Overall, I had a great experience with both courses, and I learned a lot. In this blog post, I’ll share my personal review of the courses, including some interesting learnings that I found beyond the main topics. If you’re a beginner interested in pursuing a career in business analytics, this certification program can be a great asset. So, let’s dive in and explore some key takeaways from these courses.

Google Data Analytics Courses 3 and 4: More Than Just a Course

In the following paragraphs, I will share my key takeaways from completing Google Data Analytics Courses 3 and 4, including creative exercises, the Kaggle community, SQL practice, and valuable career skills.

Google Data Analytics Certification

Exploring Creativity and Data Trends

As I progressed through Google Data Analytics Courses 3 and 4, I learned much more than just how to prepare and process data.

Data formats
Source: Google data analytic course

One of the key takeaways for me was how to explore my creative side. I learned how to observe different trends while looking at the same picture or object, and understanding how they changed over time. This exercise was made even more accessible through the use of tools like Quick Draw, a website that helps you understand this phenomenon better. 

quick draw

Additionally, I discovered that Canva offers a draw feature that can be used in this exercise as well. These exercises helped me to think more creatively and develop a deeper understanding of data exploration, trends and patterns.

data exploration
Source: Google data analytic course

Growing in the Data Science Field with Kaggle

In addition to the creative exercises, Google Data Analytics Courses 3 and 4 also introduced me to the Kaggle community. I was amazed by how people are growing the data science field, and how much one can earn by becoming a data scientist. Kaggle is an excellent platform for anyone who wants to build their skills and knowledge in data science. Through Kaggle, I was able to learn from top data scientists and participate in competitions that helped me to develop my skills. The potential to earn millions by participating in Kaggle challenges is also a motivating factor, as when you grow on Kaggle, you will have a greater chance of getting noticed, getting more work, and earning more money online.

kaggle competitions

Mastering SQL and Real-Life Applications

Another key takeaway from Google Data Analytics Courses 3 and 4 was the opportunity to learn and practice SQL. SQL is a critical skill in data analysis, and the courses offered plenty of opportunities to practice this skill. SQL offers powerful tools for cleaning large data.

types of dirty data
Source: Google data analytic course

I was able to use the website I learned in Course 1 to practice SQL, and I found that the more I practiced, the more confident I became in my ability to write SQL queries. I was able to apply this skill in real-life scenarios, and it made me appreciate the power of SQL in data analysis even more.

practice SQL for free

Preparing for a Career as a Data Analyst

Finally, Google Data Analytics Courses 3 and 4 taught me valuable skills related to my future career as a data analyst. I learned how to create a resume as a data analyst, specifically how to prepare a resume as a junior data analyst, and what transferable and soft skills to mention. The courses also helped me understand what important skills are required to become a master data analyst. During the courses, I found Canva to be a useful tool for creating professional-looking resumes that stand out. Overall, the course provided me with important guidance and tips that will be useful as I progress in my career as a data analyst.

List of Soft skills
List of Soft Skills Source: Google data analytic course

Having completed half of the Google Data Analytics Certification program, I am eager to move forward with courses that align with my interests. Specifically, I am excited to explore topics such as data analysis and data visualization. These courses offer valuable insights into how to effectively analyze and communicate data, which are critical skills in the field of data analytics.

data verification
Source: Google data analytic course

I look forward to continuing my studies and gaining a deeper understanding of these important concepts.

coursera google data analytics course 3 and 4

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