I share in this article my experience related to completing the Google Data Analytics Professional Certificate on Coursera. Learn about the skills I acquired, the challenges I faced, and how this program can prepare you for a career in data analytics.
- What this professional certificate consists of
- What I learned
- How the learning process took place
- Conclusions, my experience
- The entire route was taken to obtain certification:
- Course 1: Foundations: Data, Data, Everywhere
- Course 2: Ask Questions to Make Data-Driven Decisions.
- Course 3: Prepare Data for Exploration
- Course 4: Process Data from Dirty to Clean
- Course 5: Analyze Data to Answer Questions
- Course 6: Share Data Through the Art of Visualization.
- Course 7: Data Analysis with R Programming
- Course 8: Google Data Analytics Capstone: Complete a Case Study
I recently completed the Google Data Analytics Professional Certificate on Coursera, and it was an eye-opening experience. In just a few months, I acquired essential skills, completed practical projects and obtained professional certification. This is a summary of the course of study faced in attaining a speciality in high demand in the job market as a data analyst.
What this professional certificate consists of
The Google Data Analytics Professional Certificate on Coursera is a comprehensive curriculum that can prepare anyone for a career in data analytics, even without experience or a degree. With eight courses, this program covers all aspects of data analysis, from collecting and cleaning data to creating visualizations, which can lead to decisions crucial to business success. The lecturers are Google employees with decades of experience in data analysis. This ensures quality learning with the help of the best professionals in the field.
What I learned
This program has enabled me to thoroughly understand the methodologies and processes used in the daily work of a junior or intermediate-level data analyst. I learned critical analytical skills, such as data cleaning, analysis and visualization, and the tools needed for optimal results, including spreadsheets, SQL, R programming and Tableau. I learned the best methodologies on how to clean and organize and complete data for analysis and the most critical computational functions to use in spreadsheets, SQL, and R programming; in addition, I learned how to visualize and present data results in dashboards, using the most commonly used visualization platforms.
How the learning process took place
The program includes more than 180 hours of lectures and hundreds of practice-based assessments, which helped me simulate real-world data analysis scenarios essential to perform profitably in the future workplace. The content is highly interactive and developed by Google employees with decades of experience in data analysis. Through videos, assessments, and hands-on workshops, I was introduced to analytical tools, platforms, and critical analytical skills needed for successful work. Skills acquired include data cleaning, problem-solving, critical thinking, ethics, and visualization. The platforms and tools I learned include spreadsheets, SQL, Tableau, and R programming.
The “Capstone Project” is the final course in the program, in which I had to complete a case study to share with potential employers to showcase my new skills. The capstone project was particularly challenging and required applying all the skills learned throughout the curriculum. I used actual data to complete a case study and presented the results in a concluding report.
Conclusions, my experience
Completing this program was not easy, but it was rewarding. Each course had its challenges, with the final act devoted to the “Capstone Project” particularly challenging, given that I had not previously used the R programming language. In particular, I was required to apply all the skills I had learned in previous courses and to work with an extensive data set to answer a series of questions. It was challenging, but completing it gave me great satisfaction and renewed confidence in my work abilities. Throughout the program, I learned skills in high demand in the job market, such as data cleaning, analysis and visualization. I also delved, through BigQuery, into the use of SQL, a programming language widely used in data analysis. These skills have opened up new career opportunities for me.
To view my final paper(Capstone Case Study), you can refer to thededicated Blog article or the following links on GitHub and Kaggle:
This is the certification obtained at the end of the course:
- Course content: lectures’ quality, the topic’s relevance, and how well the course met your initial expectations.
- Clarity of instruction: how easy it was to understand the course material, whether the instructions were clear, and whether the teacher provided sufficient explanations.
- Practical applicability: if the skills acquired in the course will be helpful in my career or practical applications.
- Support and resources: additional learning resources, such as supplementary readings and support offered by tutors or fellow students.
- Difficulty level: the complexity of the course and how challenging it was for me to complete it; more stars indicate a more challenging course.
- Overall value: overall judgment of the quality of the course, considering both the cost and what I gained in terms of new skills acquired.
The entire route was taken to obtain certification:
Course 1: Foundations: Data, Data, Everywhere
In this introductory phase, I learned the basics of data analysis through a very hands-on and engaging learning program. I gained an understanding of the practices and processes used by data analysts and critical analytical skills and tools. The course also covers a range of terms and concepts relevant to the role of a junior data analyst and explores the role of analytics in the data ecosystem.
Course 2: Ask Questions to Make Data-Driven Decisions.
In this course, we focus on how to ask practical questions to make data-driven decisions. I gained an understanding of data-driven decision-making, effective query techniques, and real-world business scenarios. The course also covers how and why spreadsheets are essential for data analysts, structured thinking and stakeholder management.
Course 3: Prepare Data for Exploration
In this course, I was introduced to new topics to gain practical skills in data analysis. I learned to use spreadsheets and SQL to extract and use the correct data for their purposes and to organize and secure it. The course also covers how analysts decide what data to collect, structured and unstructured data, data types and formats, data validity, open data, data ethics, and privacy.
Course 4: Process Data from Dirty to Clean
This course taught me techniques for verifying and cleaning data using spreadsheets and SQL, as well as how to verify the results of data cleaning and report on them. Topics covered include data integrity, data cleaning techniques with spreadsheets, basic SQL queries and functions, and data cleaning reports.
Course 5: Analyze Data to Answer Questions
Based on the first four courses, we focus on the “analysis” phase of the data analysis process. I learned how to organize and format data using spreadsheets and SQL, aggregate data, and perform complex calculations on data. The course also covers using SQL formulas, functions, and queries to perform computational operations on data.
Course 6: Share Data Through the Art of Visualization.
In this course, I learned how to visualize and present data results using visualizations such as dashboards. Tableau, a data visualization platform for creating compelling visualizations for presentations, is used as the BI tool. The course also addresses the principles and practices needed to make effective presentations and how to consider potential limitations associated with data in presentations.
Course 7: Data Analysis with R Programming
This course introduced me to the R programming language and how it can clean, organize, analyze, visualize, and report data in new and better-performing ways. Topics covered include the benefits of using R, RStudio, programming in R, R packages (including the Tidyverse package), data frames, generating visualizations in R, and documenting R programming using R Markdown.
Course 8: Google Data Analytics Capstone: Complete a Case Study
In the concluding course, I had the opportunity to complete a case study to use in one’s portfolio to put oneself forward in the job market in data analysis. For the development of the Case Study, one is required to apply the methodology learned in the previous seven modules, thus with the selection of a scenario based on the analysis, drafting a set of questions, preparing, processing, analyzing, visualizing, and taking action performed on the scenario data used (Ask/Ask, Prepare/Prepare, Process/Process, Analyze/Analyze, Share/Share, Act/Act). The course also covers other helpful job search skills, such as actual job interviews and common questions, building an online portfolio, and more.
If you are interested in learning more about this topic or have specific questions, please get in touch with me using the references on my contact page. I will happily answer your questions and provide more information about my work as a Data Analyst. Thank you for visiting my site and for your interest in my work.