The teacher is great, friendly and so on. The material could be better. I've learned nothing. You should add more practical tasks in the future. I know you can do that(some tests with code practicing for example). Thank you.

I find it to not be helpful in being able to adequately make me feel confident working as a junior analyst.

It was not executed as well as the other courses and included task that could not be performed in excel

The instructor is the best so far, but the course lacks practical exercises.

Boring. Course needs to spend more time practicing using excel and SQL.

Extremely poor directions for using BigQuery. Unable to follow along.

didn't really enjoy or learn something useful

I am a complete novice and this course is too advanced for me to complete with only the lessons and information provided in this course. I am currently looking for another SQL course to take that is better at teaching SQL so that I can come back and finish this course in order to complete the certification.

Additionally, the course provides hands on experience that I cannot complete due to not having access to BigQuerry on Google. The BigQuerry appears to be too expensive for me to bite into. The free trial Google makes available does not allow you to complete all the practical exercises.

I must say it was a game-changer in my data analysis journey. This course is perfect for beginners who want to learn how to clean and prepare data for analysis.

The course content was well-structured and presented simply and understandably. It covered essential topics like identifying data quality issues, handling missing data, and dealing with inconsistencies. I found the step-by-step approach helpful in learning to clean and transform messy data into reliable insights.

The instructors were knowledgeable and explained complex concepts in an easy-to-understand manner. They provided clear instructions and shared useful tips throughout the course, making the learning process enjoyable and engaging.

What I appreciated most about this course was the emphasis on data integrity and accuracy. I learned how to identify and address common data quality issues, ensuring that my analysis is based on reliable and trustworthy data. The techniques and best practices taught in the course will undoubtedly save me time and frustration in my future data analysis projects.

I highly recommend the "Process Data from Dirty to Clean" course to anyone interested in data analysis, especially those starting their journey. The course provides a solid foundation in data cleaning techniques and equips learners with practical skills that can be immediately applied in various industries.

Completing this course as part of the Google Data Analytics Professional Certificate was a rewarding experience. It has equipped me with the necessary skills to confidently handle and process data, ensuring that my analysis is based on accurate and reliable insights.

A big thank you to the instructors and Google for offering this valuable course. It has truly transformed my approach to data analysis and opened up new possibilities for me. I look forward to applying my newfound skills in future projects and continuing my learning in the field of data analytics.

This is one of the most important sections I have completed in the Google Data Analytics Certification to date. This course introduces you processing data, with a heavy emphasis on data cleaning methodology and techniques. It covers techniques using either spreadsheets functions or SQL to be used when working with databases. I found using SQL to query in BigQuery to be very useful and think that I will utilise that particular platform for performing my own queries with data in future. Getting the opportunity to practice some basic SQL queries, in order to clean data, is very important for an analyst as most of his/her time will be spent cleaning data prior to analysis. The inclusion of preparing documentation and real-world examples, provides a professional standpoint to all of the work involved in this course. The course ends by focusing on how to prepare a data analytics-focused resume and shares some best practices - this element of the course I value greatly and really helps one prepare for the world of working as a data analyst. Very well-structured and well-paced course full of useful knowledge and techniques.

I recently completed the course "Process Data from Dirty to Clean" offered by Google, and I must say it exceeded my expectations. The content was comprehensive, providing a clear and systematic approach to handling data cleaning processes. The instructors were knowledgeable and effectively communicated complex concepts, making the learning experience enjoyable. The hands-on exercises and real-world examples significantly enhanced my practical skills in cleaning and transforming data. The course structure allowed for a gradual progression from basic to advanced topics, ensuring a solid understanding of the entire data cleaning workflow. The interactive nature of the course, including quizzes and assignments, helped reinforce key concepts and provided valuable opportunities for application. Moreover, the course's focus on industry-relevant tools and techniques, coupled with Google's credibility, adds a significant advantage to anyone looking to boost their data processing skills. I feel more confident in my ability to tackle messy datasets and apply the learned techniques in real-world scenarios.

Both SQL and R programming share a strong focus on data manipulation and analysis, making them valuable tools for data professionals. One notable similarity is their use of functions to perform operations on datasets. In SQL, functions like `SUM()`, `AVG()`, and `COUNT()` are used to aggregate data, while in R, functions like `sum()`, `mean()`, and `length()` serve similar purposes. Both languages allow for filtering data based on specific conditions, with SQL using the `WHERE` clause and R using functions like `subset()` or logical indexing. Another similarity is the ability to join and merge datasets. SQL utilizes `JOIN` operations (e.g., `INNER JOIN`, `LEFT JOIN`) to combine tables based on common keys. In R, similar results can be achieved using functions like `merge()` from base R or `left_join()`, `inner_join()` from the `dplyr` package. Both SQL and R also emphasize the importance of efficient data handling and optimization to manage large datasets effectively. Understanding these commonalities helps in leveraging the strengths of both languages for comprehensive data analysis workflows.

The overall interactivity and timed tests are a welcome addition. I like the clock placed on the quiz efforts. It forces you to really pay attention to the question, and work through a timebox, which I feel assists in developing management. The one issue I had was how the program runs as for overlaps. Some students get caught in the middle of a course reconfiguration. As a result, we lose the results of the quizzes and assignments completed already. This "catch-up" puts us closer to a breaching deadlines from the point of disruption, and worse - any affect of the grade negatively, is something the student does not need.

Other than that - the Analytics program's instructors are all fantastic! I am encouraged by all of them, and feel so much more empowered with the learned and developed skills for an analytics role! Five stars - hands down!

This course excellently covers the fundamentals of data analysis, providing a robust foundation in SQL, data visualization, and statistical techniques. It is well-structured with a mix of theoretical knowledge and practical applications, making complex concepts accessible to beginners while still offering valuable insights for more experienced learners. The inclusion of real-world projects enhances learning by allowing students to apply skills directly to tangible tasks. The course also emphasizes the importance of data ethics and privacy, preparing students to handle data responsibly in their professional lives. Overall, it's an engaging and informative course that equips students with critical skills for the data-driven world.

The instructor is lovely, with an excellent sense of humor, the right level of enthusiasm, and above all, very professional. She is very expressive, and it helps to keep us interested throughout the course, even when the coding can be a little on the difficult side some times. Many thanks also, to the team behind the scenes in planning out the syllabus, inserting the right examples at the right time, and for the refresher of concepts scattered across different parts of this course to facilitate the learning in a very smooth manner. As someone from the education sector, i must say that this course is extremely well designed and well planned, with very sound pedagogical practices. Great job, and I love every bit of this.

The course starts with statistical measures associated with data integrity including statistical power, hypothesis testing, and margin of error. Also discussed are strategies that can be used to address insufficient data, the difference between clean and dirty data, characteristics of dirty data, demonstrate an understanding of the use of spreadsheets to clean data.

Finally, the course ends with a discussion of some important parameters to keep in mind while working on a Resume, like understanding how previous experience may be added to a resume, discuss how a data analyst job description may be aligned to a particular area of interest.

Thank you Google and Coursera for this amazing course to start with data cleaning.

I'm currently enrolled in a post-graduate program in Computer Science and Data Analytics. This course has been crucial in complementing many of the concepts I'm learning and has strengthened my foundation in the field of data analysis, especially in the crucial area of data integrity—the foundation for meaningful and reliable data analysis, ensuring that insights and decisions are based on solid, dependable information. This certification is essential for anyone looking to refine their data cleaning skills, whether handling small datasets in spreadsheets or managing large datasets in BigQuery using SQL, as well as to gain experience with the real-world tools used in today's data-driven environment.

good to me.

On your resume, describe how you used and applied your experience in a new project, and what skills you used to overcome any problems you encountered. Actually, I was appointed as a safety driver in Hyundai Motor's self-driving car project, but I studied and experienced related technologies and discovered that Korea's technology was lagging behind because of the too engineering parts of the engineers. For now, I want to devote myself to playing a role of creating a reference point for transitioning from a national project to a sophisticated technology, and in the future, to have an educational environment for my future generations.

I absolutely love the diversity in the presenters for each of these courses in the program. The presenters were very knowledgeable but were excellent in breaking down the information in a beginner-friendly manner. There was so much content, but possible to learn and understand. I really loved the interactive lessons that gave the learner the plenty opportunity to practice the new content within each lesson. I really enjoyed this program that helped me to learn what I needed to learn without feeling overwhelmed. It was also extremely helpful that the design of the new information was always presented in a manner that real-world applications.

"Process Data from Dirty to Clean" teaches data cleaning techniques for data analysts and anyone who works with data. The course is designed to help students develop the skills needed to transform messy, incomplete, and inconsistent data into clean, accurate, and reliable data that can be used for analysis and decision-making.

The course covers a wide range of topics, including data quality assessment, data cleaning techniques, data transformation, and data validation. The course uses a hands-on approach, with practical exercises and real-world examples, to help students learn and apply the concepts covered.

Till this course there 4 tutors that explained each of the topic through videos and among them Sally was the tutor that I was able to follow up the most. She ensured that not only she did not explained the concepts to the best of her abilities she also had a pleasant expression which made the sessions more engaging. I never felt monotonous in any of the sessions and I was happy that she covered a good amount of basic SQL practices which made me realize a lot more areas that was confusing to me earlier especially on writing queries and using bigquery. Thank you Sally for your great work.

I have learnt spreadsheets functions and SQL along with Data Integrity and how process of Dirty to clean the data using SQL and Spreadsheets formulas , I'm really enjoyed this course and Some mistakes have done due to not understanding small things like some SQL functions but still made better score by understanding and Reviewing the videos and notes . Kaggle is something is not understand because of confusion of datasets In Big Query . Still a lot to learn in SQL and Spreadsheets

Some questions looked tricky but to want to improve better with regular practice .

Great follow up from the other courses on this certificate, it does have some very basic things but also more advanced highligths and examples that are interesting. I felt it has a good video pace for the subjects covered by each lesson, I liked most of the examples from the readings and quiz activities, good practicing for checkin on the data cleaning process I already do, I will propably work better with some recommendations that are given during the course such as better logging of cleaning steps that are done over the dataset being prepared for processing.

The instructor and her way of explanning the things is super awesome. I will fill honored, if I will get a chance to connect with her over LinkedIn and get guidance especially in data cleanning part of data analytics. I tried to find Sally on LinkedIn, but their are so many with same name. So, if I will get excess to her LinkedIn profile, it will be really helpful for me to connect with her.

So, hope someone who is reading the reviews, will definetly revert via mail. eishuarora498@gmail.com

Thank you so much #Google,#Sally for making my journey easier.

Data Collection: Gather raw data from various sources (databases, CSV files, APIs, etc.). Data Inspection: Understand the structure and content of the dataset. Identify missing values, duplicates, and inconsistencies. Use summary statistics and visualizations to get an overview of the data. Data Cleaning: Handling Missing Data: Remove rows or columns with excessive missing values. Impute missing values using mean, median, mode, or other statistical methods. Removing Duplicates: Identify and remove duplicate records to avoid redundant data.