During the entire course, I kept getting error messages when I was trying to run the code chunks in the exercise files, and one of the last videos was messed up. Also, the course was not well organized. I would not recommend this course.

Not Best, I really go through youtube videos for all the coding parts but reading and activity are very helpful.

To learn a new language, much more practice and less videos are necessary.

ขอเขียนเป็นภาษาไทยนะ เพราะเชื่อว่าองค์กรณ์สุดล้ำค่าของโลกอย่าง Google น่าจะแปลภาษาไทยได้ไม่ยาก คอร์สนี้มันไม่ไหวอะ เหมือนจะปูพื้นแต่เวลาออกข้อสอบยากไป ไม่มีตัวอย่างอะไรเลย ละคอร์สมันจ่อมมาก ละทีร่สำคัญ มีตั้ง5week กว่าจะปั่นให้จบ รู้ใช่ไหมว่าทาง Cousera เขาให้เวลาแค่เดือนเดียว ถ้าไม่ทันต้่ิองจ่ายเพิ่ม1200 บาท มันไม่เหมาะกับคนที่ด้อยโอกาสทางการศึกษาอย่างฉันเลยปัดโถ่ถัง

Overall good, though was a little buggy. 2-3 videos didn't have a transcript. Also, the hands-on assignments were a big buggy, but that may be more of an issue caused somehow with the addition of the Coursera coach. Extra buggy, didn't give my my certificate, just the Congratulations on completing your course

My last straw was when I found the correct function for the assignment in the NEXT video, not in the one that precedes it. How did you expect me to pass it when your theory is out of order?

Moreover, this course is unnecessary for a JUNIOR Data Analyst.

This was very poorly instructed. I don’t know how to write code in R. It was never actually explained. She quickly typed her code and said “run it… there you go.” And, actually writing the code were the questions I got wrong on the course challenge.

why don't you teach python, it's much more popular, this course make me very dispapointed as I've encountered tremendous amount of difficulties seeking help online

Please don't waste other people's time on this course, you hurt me a lot

Code exeuction on weekly or course challenge don't work and the question are just incoherent.

i had to use R studio download everything.

Topics are not logically orgnizated. Subjects are been repeated over and over.

I want to unenroll this part of my google data analytics course for now.

Took enroll option by mistake, there is no option of unenrolling coming out

Only rate course is there, please help me resolve in this issue.

Teaching the R language has been really poor. For someone new to the language, the tutor just skips through many concepts , which makes things pretty confusing.

This course does not teach data analytics (how to add value with data analysis). It teaches tools used in data analytics.

exams with coding gets an error message everytime. waste of time

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Course challenge

Graded Quiz. • 1h 5m. • 13 total points available.13 total points

DueSep 11, 11:59 PM PDT00:59:42 remainingTime remaining: 59 minutes and 42 seconds

1.

Question 1

Scenario 1, questions 1-7

As part of the data science team at Gourmet Analytics, you use data analytics to advise companies in the food industry. You clean, organize, and visualize data to arrive at insights that will benefit your clients. As a member of a collaborative team, sharing your analysis with others is an important part of your job.

Your current client is Chocolate and Tea, an up-and-coming chain of cafes.

The eatery combines an extensive menu of fine teas with chocolate bars from around the world. Their diverse selection includes everything from plantain milk chocolate, to tangerine white chocolate, to dark chocolate with pistachio and fig. The encyclopedic list of chocolate bars is the basis of Chocolate and Tea’s brand appeal. Chocolate bar sales are the main driver of revenue.

Chocolate and Tea aims to serve chocolate bars that are highly rated by professional critics. They also continually adjust the menu to make sure it reflects the global diversity of chocolate production. The management team regularly updates the chocolate bar list in order to align with the latest ratings and to ensure that the list contains bars from a variety of countries.

They’ve asked you to collect and analyze data on the latest chocolate ratings. In particular, they’d like to know which countries produce the highest-rated bars of super dark chocolate (a high percentage of cocoa). This data will help them create their next chocolate bar menu.

Your team has received a dataset that features the latest ratings for thousands of chocolates from around the world. Click here to access the dataset. Given the data and the nature of the work you will do for your client, your team agrees to use R for this project.

Your supervisor asks you to write a short summary of the benefits of using R for the project. Which of the following benefits would you include in your summary? Select all that apply.

1 point

2.

Question 2

Scenario 1, continued

Before you begin working with your data, you need to import it and save it as a data frame. To get started, you open your RStudio workspace and load all the necessary libraries and packages. You upload a .csv file containing the data to RStudio and store it in a project folder named flavors_of_cacao.csv.

You use the read_csv() function to import the data from the .csv file. Assume that the name of the data frame is flavors_df and the .csv file is in the working directory. What code chunk lets you create the data frame?

1 point

3.

Question 3

Scenario 1, continued

Now that you’ve created a data frame, you want to find out more about how the data is organized. The data frame has hundreds of rows and lots of columns.

Assume the name of your data frame is flavors_df. What code chunk lets you review the column names in the data frame?

1 point

4.

Question 4

Scenario 1, continued

Next, you begin to clean your data. When you check out the column headings in your data frame you notice that the first column is named Company...Maker.if.known. (Note: The period after known is part of the variable name.) For the sake of clarity and consistency, you decide to rename this column Maker (without a period at the end).

Assume the first part of your code chunk is:

flavors_df %>%

What code chunk do you add to change the column name?

1 point

5.

Question 5

After previewing and cleaning your data, you determine what variables are most relevant to your analysis. Your main focus is on Rating, Cocoa.Percent, and Company. You decide to use the select() function to create a new data frame with only these three variables.

Assume the first part of your code is: 

trimmed_flavors_df <- flavors_df %>%

Add the code chunk that lets you select the three variables.

1 RunReset

What company appears in row 1 of your tibble?

1 point

6.

Question 6

Next, you select the basic statistics that can help your team better understand the ratings system in your data. 

Assume the first part of your code is:

trimmed_flavors_df %>%

You want to use the summarize() and sd() functions to find the standard deviation of the rating for your data. Add the code chunk that lets you find the standard deviation for the variable Rating.

1 RunReset

What is the standard deviation of the rating?

1 point

7.

Question 7

After completing your analysis of the rating system, you determine that any rating

Title: Excellent Google Data Analytics Course on Coursera!

Rating: ⭐⭐⭐⭐⭐ (5/5)

I recently completed the Google Data Analytics course on Coursera, and I must say it was an outstanding experience. This course not only met my expectations but exceeded them in every aspect.

Content: ⭐⭐⭐⭐⭐

The course content is comprehensive, well-structured, and incredibly relevant. Each module was designed with clarity, providing a solid foundation before diving into more advanced topics. The real-world examples and case studies helped me understand how data analytics is applied in various industries.

Instructors: ⭐⭐⭐⭐⭐

The instructors were top-notch. They were not only knowledgeable but also had a knack for explaining complex concepts in a simple and engaging manner. Their enthusiasm and expertise kept me motivated throughout the course.

Hands-on Experience: ⭐⭐⭐⭐⭐

The hands-on assignments and projects were the highlight of the course. They allowed me to apply the concepts I learned to real datasets, giving me a taste of what it's like to work on actual data analysis projects. The step-by-step guidance and feedback provided a supportive learning environment.

Resources and Support: ⭐⭐⭐⭐⭐

The course materials, including readings, videos, and supplementary resources, were abundant and well-curated. The discussion forums were incredibly helpful, and the community aspect added a collaborative element to the learning experience. The support from both the instructors and fellow learners made problem-solving much easier.

Flexibility: ⭐⭐⭐⭐⭐

As a Coursera course, the flexibility it offered was invaluable. I could learn at my own pace, which was perfect for balancing with my other commitments. The modular structure also allowed me to revisit specific sections when needed.

Certification: ⭐⭐⭐⭐⭐

The course completion certificate holds genuine value. It serves as a testament to the skills I've acquired and has already proven to be a valuable addition to my professional profile.

Overall, I can confidently say that the Google Data Analytics course on Coursera is a must-take for anyone interested in entering the field of data analytics or looking to enhance their existing skills. The depth of knowledge, quality of instruction, and practical experience provided are truly exceptional. I'm grateful for this learning opportunity and can't wait to explore more courses in this domain.

I recently completed the "Data Analysis with R Programming" course, and I must say it exceeded all my expectations. From start to finish, the course provided a comprehensive and engaging learning experience that has truly enriched my skillset in data analysis. One of the aspects I appreciated the most about this course is its structured approach to teaching R programming. The instructors did an excellent job of breaking down complex concepts into manageable chunks, making it easy for beginners like me to grasp the fundamentals. The hands-on activities and quizzes were particularly helpful in reinforcing my understanding of key concepts and techniques. Moreover, the course content was highly relevant and up-to-date with industry standards. The practical examples and real-world case studies provided valuable insights into how R programming can be applied to solve a variety of data analysis challenges. I especially enjoyed working on the module challenges and the final project, where I had the opportunity to apply my newly acquired skills to real datasets. Another highlight of the course was the instructor support and the vibrant online community. The instructors were responsive to questions and provided timely feedback, which greatly enhanced the learning experience. Additionally, the peer interaction and discussion forums facilitated collaborative learning and allowed me to exchange ideas with fellow learners from diverse backgrounds. In conclusion, I wholeheartedly recommend the "Data Analysis with R Programming" course to anyone looking to enhance their data analysis skills. Whether you're a beginner or an experienced professional, this course offers valuable insights and practical knowledge that will undoubtedly benefit your career advancement in the field of data analysis. Kudos to the instructors and the entire team behind this exceptional course!

The course is designed to introduce learners to the basics of R programming language and its application in data analytics. The course covers various topics such as data cleaning, data visualization, data manipulation, and data analysis. The course provides a mix of video lectures, interactive quizzes, and hands-on exercises that help learners to understand the concepts better.

The course starts with an introduction to R programming language, and gradually progresses to more advanced topics such as data visualization using ggplot2, data manipulation using dplyr, and statistical analysis using inferential statistics. The course also includes several case studies and examples that help learners to apply the concepts in real-world scenarios.

One of the main advantages of this course is that it is offered by Google, which is a reputable organization in the field of technology. The course is designed to be self-paced, which means learners can complete the course at their own pace and convenience. The course also includes a dedicated discussion forum where learners can ask questions and get help from the community.

Overall, the Data Analytics in R Language course by Google is a well-structured and comprehensive course that provides learners with a solid foundation in R programming and its application in data analytics. The course is suitable for beginners as well as experienced professionals who want to enhance their skills in data analytics using R.

This must be one of the most challenging courses I've taken in this program! Not only was I a total newbie to programming languages, but I also had to rethink the way I look at data analytics as well. I always thought that data analysis only involved processing, cleaning, and analyzing the data, it never occurred to me that programming languages are involved as well. Even though it was really challenging, I really had fun learning to code, especially when it already got to coding my data viz. I realized that there are completely different tools analysts can use to achieve the same results. I am now more open and excited to learn other tools for analysis well beyond this program. The way this course was structured really helped a lot! It helped me ease into the new language, get to know it, and practice it with the activities. The hands-on activities are what I appreciate the most, not only did they guide me to what I needed to accomplish for that activity, but they also allowed me to use different functions and find out techniques I could use to make the code or my analysis more efficient. Thank you so much for your hard work!

The main female instructor was fantastic. The tools taught outstanding. However, there are two parts that need a review. The first is the idea that hiring managers prefer candidates who challenge the corporate status quo with new ideas. That may be soley with Google as I have yet to meet one in the general private sector who did. Most intereviewers are solely interested in what the candidate can produce for the firm at the time. New ideas are not part of the picture until the ones immediately pressing on the firm are resolved. The second issue was with your 'bias' button as it relates to statistical analysis. That error becamse the source of my capstone project. You have confused bias for sampling error. A store manager misjudging which film will sell well and over-ordering the wrong tapes is not a bias error but a sampling error. It is the equivalent of saying that your car has a bias for steering left due to an underinflated tire. That is a lean, not a bias.

That said. my capstone project describes real bias that has been going on in the country for decades.

This course looks at how to use programming in R to perform all of the stages in the data analysis process, from preparing and processing data, to performing preliminary and advanced analysis, to finally sharing the findings with your audience. The steps are very clearly shown with the trainer guiding you at the right pace. There are plenty of opportunities to follow along as instructed and understand the techniques by doing them - this is a great way to learn how to code. Various resources are linked to at key stages allowing for further reading on core concepts and even keeping 'cheatsheets' on key R packages for future reference. The priniciple of ongoing learning is conveyed perfectly with the instructor acknowledging her own meandering journey as an analyst and as a coder. Finally, the use of R markdown to display the steps you've used in your analysis and to easily produce a final report or presentation is extremely valuable for me. I will definitely be going through this course several times as I practice using R now and in the future. Excellent course 10/10

Excellent base knowledge for R programming aimed at data analysis delivered in an easy to follow manner and at a reasonable cost. The course not only gives the student a good start but also provides lots of accessible resources and tips to move from a novice R programmer towards mastery. Remarkably, everything worked, I write this because I have attempted to follow instructions for such things as downloading programming ide's in other online courses and found them outdated, not so here. Everything is current and up to date. I think any student with average grades in the past can work through this course, perhaps repeating sections at times, and find themselves fairly R proficient in a timely manner. Personally with two hours effort every morning while my pre-schooler was still asleep I was confidently coding in less than a month (while using an old outdated Chromebook I bought for $100, not necessary to invest thousands in a new computer).

It is not perfect, but nonetheless is still very good! In fact, this is the best course I have taken for learning (a lot of) R ever. So that's a great bonus. After the analysis and visualization courses, I was about to give up on the Google Data Analytics series because of the bad quality of those two courses. But yeah, this R course is good. The coach understands how to captivate the audience, and the course is well-paced and very informative. I learned so much from this course. In fact, here is where I actually learned the analysis and visualization steps that were supposed to be taught in the previous two courses. My recommendation is to just skim over the analysis and visualization courses, just to get a broad idea of what they offer. But really use this R course to learn analysis and visualization. Because this course actually explains the why of it all too, along with the how. So it is very effective.

I highly recommend Google's Data Analysis with R Programming course as an introduction to R. The course quickly gets one up and running quickly. I was able to install R and R Studio/Posit, learn how to navigate R Studio, install and load packages, and use R to clean, analyze, visualize, and share data analyses. I think this course would be great for an aspiring analyst with little to no experience or someone like me who is an experienced analyst wanting to learn R. You will get out of the course what you put into it. It's possible to get a rudimentary understanding of R by quickly completing only the required elements, but one thing that I appreciated is that the course points at a number of resources for further inquiry and study. You won't leave this 5 week course an expert, but there is plenty here to launch one successfully. I'm impressed with how much I learned over the 5 weeks.

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This is the first time I followed a course from google & coursera. This is a most valueble course for the persons who wish to enter the data analyst career. The course content is much clear and the lecturer also explains each theorytical and practical parts clearly in a way that any one including the persons who don't have much listening skills, can understand.It is beacause the pronounciation is that much clear.

On the other hand, the quizes and lessons have been arranged in a proper sequence. Each reading supports with clear theories and relevant codes as well as other useful website links. There is a seperate glossary for each part as well. Course covers entire process of the data analysis with practical examples and more.

Finally course is great and thank you very much for the google and coursera team.

Kelum (Sri Lanka)