If I'm honest not very useful/new stuff here. All seems a bit basic, a bit too basic for a course like this. All information can be found online for free

The content of the course was not as interesting as the first one. The approach was too theoretical and vague.

There is no option to mark the course complete when I finish auditing it.

not much to learn practicallly like sql tableau only theory based course

I have completed the course, yet I have not been issued a certificate

This is not worthy. Too basic which will not help to attend interview

Unless your common sense is quite bad, I would not recommend this.

Worst instructor and totally boring and time wasting course.

Too conceptual.

I expected something more practical/hands-on.

Extremly basic. Good advice to go for the speed track

Memorizing useless definitions, no new skills learnt

a lot of speeking with little real important staff

бестолковый курс с очевидными вещами

The Spreadsheet part was very basic

The contents are below average.

Only Basics and way to lengthy.

Too easy and superficial

Too basic and repetitive

Very common sense stuff

I recently completed the "Ask Questions to Make Data-Driven Decisions" course on Coursera, and I would like to provide feedback on my experience. Overall, I found the course to be highly informative and practical, offering valuable insights into the world of data-driven decision-making. Here are my thoughts:

1. Comprehensive and Well-Structured Content: The course content was well-organized and covered a wide range of important topics. Starting from the basics of data-driven decision-making, it gradually progressed to more advanced concepts, allowing me to build a strong foundation. The course covered essential aspects such as framing questions, data collection, analysis techniques, and interpretation of results, providing a holistic understanding of the subject.

2. Engaging and Clear Instruction: The instructors did an excellent job of presenting the material in a clear and engaging manner. Their explanations were concise, and they used real-life examples and case studies to illustrate key concepts. This approach helped me connect theory with practical applications, making it easier to grasp and retain the information.

3. Practical Assignments and Quizzes: The course included a variety of assignments and quizzes that reinforced the learning material. These hands-on exercises challenged me to apply the concepts I learned, enabling me to develop practical skills. The feedback provided on my assignments was valuable in guiding my progress and enhancing my understanding of the subject.

4. Emphasis on Critical Thinking: One aspect I particularly appreciated about the course was its emphasis on critical thinking. It taught me how to ask the right questions, identify potential biases, and interpret data objectively. This critical thinking approach has been invaluable in improving my decision-making process, both personally and professionally.

5. Relevance and Applicability: The course content was highly relevant and applicable to real-world scenarios. The practical examples and case studies covered a wide range of industries and contexts, making it easier for learners from diverse backgrounds to relate to the material. I now feel confident in applying the concepts and techniques learned in the course to my own work and decision-making processes.

6. Supportive Learning Environment: The course fostered a supportive learning environment through interactive discussion forums and peer collaboration. The instructors and teaching assistants were responsive to questions and provided helpful guidance whenever needed. Additionally, the community of learners allowed for meaningful discussions and knowledge sharing, enhancing the overall learning experience.

7. Continuous Improvement Opportunities: While the course was highly beneficial, there are a few areas that could be improved. Providing more real-time data analysis examples or interactive exercises could further enhance the learning experience. Additionally, incorporating more advanced data visualization techniques and tools would be valuable for learners seeking to develop their data analysis skills further.

In conclusion, the "Ask Questions to Make Data-Driven Decisions" course on Coursera was an excellent learning experience. The comprehensive content, engaging instruction, practical assignments, and emphasis on critical thinking made it a worthwhile investment of time. I feel equipped with the necessary skills to ask the right questions, analyze data effectively, and make informed decisions. I highly recommend this course to anyone looking to enhance their data-driven decision-making capabilities.

ChatGPT Data analytics is the process of examining and interpreting data to uncover meaningful patterns, insights, and trends. It involves the use of various techniques and tools to extract valuable information from raw data, with the ultimate goal of making informed decisions and predictions. Data analytics is a multidisciplinary field that incorporates elements of statistics, mathematics, computer science, and domain-specific knowledge. There are three main types of data analytics: Descriptive Analytics: Descriptive analytics involves summarizing and interpreting historical data to provide a snapshot of what has happened in the past. This type of analysis helps in understanding trends, patterns, and key performance indicators (KPIs). Visualization tools like charts and graphs are commonly used to present descriptive analytics results. Predictive Analytics: Predictive analytics uses statistical algorithms and machine learning techniques to identify the likelihood of future outcomes based on historical data. It involves building models that can make predictions or forecasts, enabling organizations to anticipate trends and make proactive decisions. Prescriptive Analytics: Prescriptive analytics goes beyond predicting future outcomes; it recommends actions to optimize results. This type of analytics suggests the best course of action to achieve a desired outcome, considering various constraints and objectives. Data analytics is widely applied across industries for purposes such as business intelligence, market research, fraud detection, risk management, healthcare optimization, and more. The increasing volume and complexity of data have led to the development of advanced analytics methods, including big data analytics, machine learning, and artificial intelligence, to extract deeper insights and drive more accurate predictions.

Kursus "Ask Questions to Make Decisions Based on Data" memberikan pengalaman pembelajaran yang sangat memuaskan. Materi kursus dirancang dengan baik, memungkinkan peserta untuk memahami konsep-konsep penting dalam pengambilan keputusan berbasis data secara sistematis. Metode pembelajaran interaktif, seperti studi kasus dan latihan praktis, membantu dalam memperkuat pemahaman konsep yang diajarkan. Instruktur juga memberikan klarifikasi yang tepat saat peserta memiliki pertanyaan atau kebingungan. Salah satu hal yang membuat kursus ini sangat bermanfaat adalah fokusnya pada pengembangan keterampilan bertanya yang efektif. Kemampuan untuk mengajukan pertanyaan yang tepat merupakan kunci dalam mengurai dan menganalisis data dengan baik. Dengan memperoleh keterampilan ini, peserta menjadi lebih mampu mengambil keputusan yang didukung oleh data secara lebih akurat dan efisien. Selain itu, kursus ini memberikan wawasan yang berharga tentang bagaimana memahami kebutuhan audiens dan pemangku kepentingan, yang merupakan aspek penting dalam komunikasi hasil analisis data. Dengan memperhatikan kebutuhan dan ekspektasi pemangku kepentingan, peserta dapat mengarahkan analisis data mereka untuk memberikan nilai tambah yang maksimal bagi organisasi. Secara keseluruhan, kursus "Ask Questions to Make Decisions Based on Data" sangat direkomendasikan bagi siapa saja yang tertarik untuk meningkatkan kemampuan dalam pengambilan keputusan berbasis data. Dengan materi yang berkualitas dan pendekatan pembelajaran yang interaktif, kursus ini memberikan landasan yang kuat bagi peserta untuk berhasil menerapkan konsep-konsep ini dalam konteks pekerjaan mereka.

Here is a comprehensive list of skills in data analytics:

i have been learned through this course

1. Data Analysis

2. Statistical Analysis

3. Data Visualization

4. Programming (Python, R, etc.)

5. SQL

6. Data Cleaning and Preprocessing

7. Machine Learning

8. Predictive Modeling

9. Data Warehousing

10. Data Storytelling

11. Problem-Solving

12. Domain Knowledge

13. Data Mining

14. Data Manipulation

15. Exploratory Data Analysis (EDA)

16. Data Wrangling

17. Data Modeling

18. Data Integration

19. Data Transformation

20. Feature Engineering

21. Time Series Analysis

22. Regression Analysis

23. Classification Techniques

24. Clustering Techniques

25. Natural Language Processing (NLP)

26. Sentiment Analysis

27. Text Mining

28. Big Data Technologies (Hadoop, Spark, etc.)

29. Data Governance

30. Data Ethics and Privacy

31. Data Security

32. Data Quality Assurance

33. Data Interpretation

34. Data Presentation

35. Data-driven Decision Making

36. Experimental Design

37. A/B Testing

38. Data Analytics Tools (Tableau, Power BI, Excel, etc.)

39. Data Extraction

40. Data Validation

41. Data Storage and Retrieval

42. Data Architecture

43. Data Warehouse Design

44. Data Governance

45. Data Migration

46. Data Visualization Tools (Tableau, Power BI, Matplotlib, Seaborn, etc.)

47. Data Storytelling Techniques

48. Data Communication

49. Data-driven Strategy

50. Data-driven Problem Solving

The Ask Questions to Make Data-Driven Decisions course in the Google Analytics Professional Certificate on Coursera is an excellent resource for anyone looking to develop their skills in data analysis. The course is very well structured, with clear and concise explanations of important concepts that are illustrated with relevant examples.

One of the strengths of this course is that it provides learners with practical experience in using Google Analytics to ask questions and make data-driven decisions. The interactive exercises and quizzes allow learners to apply what they've learned and receive immediate feedback, which is very helpful for solidifying understanding.

The course also covers a wide range of topics, including identifying stakeholders, asking effective questions, and visualizing data, making it a comprehensive resource for anyone interested in data analysis. The real-world scenarios provided in the course help to contextualize the content, making it more relevant and engaging.

Overall, I highly recommend this course for anyone interested in learning more about data analysis and using Google Analytics to make data-driven decisions. The course is well-designed, informative, and engaging, and the skills learned will be invaluable in a wide range of professional contexts.

Aku mulai mempelajari analisis data dengan mengikuti berbagai kursus online yang sederhana dan sadar bahwa ini adalah jalur karir yang mau kukejar. Aku mencari lingkungan pembelajaran yang komprehensif, intensif, namun praktis untuk mengembangan kemampuanku, dan akhirnya aku menemukan Coursera.Aku memilih coursera karena ulasannya, dan aku sangat senang dengan keputusanku. Aku sangat menikmati setiap aspek dalam kursus ini: platformnya, tugas-tugasnya, desainnya, flow pelajarannya, maupun dukungannya. Aku masih tidak percaya bahwa ini yang aku dapat dari investasi yang aku keluarkan. Terlewati separuh jalan dan aku masih bersemangat akan setiap bab dan tugas baru. Gaya belajar coursera sangat cocok untukku.Para tutornya sangat ahli dan banyak membantu. Dan yang benar-benar mengesankan adalah cepatnya dukungan platform online. Setiap kali aku merasa terjebak dengan tugas, aku akan mengirim pertanyaan lewat platform, responsnya cukup cepat, meskipun ada perbedaan waktu. Wow. Itu sangat mengesankan dan sangat membantu!Seperti yang aku katakan, aku benar-benar puas dengan kursus ini. Tapi jika aku memujinya terus, aku bakal terdengar seperti Sales Person. Ha ha! Sangat aku rekomendasikan!