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"Data science is what data scientist do." I firmly subscribe to the belief in this definition given by Professor White at NYU Stern. Science is never about human accomplishments, a set of opinions or truth. It is a system of the methodology of exploring the truth, understanding oneself and their respective environment, challenging one's constrained thinking and improving the life of mankind for the better. Data science is within the grand scale of science but add a "sexy" touch to it. (as declared by Harvard Business Review) How so? Before data science, we were obsessed with the idea of finding actual causation and could not settle with a strong correlation; We claimed that no one can predict the outcome of any event despite our instinctive use of different indicative signal in my daily life, We argued that artificial intelligence should be rooted in its "general use" nature and should be based on a strong logical structure. However, after the rise of machine learning (especially deep learning networks) was made possible by the increasing computing power, a new direction of understanding the world with a "specific use" type artificial intelligence have arrived.Data scientist integrates the traditional theory that has either been overlooked due to misunderstandings, such as Probability Theory and regular statistical Regression or tools that have been unused due to lack of technological support, such as machine learning, to formulate an understanding of our behavior, our system and consequently improving the life of mankind for the better. With the identification of statical features, the utilization of machine learning and the employment of analytic with reinforcement of the talents(engineers, developer, financial analyst from the respective discipline, data scientist serve as the hub of hacking complex problems and story-telling of data across the industry.

And this course will give you clear outlook of that.

There are several mismatch between the content and the answer key and even between 2 answer keys

Example1: Week 3: There was a question on what are the must have parts of report. The answer key states 'appendix' as incorrect saying that it is only optional. But the course material reads that the Prof suggests that every report, no matter how small, have an 'abstract'; but in the final assessment question of the 10 main components of a report, the answer key excludes 'abstract', but includes 'appendix'. the 2 answer keys for the same module has conflicts. Please fix these kind of issues. it is very confusing and might affect our grades and also understanding.

Example 2: Week1. Qn: "According to Prof Haidar, what is true about the cloud?". Answer key says that one of the correct answer is: 'One limitation of the cloud is that you are not able to deploy capabilities of advanced machines that do not necessarily have to be your machines.'. This is exactly the opposite of what Prof Haider says in the video. You use cloud so that you can configure advanced capabilities that your machine may not have.

Example 3: Week 3, video 2: Qn:"What are some of the 1st steps companies need to take to get started in data science?". Answer key states that one of the correct answers is: 'Discard any old data that had acquired inorder to start over'. Where as the course content explicitly states that no matter how old, data is always relevant and valid and is to be stored.

There are more conflicts like these.

I recently completed the Data Science course offered by IBM on Coursera, and overall, I found it to be a valuable learning experience, deserving of four stars.

One of the standout features of this course is the effective use of animations as a teaching tool. The visualizations and animations added a dynamic dimension to the learning materials, making complex concepts more accessible and engaging. It greatly enhanced my understanding of the subject matter, and I highly appreciate this approach. The course's conciseness is another positive aspect. It covers a wide range of data science topics without unnecessary fluff or filler. This made it easier to stay focused on the core content and ensured efficient learning. However, there is room for improvement. Some sections of the course felt unnecessarily long, which at times, diluted the otherwise well-structured content. A more streamlined approach with a focus on essential concepts and practical applications would have been more effective.

In summary, the IBM Data Science course on Coursera is a valuable resource for anyone looking to gain a solid foundation in data science. The effective use of animations and concise content delivery are major strengths. Still, addressing the issue of course length in certain sections could further enhance the overall learning experience.

I'm writing this review because it looks like people are too harsh on judging an introductory course. Yes, if you have any experience in data science this introductory course is a bore, but we have to look at it as a statistician. Specifically, the sample set of people who know about data science, maybe who have experience with R or Python or C, do not represent the population total. If we look at the population representing the people who are taking this course, the diversity is outstanding. It would consist of people from different parts of the world, different age groups, different occupations and different levels of experience with data science. Through observation, I am banking that this course is attuned towards a random sample of that population -- ranging from those with comfortable experience to no experience; from high school students to Ph.D. students; from U.S. to India, etc. So if you are new to data science, whatever your age group, whatever your profession, or wherever your location you are in, this is for you to get a macro perspective on what data science is :D. Gate is open, code is open source. :D Viva open source!

BUT, I did put a 4 star since some videos did not match the script. Fix that please <_<.

Content provided by the course is very short & videos are handy. One or two page reading material is a cherry on top of this beautiful cake. Course material provided by Prof. Murtaza Haider is excellent & the only professor I can surely say has a great understanding of the subject & clear mindset of what he is going to talk next. I personally believe that short clips of various people inside course videos can be neglected as everyone is repeating everyone else & they are not actually contributing any knowledge to the course but just making a random & irrelevant comments about Data Science. This course can be more enhanced if we put more content from Prof. Haider & Prof. White & give it a more insightful background.

Overall I enjoyed this course & I have gained a lot of knowledge about Data Science now. I am being guided in the right direction & I believe I now have clear understanding where to move forward & what to learn to achieve a great success in the fields of Data Science.

it's okay I would say, I mean, I think the content is interesting to get an overview of the field, but I think that there are too many videos that you'r kind of forced to watch, even thought you've understood the zest of what data science is. The course is separated on 3 weeks, but really, 1 week is more than enough to complete everything. or even a few days. We spend too much time defining what a data scientist is, I think the course would have been better if we jumped directly to the technical skills needed. I don't think it's efficient to keep talking about data science instead of doing data science. It's like talking about maths without actually doing maths. Anyway, there is still more courses to do to get the professional certificate, so I hope there would be more practical things, but I maintain anyway that this introductory course could have been better if it was just sumed up in one video of let's say 30 min-45min max

Provides a good overview of what Data Science is, what you need to become a data scientist, what employers are looking for and what Data Scientists do. Very solid start and foundations for an introductory course in Data Science. However, the quizzes can sometimes have irrelevant questions that either weren't discussed in the prior lessons or is of niche interest and not really relevant to your understanding of the material, and the biggest problem is the arrangement of the lessons. Week 3 gives you the certificate of completion before you go through week 4, and week 4 has information which is asked about or referenced in prior weeks. I think the correct order should be something like Week 1, 4, 2, 3, but I'm not sure, and it shouldn't be on the students to try and decipher the correct order of the coursework. Still gets 4 stars for the course contents, but deducting the 1 star for the odd ordering of the information.