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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.

I liked the course and the many inputs we got from data science professionals. However, out of all the videos one small piece of it was concerning to me. Norman White seemingly doesnt believe that courses such as these have too much value. It was a little subtle but upon answering the question about how someone can become a data scientist, his responce was that "high end" data scientist are PhD's who will get jobs at places like Google etc. He must not be aware that most people taking these courses are not likely PhD candidates. Also, he seemed to struggle a bit in remembering the name "big data univserity", and said something along the lines of what's it called again. Clearly this is an academia guy who probably deep down believe that you need to be in a brick and mortar school working on a big name degree to be of any value. I think this guy should be removed from these videos.

I enrolled in the IBM Data Science specialization and this is the first course. It says no experience necessary and I was skeptical but went ahead and signed up with--you guessed it-- no experience whatsoever! Starting with almost no knowledge on the subject, I learned a lot. Other reviews suggest that this was too basic for many people. Perhaps there could be a way to test out of it for the specialization? I was happy to find that the work was easily doable over the course of several days. The main thing I would change would be the inconsistencies. I got a wrong answer on a quiz which was then a correct answer on the final assignment. Doing peer reviews of others' answers, I found almost everyone answered the same thing incorrectly, not surprising since the reading on that topic had different information than the answer key with which we were asked to grade.

It is a good course in general but content of it is quite off-putting, taking into account that the whole idea of Coursera is to encourage self study for those who did not go to University.

Let me explain.

In quite many instances it is mentioned that, if you want to be a Data Scientist, you should have Phd, and people who have Phd are the top Data Scientists. While I agree that, the Phd gives you in depth knowledge on studied topic, many Phd's I worked with cannot keep up with ones ideas. And left to their own, will provide sophisticated study of completely irrelevant topic, pointing the company in a wrong direction or a rabbit hole. And the authority of expectations, can cause disastrous outcomes to any business which does not allocate competent business person to supervise the research.

I thought the course dragged a bit from somewhere in the 2nd week. It may just be that the pace was slower from that point onwards. I liked the simple way in which the concepts were explained using videos and animations. I didn't understand why so much emphasis was given on the format of a report at the end of this course. Yes, a good report is important, but the amount of space it got in this course seemed disproportionate. After all, (as per this course) a Data Scientist ought to be a good story-teller. Then why give such a structured, almost regimented, format for a report? The course is about 'What is Data Science", not on how a Data Scientist writes a report. Let the person use his/her creativity to prepare a report in whatever format is suitable to 'tell the story effectively."

The course highlighted the definition of data science. I enjoyed the content and feel I can now explain to people what data science is to me. The reason for 4/5 is the lacking of realavent quiz questions. 3 questions on the final exam asked me something regaurding the 21st century or sexy job. I'm here to learn, it seems like the facade of becoming part of the sexiest job outweighed the actual information. For example the five (maybe four?) key elements of data science slipped my mind, luckily I wrote them down but I do remember that a bathroom adds more value to a house. Maybe rewrite these questions and take a few hours to make them realevant. There are thousands of students, I feel like the test just invalidates the badge awarded.

I think the course is in general very well designed but two things bothered me: 1) The content seems to be of 2015 and I think in the fast advancing field of data science at least some updates would be important. E.g. some time is spent talking about projections about the years 2018 and 2020 and for me 2022 or at least 2020 projections about the 2020s would have been more relevant. 2) I think quite a few of the test questions ask about irrelevant facts and instead these questions should focus about data science concepts, methodologies, background etc. Examples for "bad" questions are: "Did BCG or E&Y publish report XY in 2014?" or "Did Larry Page or Serge Brian or XY say that data scientist is a sexy job.

I think that the teacher's answers in the 3rd question in the peer-reviewed assignment isn't entirely correct. First, many peers had an almost correct answer missing 1 component, but missing this 1 component cost them 3 points, which I think is too drastic. Second, it wasn't entirely clear which 10 components are considered to be the main ones from the course reading. The teacher's answer was missing executive summary and literature review. Many students had put these two as main components, which I think they are. I would recommend reviewing the 3rd question as well as the teacher's answer. I would also recommend that the peer reviewing would be done by 2 or 3 students, not 1 to even out bias.

The basic knowledge of Data Science has been introduced which were clear and easy to understand. However, most of the materials are interviews of different data sicence professionals, there were few videos of documented lectures. I think documented lectures are important to present some core concepts in a more academic way. Interviews are sometimes too casual, they are like open discussions, different person could have different feelings or understanding about Data Science. But, as a beginner of Data Science, I need to learn the foundations of Data Science in a more academic way. So I don't think it will be a good idea to use interviews video as the core course material.

It gave a good overview of what Data Science is as a discipline and who Data Scientist should be as a professional. It allows you to understand what Data Science is for a beginner before you consider investing heavily into this field. Please note that although this introductory course is great, rest of the courses in the IBM specialization are not good, especially if you are a beginner (like me) and have no programming and coding experience - you will not learn well because a lot of the content already assumes that you know coding, syntax and complex computer science terms. Read reviews carefully from other courses in this specialization before making any commitment.