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Learner Reviews & Feedback for What is Data Science? by IBM

4.7
stars
69,086 ratings

About the Course

Do you want to know why data science has been labeled the sexiest profession of the 21st century? After taking this course, you will be able to answer this question, understand what data science is and what data scientists do, and learn about career paths in the field. The art of uncovering insights and trends in data has been around since ancient times. The ancient Egyptians used census data to increase efficiency in tax collection and accurately predicted the Nile River's flooding every year. Since then, people have continued to use data to derive insights and predict outcomes. Recently, they have carved out a unique and distinct field for the work they do. This field is data science. In today's world, we use Data Science to find patterns in data and make meaningful, data-driven conclusions and predictions. This course is for everyone and teaches concepts like how data scientists use machine learning and deep learning and how companies apply data science in business. You will meet several data scientists, who will share their insights and experiences in data science. By taking this introductory course, you will begin your journey into this thriving field....

Top reviews

RS

May 11, 2020

Very learning experience, I am a beginner in DS, but the instructors in this course simplified the contents that made me I could easily understand, tools and materials were very helpful to start with.

BR

Feb 21, 2019

Excellent quality content! It's a great introductory course that really gets you interested in Data Science. I would highly recommend it to anyone curious in learning about what Data Science is about.

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51 - 75 of 10,000 Reviews for What is Data Science?

By Joel L

Oct 22, 2018

Didn't really learn anything but I guess it was a good gatekeeper.

By Preston K

Oct 1, 2018

Utter waste of time

By Andrew F

Jan 3, 2019

Great introduction to Data Science!

By Surawut P

May 10, 2022

The content is good and easy to follow.

What I hate about this course the most is all test, quiz and examimation.

Most of their questions are not fair. They require to recite inconsequencial minor detail, such as who or which book said what.

I expect the test to recall about main concept, such as "What is different between AI, ML, and deep learning?", "What is properties of big data?", "what is application of regression". These kind of questions recall things much more important than minor detail I mention above, but they are non existent.

This happen possibly because the questions emphasized too much on module articles, which is full with detail, rather than clip videos, which present important concepts.

I hope you to revise examination questions to be more appropriate. I feel frustrate when doing them because asking minor detail feel like you are cheating upon students.

By Vincent Z

Jan 7, 2019

This is really an introductory course, and there is not much to be learned, not a single line of programming or a single chart generated. But it can all be done in a single day, so it is a necessary evil to reach the good stuff in the specialization, I guess.

By Georgi K

Aug 21, 2020

[Reviewing the entire IBM Data Science specialization but points are applicable for each course]

I signed up for the IBM Data Science specialization and I was genuinely excited to start it for some 4-5 weeks (I had a GCP exam coming up). I eventually started the specialization beginning of August `20 and started making my way though it and I was amazed … amazed of how much a pile of bullshit this specialization is. I made it though the first 4 courses and at the end of the SQL for data science I couldn’t take it anymore. Here’s why:

1. First and foremost, the entire specialization (all 4 courses I have taken at least) were full of typos and broken URLs which a lot of other students confirm as well. This does not speak professionalism to me but whatever, lets move on.

2. The in-video quizzes and following tests are simply ridiculous … you are expected to have memorized content word by word rather than understand thing for your own and be able to explain them. Some of the question were so far away from tech courses it is not even funny.

3. The final assignments are a total joke. We are asked to review each other which IMHO is a terrible idea since we are all just starting up. Nothing stops you from giving top marks to a bad assignments and vice versa.

4. We eventually got to the more techy part and even got code snippets and jupyter notebooks to look through but they were still bad. There was no proper order in which information was presented i.e. you would read python and seaborn code in the SQL course’s tasks even though python and matplotlib/seaborn are discussed in the following courses.

5. And my final and biggest problem with this whole specialization is that it all feel like an extended advertisement of this piece-of-dodo tech inbred excuse-of-a-software called IBM cloud. There are constants up-sells here and there how almighty IBM is and how great their cloud and IBM Watson Studio are … they are not. I had to spend 2+ hours fixing problems with jupyter notebooks and their cloud just to complete my assignments which both took me 30ish minutes. They mention open source and even though there are open source equivalents to jupyter they insist using IBM cloud. I kept having the feeling they are more focused on promoting IBM products than actually bringing quality content.

6. Now after finishing the SQL course there was a 1min survey which I gladly filled in basically letting them know their specialization if terrible and is doing more harm than good in my opinion. I even sent them a quick challenge because I do not think IBM maintains this course at all or even reads the reviews. You can see my challenge to IBM here: https://bit.ly/3geOyfb

I was very saddened by the quality of the specialization and the content and was wondering whether I should even try and finish the remaining courses but after reading some reviews on the remaining courses I figured out it was just more of the same. If you are in the same boat I would recommend the kaggle micro-courses which I will focus on starting next week.

In conclusion, I got this whole specialization for free via financial aid and I have to say even though I did not pay a dime I feel I need to be compensated by IBM and refunded real money for torturing myself with their courses.

By Nicholas B

Feb 2, 2020

Extremely basic introductory course. Unfortunately you don't learn much about actual data science methods. Quiz questions tend to require you to memorize word for word quotations of supplied text, as opposed to challenging you to think about concepts. I would recommend this course for someone completely new to the idea of data science, but not to people who already know a bit.

By Ashmini G K

Jul 17, 2021

This was a great introduction to the field of data science. Having videos interspaced with readings made it easier to maintain focus. The speakers in the videos were super engaging and I liked the upfront warning that data science involves continuous learning, and a willingness to look up stuff and practice until you understand how to do new developments in field. As a researcher who writes reports for shareholders, I felt like students could have benefited from a warning that after you figure out 5 possible solutions to a problem, and detail them in your conclusion and recommendations section, few of the stakeholders will actually read or implement the recommendations. But, hey, at least you'll have fun doing the analysis.

Although the e-note format was great in theory, I found the traditional technique of writing stuff down while watching the videos and reading the material to be more useful, as I didn't need to be logged into the site to study my notes. It's great that both options are available and learners can use the option that best fits their learning style.

By Shelley

Sep 23, 2018

The course provides a good overview of data science in general. I particularly liked the definitions of a data scientist and data science. Mr. Haider's definitions are inclusive, broad and encouraging, He says one of the most important traits for a data scientist to possess is curiosity and that tools and techniques can be learnt.

The course also touches upon hot topic areas that people have heard of but most do not understand - i.e. Machine Learning, Neural Networks, Data Mining and Big Data. I have a much better idea what these terms mean now along with the tools of the trade. The course was quite short and concise. I found it the perfect pace for me. The quizzes matched the content and there was nothing extraneous.

I am looking forward to the other courses in the specialization. A quick glance has shown me that the difficulty level increases quite a lot in the other courses and I would definitely have to invest a lot more time in them. The start has been gentle and encouraging, thank you!

By Ghids E

Apr 3, 2024

This course serves as an excellent primer for individuals embarking on a journey into data science. It effectively covers essential concepts such as data types, metadata, data repositories, and storage technologies. The lessons are well-structured and provide a comprehensive overview of the data ecosystem. Overall, Course 1 lays a solid groundwork for aspiring data scientists and adequately prepares them for more advanced topics in the field.

By Enas J k

Jul 22, 2020

This course has very detailed information on data science and data scientists. The real-life examples and applications of data science presented by different data scientists are also amazing. Overall an excellent course for anyone who wants to venture into this amazing field.

By Abdul W

May 31, 2020

After completing this course you can easily understand and define what is Data Science and clear your doubt about Data Science.I recommend this course to all beginners.

By longmen

May 6, 2019

I have learnt about what the data science is and it's basic knowledge. I am glad I took the course. I will continue finishing the rest of the courses.

By Kanchan P

Jan 3, 2019

This is a very good introduction to what actually is data science! Lot of people gets really confused with the definitions and area!

By Sergi

Jan 1, 2019

Direct to the point. Increases one's passion to study Data Science by summarizing the main topics. Simple and brilliant

By Amarjot S

Mar 7, 2020

This course equips a person with all necessary knowledge required to get started in this field with confidence.

By uzair k

Mar 7, 2020

A very brief and complete introduction of Data Science from industry experts highly recommended course

By Mahesh K

Jan 3, 2019

It encompasses fine details to introduce data science and explore data scientists as a career.

By Leticia V L

Apr 29, 2024

Excelente curso, son indispensables las bases para comprender temas más específicos

By Harsh R

Jun 1, 2020

Amazing course to a roadmap to data science

By Ferry T

Aug 20, 2019

Great for introduction!

By Irfani K

Nov 25, 2020

Very good thank you

By Chan H D L

Jan 3, 2019

Very informative and presented by respected individuals with a passion for the field. The only critique is that the material might be a little outdated as it seems to have been created around 2014-2015.

By Dwight F

Jan 1, 2019

It does in fact answer a basic, fundamental question; what is Data Science?

By Dylan H

Feb 12, 2019

Was ok.

1) For what it was, (predominantly opinion videos) you could probably stand to remove a couple of the videos from the two professors, (especially the second one) without changing the overall effectiveness of the course.

2) You might want to work with that second professor, (or just edit the appropriate video) to remove the clear disdain he expresses toward statisticians since a) they're at least just as much a part of data science as computer scientists and are thus due the same degree of respect, and b) some / several of the people taking this course, (such as me) come from a statistics background, and it's really not a good idea to require us to sit there and watch while this prof decides to backhand us all with his not only inappropriate, but, more importantly, inaccurate statements - really not a good way to help us feel welcome as part of the program.

3) The final question on the final exam was -horribly- rote and pedantic. I get that you're trying to find something that's easy to peer review, but really - one random person's opinion on how to format a paper? That was about as unimaginative / uninteresting as you could possibly get and only goes further into making data science seem like it's going to be an incredibly boring-to-other-than-totally-left-brained-control-freak humans. How about easing it up a bit on the precise content and let people answer more about the Voltaire discussion, (i.e. thinking about how you want to present the results even before writing the report)? How about having people talk about the first professor's discussion of what he did with the Toronto busses - going off on his own to acquire priorly-unrelated data to determine correlations? These are the kinds of things that are going to help someone become a good data scientist later in life, not whether they could list 10 italicized bullet points about how precisely one form of report, (i.e. a very formal, long form - often only used in its entirety in academia and generally inappropriate in other contexts).