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Voltar para Open Source tools for Data Science

Comentários e feedback de alunos de Open Source tools for Data Science da instituição IBM

4.6
estrelas
15,055 classificações
2,043 avaliações

Sobre o curso

What are some of the most popular data science tools, how do you use them, and what are their features? In this course, you'll learn about Jupyter Notebooks, RStudio IDE, Apache Zeppelin and Data Science Experience. You will learn about what each tool is used for, what programming languages they can execute, their features and limitations. With the tools hosted in the cloud on Cognitive Class Labs, you will be able to test each tool and follow instructions to run simple code in Python, R or Scala. To end the course, you will create a final project with a Jupyter Notebook on IBM Data Science Experience and demonstrate your proficiency preparing a notebook, writing Markdown, and sharing your work with your peers. LIMITED TIME OFFER: Subscription is only $39 USD per month for access to graded materials and a certificate....

Melhores avaliações

RR

Apr 25, 2019

To the contrast of other reviews, I find the content very well bifurcated and fed to the learners. The course very easily digestable and I have had a great amount of fun learning it.. Go for it!!!!

SH

Feb 01, 2019

All the tools required for ML kick starting was explained very clearly and it helped me a lot in building the understanding of what tools need to be learnt in the field of ML and Data Science.

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126 — 150 de {totalReviews} Avaliações para o Open Source tools for Data Science

por Rudolph M N

May 16, 2019

I use a chromebook and these tools were very helpfull for someone who is new to the discipline and cannot reliably boot a python terminal or R studio on his primary machine.

por Devvrat M

Dec 02, 2019

Great course for understanding the essentials tools required to implement Data Science. Juypter Notebook and Zappelin Notebooks are the most preferred Notebooks to work on.

por 陈嘉琪

Aug 27, 2019

Brief introduction to manny useful online tools for data science. Although it seems to be easy to accomplish this course, it really helps me learn more about data science.

por Tassio C d A G

Feb 24, 2019

This course is primordial to meet the tools applied to the slim courses. Explicit concepts and ease of access in the course. Course clear, structured and of great value.

por Zaheer R

Oct 14, 2019

Makes you familiar with the software tools needed to write code in Data Science. It is not an programming class but rather a great tutorial around each piece software.

por Nikhil K

Jan 26, 2019

Thanks a lot to Coursera & mentors. I am really very happy for such a wonderful teaching pattern which is not only beginner friendly but interesting & interactive too.

por Glener D M

Jan 22, 2019

I learned how to use Jupyter Notebook in the IBM Data Science Experience and practically with its proficiency in preparing a notepad along with the Markdown recording.

por Andrew B

Jan 06, 2019

Useful tools for the beginning data scientist. However I found that all courses listed through this specialization are available for free through Cognitive Class Labs.

por Gopala R

Oct 03, 2018

Very nice introduction to online tools with simple hands on training. Labs and quizzes are build the confidence of the student whether novice or expert in other areas.

por GREGORY M

Mar 25, 2020

Loved the practical exercises and information. Hands on in the notebooks in a few different environment gave me confidence to try out other things on my own. Thanks.

por Raghupati Y P

Feb 22, 2020

This course is little tough to clear at Assignment Stage but if your curious and determined then it's just the matter of time and you will start loving DATA SCIENCE.

por Pinky C

Dec 12, 2019

Thanks for creating this course but while doing it with complete specialization according to first course it feels little hard and not connected with previous course

por Vivekanand P

Sep 11, 2019

Nicely documented learning. Only suggestion is to update the content of lab where instructions are per the old DSX tool and are not exactly same for Watson studio.

por Andrew K J

Jul 12, 2019

I found this course very useful. I especially enjoyed practice with new markdown tools in Jupyter which were very useful for creating well formatted notebooks.

por Ramiro B

Sep 22, 2019

As elementary as it could be, it's a great introduction to Jupyter Notebooks indeed. But starting from this, I could see farther the reaching of Data Science.

por Patrícia P A

Jan 17, 2019

São muitos recursos e um mundo de ferramentas. O curso passa pela mais relevantes e propõe atividades práticas em cada uma delas. Muito bom ter este panorama.

por Azhan A

Sep 05, 2019

Awesome course. Lots of Learning. These free sources adds lots of ease to your work, its like everything tool you can think of is present in a single place.

por Onkar s

Aug 23, 2019

Really helpful in terms of getting a knowledge about the main core technologies used in Data Science.I would recommend this course to the absolute beginner.

por Girish B P

Jun 26, 2019

Course content was good, but IBM watson contents need to be updated and lots of issues while creating project. hopefully it gets resolved for other students

por Ekwoge E B

Jun 10, 2019

This course help open a very broad area of tools to use for Data Science I had no idea even existed. I am thrilled and motivated to work on this even more

por Maria N L

May 16, 2019

Excellent introduction to basic open source tools used in data science; however, the content needs updating since the UI of IBM Watson Studio has changed.

por Raissa B T

Mar 03, 2020

I liked very much the content. I know is an easy course, but I'd like to see more of what can be done, the languages etc. Thanks IBM for all this effort!

por Muhamma S

Feb 21, 2020

This is very useful course because I am only know the tool like juypter notebook , but course tells me about other tools like zepline , R studio , Amzing

por Jorge A C C

Nov 30, 2019

Muy recomendable, me encanto la oportunidad de poder hacer uso del Jupyter, Zeppelin, R en los entornos como el Skill Network Labs y el IBM Watson Studio