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Отзывы учащихся о курсе Tools for Data Science от партнера IBM Skills Network

4.5
звезд
Оценки: 24,428

О курсе

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, JupyterLab, RStudio IDE, Git, GitHub, and Watson Studio. 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 Skills Network 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 Watson Studio and demonstrate your proficiency preparing a notebook, writing Markdown, and sharing your work with your peers....

Лучшие рецензии

ED

14 авг. 2022 г.

I love the detailing of every aspect of this course. The Labs, the free subscriptions and free trials provided by IBM Skills Network, everything has been so amazing. Thank you Coursera, thank you IBM.

GC

12 апр. 2020 г.

It serves perfecty its aim that is giving a first glance of the open course tools for data science. Of course each tool is briefly touched and it hands over the student the duty to deepen each tool.

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126–150 из 3,924 отзывов о курсе Tools for Data Science

автор: Juan D P

12 апр. 2019 г.

It was a very good introductory course to the Open Source tools available for the practice of Data Science. The pace is normal and the contents are enough to understand and fulfill the requirements in the final project assignment. The IBM Watson Studio presented some issues but they were solved in less than 24 hours by the IBM Engineers. Kudos to the support team!

автор: Meryl D

12 апр. 2022 г.

I like it. I've learned so much from it. It was exciting also to grade other students. The only problem is the tools. Though it's free, I had a problem connecting with it. So if you encountered problem with the tools you can just go to discussion. There you can ask question and seek answer for your problems. Goodluck and hope we hope we can all achieve our goal.

автор: Kefang A Y

18 апр. 2019 г.

It is very useful to know the IBM cloud platform and practical. The cloud platform is very complicate and may do many things. It looks like a forest for me.

Some videos is old interface. If there are step by step instructions, it will save me some time to quickly start. Assignment is easy, but take time to get used to the environment and to know where am I.

автор: OLUCHUKWU G

19 апр. 2020 г.

I have to give it 5 star cause it was so explanatory and exciting, though the video instructions need to be upgraded cause the web interface as at April 19th, 2020 isn't the same as the Video instructions. So that needs to be upgraded!!! But you work your way round it if you pay attention!!!

Nevertheless, it's a wonderful course and very exciting!!!!

автор: Anand P

26 февр. 2022 г.

Good Course. Will suggest the learners to do optional labs and hands on with Jupyter Labs using Kaggle notebooks and explore IBM cloud and Watson Studio features such as Data Refinery to get the maximum out of this course. This course can easily take 25-30 Hours if you are a beginner and complete all hands on Kaggle exercises including GitHub.

автор: Mamtha V S

20 февр. 2019 г.

Loved it! The most useful aspect was the cognitive class cloud web environment, which truly allows you the flexibility and mobility to practice from anywhere. There is no need to download install software, so you can access the course, literally from any machine, in a seamless manner. That's a very neat feature for someone who is working.

автор: Mohammed A M A

29 июня 2020 г.

Assignment assessment is not controlled and sometimes assessors don't assess based on the right content. I encountered this issue in several courses' assignments. I hope that the peer-graded assignment undergoes some revision to establish a more credited assessment approach.

Thank you for your consideration.

Coursera is always the best!

автор: La D Q

19 июня 2021 г.

This is a very informative course, provide data science newbies with various of tools to learn about on their data science journey. Not only that, this course also explained to me that doing data science isn't just analyzing data by coding with R or Python, it is more than that, with preparing data, storing data and building models.

автор: Joseph G

29 нояб. 2019 г.

I learned so much in this course. I had no idea these tools were available in one place online. In preparation for doing the IBM Professional Certificate, I spent about a week installing programs and languages and stressing out about GUIs and IDEs. This course showed me multiple ways to get around all this and use IBM online tools.

автор: K L K

10 окт. 2020 г.

A very good informative course! All tools of data science are discussed. A bit practical sense of few important open source tools is given nicely. You will uderstood that there are wide variety of tools for data science! Great one! This will push the beginners out of their theoretical zone to see the real information and tools.

автор: Jess M

29 янв. 2019 г.

A lot of this is outdated since the IBM Watson stuff has been completely revamped, and the Zeppelin Notebooks tutorials were difficult to follow, since they seemed to have already been run when I went in, and therefore I couldn't tell what was function and what was output. But it's great to have all these free tools available.

автор: James L M

10 июня 2020 г.

This actually helps me since I have no background regarding the tools needed for development in Data Science. Week 1 and 2 are particularly helpful since it introduces a lot of tools that one could use. Week 3 is a bit promotional and week 4 is true challenge. The course really guides you on how to start jupyter notebook.

автор: Ankit T

19 апр. 2020 г.

It was a great experience to learn the various open-source tool for data science. I have gained considerable knowledge of the Jupyter Notebook and IBM Watson Studio. It will be of great help for learners if the data science experience tool videos will be modified with IBM Watson Studio navigation and notebook creation.

автор: Isis S C

19 янв. 2020 г.

Loved the course! It presents integrated environments where we can perform data analysis (+all previous and post steps) using multiple languages in open-source tools. IBM Skills Network Labs is perfect for learning, IBM Watson Studios enables collaboration and scalability, for enterprises. Super convenient tools!

автор: Frances B

20 мар. 2019 г.

the course is great for people getting into the field of data science and have no clue where to start with resources for the filed. It helps build confidence and security knowing there are resources at our finger tips, for free, and with guidance from the the tutorial videos provided in this course. great stuff.

автор: Atal S S M

5 мар. 2019 г.

Very informative on the open source tools available. It does get tricky sometimes to understand the instructions of the notebooks as the videos display an older version and the current website would have the updated version. IBM Cloud is a huge advantage to work on Python,R and Scala with spark kernels for free.

автор: Mike M

14 дек. 2019 г.

Very beneficial, albeit somewhat painful , in getting the assignment done since the videos (at the time of this review) are not exactly correlated with current software (Watson v. Data Experience).

But hey, we are to be problem solvers, so that was just one minor hurdle to overcome and learn from in the process!

автор: Avadhoot

18 июля 2020 г.

This course was intensive on tools used in Data Science. It was an overwhelming experience for I learnt to use resources on Github and understood how Jupyter notebooks are important in writing long codes. All in all , a great experience and would like to complete the full IBM data science specialization soon.

автор: Travis T

4 июня 2020 г.

It's a good overview of all the tools that can be used for data science. If you're following along with the IBM course, it gives you a good idea of what you could be using for your capstone class. They do not detail much of tools rather introduce them to you. It'd be up to you to delve deeper if you'd like.

автор: Nyaniso N

1 мар. 2021 г.

A very gentle and slightly challenging introduction to the some of the best tools in the Data Science fraternity. Also, the added introduction to the IBM Watson Clouds tools was seriously interesting. Who knew you could just drag and drop files of refined data and you are on your way to "Model Building"?

автор: Luis G

4 авг. 2020 г.

Astonishing course for learning the basics of the tools used for data science, open source tools and comercial tools, at the beginning it might be a bit overwhelming because of lots of terms that are unknown by most starters like me, but as the course goes on and if you are commited, it's a piece of cake

автор: CHIN-HUNG, Y

24 мая 2019 г.

Nice in introducing approaches to data science; however, some parts appears unnecessary to be mentioned right in the beginning. For example: Appache Zeeplin and Zeppling for Scala are more of courses in either intermediate or advanced level. Perhaps postponing it till database would be a better option.

автор: elmer e

22 июня 2020 г.

This is a very comprehensive presentation on the available tools for Data Science both Open Sources and that of IBM proprietary tools. As presented, you as a Data Scientist has the sole option which of these tools are fit for your data science studies. Very enlightening and full of thoughts to ponder.

автор: Isha C

23 сент. 2019 г.

Good introduction to free and paid programs available for practicing and understanding data science! It shows detailed UI walkthroughs and tutorials, and gets you started setting up accounts that you will most likely use many years while learning data science and programming (R, Scala, Python, etc).

автор: Courtney B

24 сент. 2018 г.

Love it! It's such a gentle introduction to the tools of the trade as well as the languages we need to learn in order to use them. The labwork is my favorite part. The only way you can learn anything interactive like this is by diving in and trying things out, and now all I want to do is learn MORE.