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学生对 IBM 提供的 Tools for Data Science 的评价和反馈

4.5
30,308 个评分

课程概述

In order to be successful in Data Science, you need to be skilled with using tools that Data Science professionals employ as part of their jobs. This course teaches you about the popular tools in Data Science and how to use them. You will become familiar with the Data Scientist’s tool kit which includes: Libraries & Packages, Data Sets, Machine Learning Models, Kernels, as well as the various Open source, commercial, Big Data and Cloud-based tools. Work with Jupyter Notebooks, JupyterLab, RStudio IDE, Git, GitHub, and Watson Studio. You will understand what each tool is used for, what programming languages they can execute, their features and limitations. This course gives plenty of hands-on experience in order to develop skills for working with these Data Science Tools. 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. Towards the end the course, you will create a final project with a Jupyter Notebook. You will demonstrate your proficiency preparing a notebook, writing Markdown, and sharing your work with your peers....

热门审阅

AA

Oct 15, 2024

Good Course overall focus in basic tools. the course could be shorter as someone who know which language who want to use and familir with the tools already shouldn't learn all the course materials

MA

May 19, 2023

The course is overwhelming for a beginner with no experiecne of programming. The examples given in the class seem difficult and should have been of a lower difficulty level to keep the hopes high.

筛选依据:

3301 - Tools for Data Science 的 3325 个评论(共 4,973 个)

创建者 Juan P

Feb 7, 2025

Las traducciones no son tan buenas, las calificaciones no son fiables

创建者 Vasu S

Jun 3, 2021

It's a good course but we need more detailed videos on Watson Studio.

创建者 Christopher D

May 13, 2021

It was a lot of information but a good primer for the specialization.

创建者 Karim A

Jan 5, 2020

Please update the vides to reflect the same environment as IBM Watson

创建者 Filipe S M G

Aug 18, 2019

Interesting to know the available tools (although, a bit repetitive).

创建者 Samantha R

Jul 8, 2019

Nice starting point to learn how to use a notebook in a simple manner

创建者 Joe A

Jun 29, 2019

Good introduction to various tools available to data science novices.

创建者 David P

Jun 13, 2019

An exciting start to the actual work of data science using IBM tools.

创建者 Humayun A

Apr 19, 2019

need to explain what we will learn from this week materials and quiz.

创建者 Navaneetha K R

Apr 4, 2019

Course gives a overall view of how to use various data science tools.

创建者 asher b

Sep 14, 2018

Succinct high-level overview. Still need more courses to get skilled.

创建者 Leo S

Oct 15, 2022

its so good to learn ,but its also little bit harder to understand.

创建者 Juan D P M

Feb 11, 2022

i would liked more practic labs on the course, but overall is good.

创建者 Kwaku B

Jul 18, 2020

It needs more real-world or direct workplace data sources to work on

创建者 Robert W

Mar 3, 2020

Excellent course. It was perfectly paced, the videos were excellent,

创建者 Georgios G

Oct 25, 2019

The IBM videos for using the IBM experience module should be updated

创建者 Deleted A

Sep 4, 2018

Why not just write with a Jupyter notebook from your own web browser

创建者 Hazem A M S

Sep 15, 2021

Sufficient moderate detailed information of IBM Watson Studio tools

创建者 Joshua

Jul 26, 2020

Insightful thou though but all the effort and extra reading pay off

创建者 Rubén Q “ H D M

Apr 15, 2020

Skills acquired from tools like, Jupyter notebooks, R, Scale, Spark

创建者 Prateek G

Mar 18, 2020

Good course on introduction of various tools used for data science.

创建者 Matt A

Feb 8, 2019

Pretty comprehensive. Good to get familiar with open-source tools.

创建者 Semilogo O

Jul 18, 2023

Overall very informative and supported by lab task for application

创建者 Rajnish S

Jun 23, 2022

Adetaileed course on tools belonging to the field of data science.

创建者 Selene E A H

Mar 1, 2022

Me gustaria mas practicas en las que tuvieramos que explorar datos