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.
提供方
IBM課程信息
761,672 次近期查看
學生職業成果
23%
完成這些課程後已開始新的職業生涯
27%
通過此課程獲得實實在在的工作福利
17%
加薪或升職
可靈活調整截止日期
根據您的日程表重置截止日期。
可分享的證書
完成後獲得證書
100% 在線
立即開始，按照自己的計劃學習。
初級
完成時間大約為17 小時
英語（English）
字幕：阿拉伯語（Arabic）, 法語（French）, （歐洲人講的）葡萄牙語, 意大利語, 越南語, 德語（German）, 俄語（Russian）, 英語（English）, 西班牙語（Spanish）, 波斯語
學生職業成果
23%
完成這些課程後已開始新的職業生涯
27%
通過此課程獲得實實在在的工作福利
17%
加薪或升職
可靈活調整截止日期
根據您的日程表重置截止日期。
可分享的證書
完成後獲得證書
100% 在線
立即開始，按照自己的計劃學習。
初級
完成時間大約為17 小時
英語（English）
字幕：阿拉伯語（Arabic）, 法語（French）, （歐洲人講的）葡萄牙語, 意大利語, 越南語, 德語（German）, 俄語（Russian）, 英語（English）, 西班牙語（Spanish）, 波斯語
提供方
IBM
IBM is the global leader in business transformation through an open hybrid cloud platform and AI, serving clients in more than 170 countries around the world. Today 47 of the Fortune 50 Companies rely on the IBM Cloud to run their business, and IBM Watson enterprise AI is hard at work in more than 30,000 engagements. IBM is also one of the world’s most vital corporate research organizations, with 28 consecutive years of patent leadership. Above all, guided by principles for trust and transparency and support for a more inclusive society, IBM is committed to being a responsible technology innovator and a force for good in the world.
教學大綱 - 您將從這門課程中學到什麼
完成時間為 2 小時
Data Scientist's Toolkit
This week, you will get an overview of the programming languages commonly used, including Python, R, Scala, and SQL. You’ll be introduced to the open source and commercial data science tools available. You’ll also learn about the packages, APIs, data sets and models frequently used by data scientists.
完成時間為 2 小時
17 個視頻 （總計 84 分鐘）
完成時間為 10 小時
Open Source Tools
This week, you will learn about three popular tools used in data science: GitHub, Jupyter Notebooks, and RStudio IDE. You will become familiar with the features of each tool, and what makes these tools so popular among data scientists today.
完成時間為 10 小時
12 個視頻 （總計 54 分鐘）
完成時間為 3 小時
IBM Tools for Data Science
This week, you will learn about an enterprise-ready data science platform by IBM, called Watson Studio. You'll learn about some of the features and capabilities of what data scientists use in the industry. You’ll also learn about other IBM tools used to support data science projects, such as IBM Watson Knowledge Catalog, Data Refinery, and the SPSS Modeler.
完成時間為 3 小時
15 個視頻 （總計 72 分鐘）
完成時間為 1 小時
Final Assignment: Create and Share Your Jupyter Notebook
This week, you will demonstrate your skills by creating and configuring a Jupyter Notebook. As part of your grade for this course, you will share your Jupyter Notebook with your peers for review.
完成時間為 1 小時
1 個閱讀材料
審閱
來自TOOLS FOR DATA SCIENCE的熱門評論
由 CB 提供2018年11月1日
Tools are fantastic and will make a significant contribution to my education. Videos need to be updated for changes to Watson Studios. Support from IBM on their cloud services should also be improved.
由 NK 提供2019年5月19日
The course video contents and the tools versions are not the same.There are some significant differences .Videos should be updated.In general the course is a good fundamental course about the tools.
由 AJ 提供2020年9月15日
Absolutely Loved this course!! Challenging at times to keep up with all the terms and processes. The course provided great insight into Data Science. Would highly recommend it as your first course.
由 FD 提供2020年11月17日
Some of the lab assignments had instructions that didn't line up with how the programs actually worked. This was particularly the case for modular flow where auto-numerics seemed impossible to use.
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