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.
IBM課程信息
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完成這些課程後已開始新的職業生涯
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19%
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完成後獲得證書
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初級
完成時間大約為16 小時
英語（English）
字幕：英語（English）, 韓語
學生職業成果
46%
完成這些課程後已開始新的職業生涯
45%
通過此課程獲得實實在在的工作福利
19%
加薪或升職
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。
可靈活調整截止日期
根據您的日程表重置截止日期。
初級
完成時間大約為16 小時
英語（English）
字幕：英語（English）, 韓語
提供方
IBM
IBM offers a wide range of technology and consulting services; a broad portfolio of middleware for collaboration, predictive analytics, software development and systems management; and the world's most advanced servers and supercomputers. Utilizing its business consulting, technology and R&D expertise, IBM helps clients become "smarter" as the planet becomes more digitally interconnected. IBM invests more than $6 billion a year in R&D, just completing its 21st year of patent leadership. IBM Research has received recognition beyond any commercial technology research organization and is home to 5 Nobel Laureates, 9 US National Medals of Technology, 5 US National Medals of Science, 6 Turing Awards, and 10 Inductees in US Inventors Hall of Fame.
教學大綱 - 您將從這門課程中學到什麼
完成時間為 4 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 4 小時
17 個視頻 （總計 84 分鐘）, 1 個閱讀材料, 4 個測驗
17 個視頻
Languages of Data Science2分鐘
Introduction to Python3分鐘
Introduction to R Language3分鐘
Introduction to SQL3分鐘
Other Languages6分鐘
Categories of Data Science Tools2分鐘
Open Source Tools for Data Science - Part 17分鐘
Open Source Tools for Data Science - Part 25分鐘
Commercial Tools for Data Science5分鐘
Cloud Based Tools for Data Science8分鐘
Libraries for Data Science4分鐘
Application Programming Interfaces (API)4分鐘
Data Sets - Powering Data Science6分鐘
Sharing Enterprise Data - Data Asset eXchange3分鐘
Machine Learning Models7分鐘
The Model Asset Exchange5分鐘
1 個閱讀材料
Lab - Explore Data Sets and Models 10分鐘
4 個練習
Practice Quiz - Languages 30分鐘
Practice Quiz - Tools30分鐘
Practice Quiz - Packages, APIs, Data Sets, Models30分鐘
Graded Quiz30分鐘
完成時間為 6 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 6 小時
10 個視頻 （總計 57 分鐘）, 9 個閱讀材料, 7 個測驗
10 個視頻
GitHub on Mac/Linux - Part 19分鐘
GitHub on Mac/Linux - Part 2 (Optional)5分鐘
GitHub on Mac/Linux - Part 3 (Optional)8分鐘
Getting Started with Jupyter Notebook5分鐘
Getting Started with JupyterLab6分鐘
Jupyter Architecture6分鐘
What is RStudio IDE?4分鐘
Installing Packages and Loading Libraries in RStudio IDE2分鐘
Plotting Within RStudio IDE3分鐘
9 個閱讀材料
GitHub Lab Part - 110分鐘
Pre-requisites for command line interface (Optional)10分鐘
Branching & Merging on GitHub (Optional)10分鐘
Configuring SSH access to repository (Optional)10分鐘
GitHub Part 2 Instructions for Windows (Optional)10分鐘
GitHub Part 3 Instructions for Windows (Optional)10分鐘
Jupyter Notebooks on the Internet10分鐘
Lab: RStudio – The Basics10分鐘
Lab: Creating an Interactive Map in R30分鐘
4 個練習
Practice Quiz - GitHub30分鐘
Practice Quiz - Jupyter Notebook30分鐘
Practice Quiz - RStudio IDE30分鐘
Graded Quiz30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）, 2 個閱讀材料, 3 個測驗
15 個視頻
Watson Studio Introduction4分鐘
Creating an Account on IBM Watson Studio2分鐘
Jupyter Notebook in Watson Studio - Part 1 2分鐘
Jupyter Notebook in Watson Studio - Part 23分鐘
Linking GitHub to Watson Studio2分鐘
Other IBM Tools for Data Science1分鐘
IBM Watson Knowledge Catalog6分鐘
Data Refinery7分鐘
SPSS Modeler Flows in Watson Studio6分鐘
IBM SPSS Modeler7分鐘
SPSS Statistics7分鐘
Model Deployment with Watson Machine Learning4分鐘
Auto AI in Watson Studio4分鐘
IBM Watson OpenScale7分鐘
2 個閱讀材料
Lab: Creating a Watson Studio Project with Jupyter Notebook10分鐘
Lab: Modeler Flows in Watson Studio10分鐘
3 個練習
Practice Quiz - Watson Studio30分鐘
Practice Quiz - Other IBM Tools30分鐘
Graded Quiz30分鐘
完成時間為 3 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 3 小時
1 個閱讀材料
1 個閱讀材料
IBM Digital Badge2分鐘
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 RR 提供2019年4月24日
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!!!!
由 FC 提供2019年11月5日
It would be nice if you could update the material since some tools have changed either name or the way they look compared to the videos/images. Very good material though, I enjoyed the course much.
由 SS 提供2020年7月19日
Good content, excellent delivery. Week1 had too much of information at once. I was left with little motivation after week-1. All the other weeks were good. Exercises were engaging and very useful.
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