在数据科学领域工作. Gain foundational data science skills to prepare for a career or further advanced learning in data science.

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Rav Ahuja 另外 +6 位授课教师

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数据科学导论专项课程IBM

您将学到的内容有

Describe what data science and machine learning are, their applications & use cases, and various types of tasks performed by data scientists

Gain hands-on familiarity with common data science tools including JupyterLab, R Studio, GitHub and Watson Studio

Develop the mindset to work like a data scientist, and follow a methodology to tackle different types of data science problems

Write SQL statements and query Cloud databases using Python from Jupyter notebooks

您将获得的技能

Data Science
Relational Database Management System (RDBMS)
Cloud Databases
Python Programming
SQL
Deep Learning
Machine Learning
Big Data
Data Mining
Github
Jupyter notebooks
Rstudio

关于此专项课程

41,433 次近期查看

Interested in learning more about data science, but don’t know where to start? This 4-course Specialization from IBM will provide you with the key foundational skills any data scientist needs to prepare you for a career in data science or further advanced learning in the field. This Specialization will introduce you to what data science is and what data scientists do. You’ll discover the applicability of data science across fields, and learn how data analysis can help you make data driven decisions. You’ll find that you can kickstart your career path in the field without prior knowledge of computer science or programming languages: this Specialization will give you the foundation you need for more advanced learning to support your career goals. You’ll grasp concepts like big data, statistical analysis, and relational databases, and gain familiarity with various open source tools and data science programs used by data scientists, like Jupyter Notebooks, RStudio, GitHub, and SQL. You'll complete hands-on labs and projects to learn the methodology involved in tackling data science problems and apply your newly acquired skills and knowledge to real world data sets. In addition to earning a Specialization completion certificate from Coursera, you’ll also receive a digital badge from IBM recognizing you as a specialist in data science foundations. This Specialization can also be applied toward the IBM Data Science Professional Certificate.

应用的学习项目

You will utilize tools like Jupyter, GitHub, R Studio, and Watson Studio to complete hands-on labs and projects throughout the Specialization. Using new skills and knowledge gained through the program, you’ll also work with real world data sets and query them using SQL from Jupyter notebooks.

学生职业成果

12%

完成此专项课程后开始了新的职业。

可分享的证书

完成后获得证书

100% 在线课程

立即开始，按照自己的计划学习。

灵活的计划

设置并保持灵活的截止日期。

初级

无需相关领域的预备知识无需相关经验。

完成课程大约需要 4 个月

建议进度：3 小时/周

英语（English）

字幕：英语（English）, 阿拉伯语（Arabic）, 法语（French）, （欧洲人讲的）葡萄牙语, 意大利语, 越南语, 德语（German）, 俄语（Russian）, 土耳其语（Turkish）, 西班牙语（Spanish）, 波斯语

学生职业成果

12%

完成此专项课程后开始了新的职业。

可分享的证书

完成后获得证书

100% 在线课程

立即开始，按照自己的计划学习。

灵活的计划

设置并保持灵活的截止日期。

初级

无需相关领域的预备知识无需相关经验。

完成课程大约需要 4 个月

建议进度：3 小时/周

英语（English）

专项课程的运作方式

加入课程

Coursera 专项课程是帮助您掌握一门技能的一系列课程。若要开始学习，请直接注册专项课程，或预览专项课程并选择您要首先开始学习的课程。当您订阅专项课程的部分课程时，您将自动订阅整个专项课程。您可以只完成一门课程，您可以随时暂停学习或结束订阅。访问您的学生面板，跟踪您的课程注册情况和进度。

实践项目

每个专项课程都包括实践项目。您需要成功完成这个（些）项目才能完成专项课程并获得证书。如果专项课程中包括单独的实践项目课程，则需要在开始之前完成其他所有课程。

获得证书

在结束每门课程并完成实践项目之后，您会获得一个证书，您可以向您的潜在雇主展示该证书并在您的职业社交网络中分享。

此专项课程包含 4 门课程

课程1

课程 1

什么是数据科学？

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The art of uncovering the insights and trends in data has been around since ancient times. The ancient Egyptians used census data to increase efficiency in tax collection and they accurately predicted the flooding of the Nile river every year. Since then, people working in data science have carved out a unique and distinct field for the work they do. This field is data science. In this course, we will meet some data science practitioners and we will get an overview of what data science is today.

课程2

课程 2

Tools for Data Science

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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.

课程3

课程 3

Data Science Methodology

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Despite the recent increase in computing power and access to data over the last couple of decades, our ability to use the data within the decision making process is either lost or not maximized at all too often, we don't have a solid understanding of the questions being asked and how to apply the data correctly to the problem at hand.

This course has one purpose, and that is to share a methodology that can be used within data science, to ensure that the data used in problem solving is relevant and properly manipulated to address the question at hand. Accordingly, in this course, you will learn: - The major steps involved in tackling a data science problem. - The major steps involved in practicing data science, from forming a concrete business or research problem, to collecting and analyzing data, to building a model, and understanding the feedback after model deployment. - How data scientists think!

课程4

课程 4

Databases and SQL for Data Science with Python

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Much of the world's data resides in databases. SQL (or Structured Query Language) is a powerful language which is used for communicating with and extracting data from databases. A working knowledge of databases and SQL is a must if you want to become a data scientist.

The purpose of this course is to introduce relational database concepts and help you learn and apply foundational knowledge of the SQL language. It is also intended to get you started with performing SQL access in a data science environment. The emphasis in this course is on hands-on and practical learning . As such, you will work with real databases, real data science tools, and real-world datasets. You will create a database instance in the cloud. Through a series of hands-on labs you will practice building and running SQL queries. You will also learn how to access databases from Jupyter notebooks using SQL and Python. No prior knowledge of databases, SQL, Python, or programming is required. Anyone can audit this course at no-charge. If you choose to take this course and earn the Coursera course certificate, you can also earn an IBM digital badge upon successful completion of the course. LIMITED TIME OFFER: Subscription is only $39 USD per month for access to graded materials and a certificate.

授课教师

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.

常见问题

退款政策是如何规定的？
如果订阅，您可以获得 7 天免费试听，在此期间，您可以取消课程，无需支付任何罚金。在此之后，我们不会退款，但您可以随时取消订阅。请阅读我们完整的退款政策。
我可以只注册一门课程吗？
可以！点击您感兴趣的课程卡开始注册即可。注册并完成课程后，您可以获得可共享的证书，或者您也可以旁听该课程免费查看课程资料。如果您订阅的课程是某专项课程的一部分，系统会自动为您订阅完整的专项课程。访问您的学生面板，跟踪您的进度。
有助学金吗？
是的，Coursera 可以为无法承担费用的学生提供助学金。通过点击左侧“注册”按钮下的“助学金”链接可以申请助学金。您可以根据屏幕提示完成申请，申请获批后会收到通知。您需要针对专项课程中的每一门课程完成上述步骤，包括毕业项目。了解更多。
我可以免费学习课程吗？
完成注册课程后，您可以学习专项课程中的所有课程，并且完成作业后可以获得证书。如果您只想阅读和查看课程内容，可以免费旁听该课程。如果您无法承担课程费用，可以申请助学金。
此课程是 100% 在线学习吗？是否需要现场参加课程？
此课程完全在线学习，无需到教室现场上课。您可以通过网络或移动设备随时随地访问课程视频、阅读材料和作业。
How can I earn my IBM Badge?
Upon completion of the program, you will receive an email from Acclaim with your IBM Badge recognizing your expertise in the field. Some badges are issued almost immediately after completion of the badge activities, while others may take 1-2 weeks before they are issued. Once issued, you will receive a notification email from admin@youracclaim.com with instructions for claiming the badge. Learn more about IBM Badges
What is data science?
Data science is the process of collecting, storing, and analyzing data. Data scientists use data to tell compelling stories to inform business decisions. Learn more about what data science is and what data scientists do in the IBM Course, "What is Data Science?"
What are some examples of careers in data science?
An understanding of data science and the ability to make data driven decisions is useful in any career, but some careers specifically require a data science background. Some examples of careers in data science include:
- Business Intelligence Analyst
- Data Analyst
- Data Architect
- Data Engineer
- Data Scientist
- Machine Learning Engineer
- Marketing Analyst
- Operations Analyst
- Quantitative Analyst
How long does it take to complete this Specialization?
The Specialization consists of 4 courses. Suggested time to complete each course is 3-4 weeks. If you follow recommended timelines, it would take 3 to 4 months to complete the entire Specialization.
What background knowledge is necessary?
This Specialization is intended for learners wanting to build foundational skills in data science. No prior background in data science or programming is required.
Do I need to take the courses in a specific order?
In order to get the most out of this Specialization, it is recommended to take the courses in the order they are listed.
完成专项课程后我会获得大学学分吗？
No, there is no university credit associated with completing this Specialization.
What will I be able to do upon completing the Specialization?
In this Specialization, learners will develop foundational data science skills to prepare them for a career or further learning that involves more advanced topics in data science.

还有其他问题吗？请访问学生帮助中心。