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. The specialization entails understanding what is Data Science and the various kinds of activities that a Data Scientist performs. It will familiarize learners with various open source tools, like Jupyter notebooks, used by Data Scientists. It will teach you about methodology involved in tackling data science problems. The specialization also provides knowledge of relational database concepts and the use of SQL to query databases. Learners will complete hands-on labs and projects to apply their newly acquired skills and knowledge.
26,868 人已註冊
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關於此 專項課程
107,863 次近期查看
學生職業成果
46%
完成此 專項課程 後開始了新的職業。
19%
加薪或升職。
您將獲得的技能
Data SciencePython ProgrammingRstudioSQL
學生職業成果
46%
完成此 專項課程 後開始了新的職業。
19%
加薪或升職。
可分享的證書
完成後獲得證書
100% 在線課程
立即開始，按照自己的計劃學習。
靈活的計劃
設置並保持靈活的截止日期。
初級
完成時間大約為1 個月
建議 12 小時/週
英語（English）
字幕：英語（English）, 俄語（Russian）, 韓語, 阿拉伯語（Arabic）
專項課程的運作方式
加入課程
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實踐項目
每個專項課程都包括實踐項目。您需要成功完成這個（些）項目才能完成專項課程並獲得證書。如果專項課程中包括單獨的實踐項目課程，則需要在開始之前完成其他所有課程。
獲得證書
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此專項課程包含 4 門課程
????????
27,311 個評分
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.
Open Source tools for Data Science
15,004 個評分
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.
Data Science Methodology
11,565 個評分
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.
Databases and SQL for Data Science
9,002 個評分
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.
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.
審閱
4.6
來自??????的熱門評論
由 JM 提供Feb 27, 2019
Clear, thorough, well-designed. Labs give useful practice, assignments and quizzes test the actual instructional content. This course is by far the best in the IBM Data Science specialization.
由 SS 提供Sep 21, 2018
The Course gives a holistic introduction to Data Science and what to expect going further into the other courses of this specialization. I enjoyed the classes which were easy to understand
由 OO 提供Mar 9, 2020
This is the first time I'm completing an "actual" course online. I enjoyed every bit of it and can't wait to complete the specialization. Kudos to Coursera and the lecturers!!!
由 VS 提供May 7, 2019
Nice introduction to the ideas and theories in Data Science. Shows you what to expect in your Data Science career and sets you up nicely for the rest of this specialization.
由 AB 提供Jan 5, 2019
Useful tools for the beginning data scientist. However I found that all courses listed through this specialization are available for free through Cognitive Class Labs.
由 PC 提供Dec 11, 2019
Thanks for creating this course but while doing it with complete specialization according to first course it feels little hard and not connected with previous course
由 AJ 提供Aug 30, 2019
This is my first certified course and I am very happy to complete this course. Looking forward to complete the whole specialization very soon.
由 PK 提供Dec 8, 2019
A great introductory course on Data Science Methodology! Well presented and structured as all the previous for that specialization!
常見問題
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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.
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No University credit will be earned.
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.
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