Data Science: A New Way of Thinking | 数据科学导论 from edX

As a pilot course and cognitive course for data science, this course is dedicated to popularizing the basic knowledge, core concepts and thinking models related to data mining and big data for students through a vivid teaching model, from engineering technology, legal norms, and application practice. Describe the beautiful blueprint of data science from different angles. This course is suitable for college students from various backgrounds who are interested in the fascinating field of data science. Existing online data science courses mainly focus on purely technical content such as learning specific algorithms. In contrast, data science is an application-oriented, highly interdisciplinary field that requires systematic knowledge from multiple domains. In addition to algorithmic learning, students also need to recognize the challenges people may face in the real world and the relationship between data and human society. The purpose of this course is to comprehensively understand the key issues in the big data era, improve data awareness, and help students lay a solid foundation for subsequent data science courses.

This is an introductory course suitable for university students with diverse backgrounds interested in getting into the fascinating world of data science. Existing online data science courses mainly focus on learning specific algorithms and other purely technical contents. By contrast, data science is an application-oriented, highly interdisciplinary domain, which requires systematic knowledge from a variety of sources. In addition to algorithm learning, students also need to appreciate the challenges that people may face in the real world as well as the relationship between data and human society. The purpose of this course is to provide a comprehensive understanding of the key issues in the era of big data and promote data awareness to help students lay a solid foundation for subsequent data science courses.

What's inside

Learning objectives

Basic concepts of data science
Techniques for data acquisition, storage and transmission
Data visualization: principles and techniques

High performance computing
Data ethics and intellectual property law
Real-world case studies

Basic concepts of data science
Techniques for data acquisition, storage and transmission
Data visualization: principles and techniques
High performance computing
Data ethics and intellectual property law
Real-world case studies

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Introduces the core concepts, thinking patterns, and basic principles of data mining, big data technologies, and their applications

Provides real-world case studies to illustrate the application of data mining and big data technologies in various domains

Suitable for university students with diverse backgrounds, making it accessible to learners with no prior knowledge in data science

Covers data acquisition, storage, transmission, visualization, high-performance computing, data ethics, and intellectual property law, providing a comprehensive overview of data science

Offers a solid foundation for subsequent data science courses, enabling learners to build upon the knowledge gained in this course

Taught by instructors with expertise in data mining and big data, ensuring the delivery of up-to-date and relevant knowledge

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Data Science: A New Way of Thinking | 数据科学导论 with these activities:

Review Basic Concepts of Data Science

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Brush up on the fundamental principles and concepts of data science to establish a strong foundation for the course.

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Read through introductory materials on data science.
Review key definitions and terminology related to data science.
Summarize the different types of data and their applications.

Read 'Data Science for Business'

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Gain insights into the practical applications of data science in business contexts by reading this industry-focused book.

Browse courses on Business Analytics

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Read through the chapters covering topics such as data analysis, predictive modeling, and decision-making.
Summarize the key concepts and case studies presented in the book.

Organize Course Materials

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Stay organized and improve your learning experience by compiling and reviewing course materials.

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Create a central repository for all course materials, such as lecture notes, assignments, and readings.
Review and summarize key concepts from each lecture.
Develop study guides that consolidate important information.

Four other activities

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Practice Data Acquisition Techniques

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Gain practical experience in acquiring data from various sources to enhance your understanding of data handling.

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Explore different methods of data acquisition, such as web scraping and API integration.
Practice extracting data from structured and unstructured sources.

Join a Data Science Study Group

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Foster collaboration and strengthen your understanding by engaging in discussions and working on projects with peers.

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Find or create a study group with other students enrolled in the course.
Regularly meet to discuss course material, solve problems, and share insights.

Visualize Data Using Python

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Develop your data visualization skills by creating interactive visualizations using Python libraries.

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Learn the basics of data visualization libraries such as Matplotlib and Seaborn.
Create visualizations for various data types, such as bar charts, histograms, and scatter plots.

Develop a Data Science Project

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Apply your knowledge to a real-world problem by developing a data science project from conception to completion.

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Identify a problem or opportunity that can be addressed using data science.
Gather and analyze data relevant to the problem.
Develop and implement a data science solution.
Communicate your findings and recommendations.

Career center

Learners who complete Data Science: A New Way of Thinking | 数据科学导论 will develop knowledge and skills that may be useful to these careers:

Machine Learning Engineer

A Machine Learning Engineer designs, develops, and deploys machine learning models. This course may be useful for this role as it provides a foundation in data science concepts, techniques, and tools.

See salaries and explore the career path for Machine Learning Engineer

Data Scientist

A Data Scientist builds machine learning models and analyzes data to extract insights and make predictions. Completing this course can help build a foundation for success in this role by providing a comprehensive understanding of the key issues in the big data era, including data acquisition, storage and transmission, data visualization, high performance computing, data ethics, and intellectual property law.

See salaries and explore the career path for Data Scientist

Data Visualization Analyst

A Data Visualization Analyst designs and develops visualizations to communicate data insights. This course may be useful for this role as it provides a foundation in data science concepts, techniques, and tools.

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Statistician

A Statistician collects, analyzes, interprets, and presents data. This course may be useful for this role as it provides a foundation in data science concepts, techniques, and tools.

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Data Governance Analyst

A Data Governance Analyst develops and implements policies and procedures to ensure the quality and security of data. This course may be useful for this role as it provides a foundation in data science concepts, techniques, and tools.

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Data Analyst

A Data Analyst analyzes data to identify trends and patterns, and to make recommendations. This course may be useful for this role as it provides a foundation in data science concepts, techniques, and tools.

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Information Security Analyst

An Information Security Analyst protects an organization's data from unauthorized access, use, disclosure, disruption, modification, or destruction. This course may be useful for this role as it provides a foundation in data science concepts, techniques, and tools.

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Cybersecurity Analyst

A Cybersecurity Analyst protects an organization's networks and systems from cyberattacks. This course may be useful for this role as it provides a foundation in data science concepts, techniques, and tools.

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Networking Engineer

A Networking Engineer designs, builds, and maintains computer networks. This course may be useful for this role as it provides a foundation in data science concepts, techniques, and tools.

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Software Engineer

A Software Engineer designs, develops, and maintains software applications. This course may be useful for this role as it provides a foundation in data science concepts, techniques, and tools.

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Data Engineer

A Data Engineer builds and maintains the infrastructure that supports data science and machine learning applications. This course may be useful for this role as it provides a foundation in data science concepts, techniques, and tools.

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Cloud Architect

A Cloud Architect designs and manages cloud computing systems. This course may be useful for this role as it provides a foundation in data science concepts, techniques, and tools.

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Product Manager

A Product Manager manages the development and launch of products. This course may be useful for this role as it provides a foundation in data science concepts, techniques, and tools.

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Business Analyst

A Business Analyst analyzes business processes and data to identify opportunities for improvement. This course may be useful for this role as it provides a foundation in data science concepts, techniques, and tools.

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Marketing Analyst

A Marketing Analyst analyzes marketing data to measure the effectiveness of marketing campaigns. This course may be useful for this role as it provides a foundation in data science concepts, techniques, and tools.

See salaries and explore the career path for Marketing Analyst