Princeton University
Private University • US
Showing 29 courses from Princeton
Algorithms Part I - Princeton University
Princeton (via Coursera)
Algorithms Part I - Princeton University is a comprehensive intermediate-level resource offered by Princeton, focused on building practical skills in data science and analytics. Whether you're a complete beginner looking to start a new career or a professional aiming to upgrade your skills, this resource provides a thorough learning experience. This is a structured online course with a carefully designed curriculum. Each module builds on the previous one, creating a logical progression from fundamentals to advanced topics. The course typically includes video lectures, reading materials, hands-on exercises, quizzes, and sometimes peer-reviewed assignments. This structured approach ensures you don't miss any critical concepts and build a solid foundation. This resource covers topics essential for success in data science and analytics, including Python, SQL, Pandas, NumPy, data visualization, statistics, and machine learning basics. The curriculum is structured to build your knowledge progressively — starting with foundational concepts and advancing to real-world applications. By the end, you should be able to: Master Go syntax, goroutines, and channels Build concurrent programs with Go's lightweight threading model Create REST APIs and CLI tools in Go Deploy Go binaries to production servers Duration: Estimated duration: 54 hours of content, designed to be completed in 6-11 weeks at a comfortable pace. Basic familiarity with the subject area is recommended. You should have completed a beginner-level course or have equivalent self-taught knowledge. Comfort with using a computer and basic problem-solving skills will help. This resource is designed for a wide audience: Students (B.Tech, BCA, MCA, BSc) looking to complement their academic learning with practical, industry-relevant skills Fresh graduates preparing for campus placements or off-campus interviews Working professionals looking to upskill, switch domains, or advance their careers Career changers transitioning from non-tech backgrounds into data science and analytics Freelancers wanting to add new services to their portfolio Self-learners passionate about data science and analytics and wanting structured guidance Pricing: This resource is completely free with no hidden charges. Completing this resource and building related skills can prepare you for roles such as Data Analyst, Business Analyst, Data Scientist, Analytics Engineer. Realistic salary bands in India (2025-2026), based on Naukri/AmbitionBox data: Freshers / 0-2 years: Rs 4-8 LPA Mid-level / 2-5 years: Rs 10-22 LPA Senior / 5+ years: Rs 25-50 LPA Actual offers vary heavily by city, company tier, and how strong your portfolio or interview performance is. Companies actively hiring in this space include TCS, Infosys, Flipkart, Amazon, Swiggy, Zomato, PhonePe. The data science industry in India is projected to grow at 27% CAGR through 2028. Companies across all sectors — from banking (HDFC, ICICI) to e-commerce (Flipkart, Amazon) to healthcare (Practo, PharmEasy) — are building data teams. India currently has a shortage of 200,000+ data professionals, making this one of the best fields to enter right now. Cities like Bangalore, Hyderabad, Pune, and Gurgaon have the highest concentration of data science jobs. Princeton is a well-established platform trusted by millions of learners worldwide. This particular resource has been selected by our editorial team based on: Content quality — comprehensive coverage with clear explanations Practical focus — emphasis on hands-on skills over pure theory Student outcomes — positive reviews and career success stories Indian relevance — content applicable to the Indian job market and interview patterns Updated curriculum — material reflects current industry practices and tools We regularly review and update our recommendations to ensure they remain relevant and high-quality.
Algorithms Part II - Princeton University
Princeton (via Coursera)
Algorithms Part II - Princeton University is a comprehensive advanced-level resource offered by Princeton, focused on building practical skills in data science and analytics. Whether you're a complete beginner looking to start a new career or a professional aiming to upgrade your skills, this resource provides a thorough learning experience. This is a structured online course with a carefully designed curriculum. Each module builds on the previous one, creating a logical progression from fundamentals to advanced topics. The course typically includes video lectures, reading materials, hands-on exercises, quizzes, and sometimes peer-reviewed assignments. This structured approach ensures you don't miss any critical concepts and build a solid foundation. This resource covers topics essential for success in data science and analytics, including Python, SQL, Pandas, NumPy, data visualization, statistics, and machine learning basics. The curriculum is structured to build your knowledge progressively — starting with foundational concepts and advancing to real-world applications. By the end, you should be able to: Master Go syntax, goroutines, and channels Build concurrent programs with Go's lightweight threading model Create REST APIs and CLI tools in Go Deploy Go binaries to production servers Duration: Estimated duration: 50 hours of content, designed to be completed in 5-10 weeks at a comfortable pace. This is an advanced resource meant for learners who already have solid fundamentals. You should have at least 6 months of hands-on experience or have completed intermediate-level courses in this area. This resource is designed for a wide audience: Students (B.Tech, BCA, MCA, BSc) looking to complement their academic learning with practical, industry-relevant skills Fresh graduates preparing for campus placements or off-campus interviews Working professionals looking to upskill, switch domains, or advance their careers Career changers transitioning from non-tech backgrounds into data science and analytics Freelancers wanting to add new services to their portfolio Self-learners passionate about data science and analytics and wanting structured guidance Pricing: This resource is completely free with no hidden charges. Completing this resource and building related skills can prepare you for roles such as Data Analyst, Business Analyst, Data Scientist, Analytics Engineer. Realistic salary bands in India (2025-2026), based on Naukri/AmbitionBox data: Freshers / 0-2 years: Rs 4-8 LPA Mid-level / 2-5 years: Rs 10-22 LPA Senior / 5+ years: Rs 25-50 LPA Actual offers vary heavily by city, company tier, and how strong your portfolio or interview performance is. Companies actively hiring in this space include TCS, Infosys, Flipkart, Amazon, Swiggy, Zomato, PhonePe. The data science industry in India is projected to grow at 27% CAGR through 2028. Companies across all sectors — from banking (HDFC, ICICI) to e-commerce (Flipkart, Amazon) to healthcare (Practo, PharmEasy) — are building data teams. India currently has a shortage of 200,000+ data professionals, making this one of the best fields to enter right now. Cities like Bangalore, Hyderabad, Pune, and Gurgaon have the highest concentration of data science jobs. Princeton is a well-established platform trusted by millions of learners worldwide. This particular resource has been selected by our editorial team based on: Content quality — comprehensive coverage with clear explanations Practical focus — emphasis on hands-on skills over pure theory Student outcomes — positive reviews and career success stories Indian relevance — content applicable to the Indian job market and interview patterns Updated curriculum — material reflects current industry practices and tools We regularly review and update our recommendations to ensure they remain relevant and high-quality.
Computer Science: Programming with a Purpose - Princeton
Princeton (via Coursera)
Computer Science: Programming with a Purpose - Princeton is a comprehensive beginner-level resource offered by Princeton, focused on building practical skills in programming and data structures. Whether you're a complete beginner looking to start a new career or a professional aiming to upgrade your skills, this resource provides a thorough learning experience. This is a structured online course with a carefully designed curriculum. Each module builds on the previous one, creating a logical progression from fundamentals to advanced topics. The course typically includes video lectures, reading materials, hands-on exercises, quizzes, and sometimes peer-reviewed assignments. This structured approach ensures you don't miss any critical concepts and build a solid foundation. This resource covers topics essential for success in programming and data structures, including algorithms, data structures, system design, and coding interview patterns. The curriculum is structured to build your knowledge progressively — starting with foundational concepts and advancing to real-world applications. By the end, you should be able to: Understand the core concepts and theoretical foundations Apply your knowledge through hands-on exercises and small projects Build the practical skills employers actually screen for Develop the problem-solving approach used by working professionals Duration: Estimated duration: 25 hours of content, designed to be completed in 3-5 weeks at a comfortable pace. No prior experience is required. This course starts from the absolute basics and gradually builds up complexity. A computer with internet access is all you need to get started. This resource is designed for a wide audience: Students (B.Tech, BCA, MCA, BSc) looking to complement their academic learning with practical, industry-relevant skills Fresh graduates preparing for campus placements or off-campus interviews Working professionals looking to upskill, switch domains, or advance their careers Career changers transitioning from non-tech backgrounds into programming and data structures Freelancers wanting to add new services to their portfolio Self-learners passionate about programming and data structures and wanting structured guidance Pricing: This resource is completely free with no hidden charges. Completing this resource and building related skills can prepare you for roles such as Software Development Engineer (SDE), Software Engineer, Backend Developer. Realistic salary bands in India (2025-2026), based on Naukri/AmbitionBox data: Freshers / 0-2 years: Rs 6-12 LPA Mid-level / 2-5 years: Rs 15-30 LPA Senior / 5+ years: Rs 30-60 LPA Actual offers vary heavily by city, company tier, and how strong your portfolio or interview performance is. Companies actively hiring in this space include Google, Amazon, Microsoft, Flipkart, PhonePe, Atlassian. Strong programming and DSA skills are the 1 factor in clearing technical interviews at product companies. Companies like Google, Amazon, Microsoft, Flipkart, and PhonePe all use coding rounds as their primary hiring filter. The Indian tech interview landscape typically involves 2-3 DSA rounds, 1 system design round (for experienced roles), and 1-2 behavioral rounds. Candidates who have solved 200+ quality problems on platforms like LeetCode consistently report higher interview success rates. Princeton is a well-established platform trusted by millions of learners worldwide. This particular resource has been selected by our editorial team based on: Content quality — comprehensive coverage with clear explanations Practical focus — emphasis on hands-on skills over pure theory Student outcomes — positive reviews and career success stories Indian relevance — content applicable to the Indian job market and interview patterns Updated curriculum — material reflects current industry practices and tools We regularly review and update our recommendations to ensure they remain relevant and high-quality.
Analysis of Algorithms - Princeton
Princeton (via Coursera)
Analysis of Algorithms - Princeton is a comprehensive advanced-level resource offered by Princeton, focused on building practical skills in programming and data structures. Whether you're a complete beginner looking to start a new career or a professional aiming to upgrade your skills, this resource provides a thorough learning experience. This is a structured online course with a carefully designed curriculum. Each module builds on the previous one, creating a logical progression from fundamentals to advanced topics. The course typically includes video lectures, reading materials, hands-on exercises, quizzes, and sometimes peer-reviewed assignments. This structured approach ensures you don't miss any critical concepts and build a solid foundation. This resource covers topics essential for success in programming and data structures, including algorithms, data structures, system design, and coding interview patterns. The curriculum is structured to build your knowledge progressively — starting with foundational concepts and advancing to real-world applications. By the end, you should be able to: Master Go syntax, goroutines, and channels Build concurrent programs with Go's lightweight threading model Create REST APIs and CLI tools in Go Deploy Go binaries to production servers Duration: Estimated duration: 20 hours of content, designed to be completed in 2-4 weeks at a comfortable pace. This is an advanced resource meant for learners who already have solid fundamentals. You should have at least 6 months of hands-on experience or have completed intermediate-level courses in this area. This resource is designed for a wide audience: Students (B.Tech, BCA, MCA, BSc) looking to complement their academic learning with practical, industry-relevant skills Fresh graduates preparing for campus placements or off-campus interviews Working professionals looking to upskill, switch domains, or advance their careers Career changers transitioning from non-tech backgrounds into programming and data structures Freelancers wanting to add new services to their portfolio Self-learners passionate about programming and data structures and wanting structured guidance Pricing: This resource is completely free with no hidden charges. Completing this resource and building related skills can prepare you for roles such as Software Development Engineer (SDE), Software Engineer, Backend Developer. Realistic salary bands in India (2025-2026), based on Naukri/AmbitionBox data: Freshers / 0-2 years: Rs 6-12 LPA Mid-level / 2-5 years: Rs 15-30 LPA Senior / 5+ years: Rs 30-60 LPA Actual offers vary heavily by city, company tier, and how strong your portfolio or interview performance is. Companies actively hiring in this space include Google, Amazon, Microsoft, Flipkart, PhonePe, Atlassian. Strong programming and DSA skills are the 1 factor in clearing technical interviews at product companies. Companies like Google, Amazon, Microsoft, Flipkart, and PhonePe all use coding rounds as their primary hiring filter. The Indian tech interview landscape typically involves 2-3 DSA rounds, 1 system design round (for experienced roles), and 1-2 behavioral rounds. Candidates who have solved 200+ quality problems on platforms like LeetCode consistently report higher interview success rates. Princeton is a well-established platform trusted by millions of learners worldwide. This particular resource has been selected by our editorial team based on: Content quality — comprehensive coverage with clear explanations Practical focus — emphasis on hands-on skills over pure theory Student outcomes — positive reviews and career success stories Indian relevance — content applicable to the Indian job market and interview patterns Updated curriculum — material reflects current industry practices and tools We regularly review and update our recommendations to ensure they remain relevant and high-quality.
Bitcoin and Cryptocurrency Technologies - Princeton
Princeton (via Coursera)
Bitcoin and Cryptocurrency Technologies - Princeton is a comprehensive intermediate-level resource offered by Princeton, focused on building practical skills in data science and analytics. Whether you're a complete beginner looking to start a new career or a professional aiming to upgrade your skills, this resource provides a thorough learning experience. This is a structured online course with a carefully designed curriculum. Each module builds on the previous one, creating a logical progression from fundamentals to advanced topics. The course typically includes video lectures, reading materials, hands-on exercises, quizzes, and sometimes peer-reviewed assignments. This structured approach ensures you don't miss any critical concepts and build a solid foundation. This resource covers topics essential for success in data science and analytics, including Python, SQL, Pandas, NumPy, data visualization, statistics, and machine learning basics. The curriculum is structured to build your knowledge progressively — starting with foundational concepts and advancing to real-world applications. By the end, you should be able to: Understand the core concepts and theoretical foundations Apply your knowledge through hands-on exercises and small projects Build the practical skills employers actually screen for Develop the problem-solving approach used by working professionals Duration: Estimated duration: 23 hours of content, designed to be completed in 3-5 weeks at a comfortable pace. Basic familiarity with the subject area is recommended. You should have completed a beginner-level course or have equivalent self-taught knowledge. Comfort with using a computer and basic problem-solving skills will help. This resource is designed for a wide audience: Students (B.Tech, BCA, MCA, BSc) looking to complement their academic learning with practical, industry-relevant skills Fresh graduates preparing for campus placements or off-campus interviews Working professionals looking to upskill, switch domains, or advance their careers Career changers transitioning from non-tech backgrounds into data science and analytics Freelancers wanting to add new services to their portfolio Self-learners passionate about data science and analytics and wanting structured guidance Pricing: This resource is completely free with no hidden charges. Completing this resource and building related skills can prepare you for roles such as Data Analyst, Business Analyst, Data Scientist, Analytics Engineer. Realistic salary bands in India (2025-2026), based on Naukri/AmbitionBox data: Freshers / 0-2 years: Rs 4-8 LPA Mid-level / 2-5 years: Rs 10-22 LPA Senior / 5+ years: Rs 25-50 LPA Actual offers vary heavily by city, company tier, and how strong your portfolio or interview performance is. Companies actively hiring in this space include TCS, Infosys, Flipkart, Amazon, Swiggy, Zomato, PhonePe. The data science industry in India is projected to grow at 27% CAGR through 2028. Companies across all sectors — from banking (HDFC, ICICI) to e-commerce (Flipkart, Amazon) to healthcare (Practo, PharmEasy) — are building data teams. India currently has a shortage of 200,000+ data professionals, making this one of the best fields to enter right now. Cities like Bangalore, Hyderabad, Pune, and Gurgaon have the highest concentration of data science jobs. Princeton is a well-established platform trusted by millions of learners worldwide. This particular resource has been selected by our editorial team based on: Content quality — comprehensive coverage with clear explanations Practical focus — emphasis on hands-on skills over pure theory Student outcomes — positive reviews and career success stories Indian relevance — content applicable to the Indian job market and interview patterns Updated curriculum — material reflects current industry practices and tools We regularly review and update our recommendations to ensure they remain relevant and high-quality.
Computer Science: Algorithms, Theory, and Machines
Princeton University (via Coursera)
This course introduces the broader discipline of computer science to people having basic familiarity with Java programming. It covers the second half of our book Computer Science: An Interdisciplinary Approach (the first half is covered in our Coursera course Computer Science: Programming with a Purpose, to be released in the fall of 2018). Our intent is to demystify computation and to build awareness about the substantial intellectual underpinnings and rich history of the field of computer science. First, we introduce classic algorithms along with scientific techniques for evaluating performance, in the context of modern applications. Next, we introduce classic theoretical models that allow us to address fundamental questions about computation, such as computability, universality, and intractability. We conclude with machine architecture (including machine-language programming and its relationship to coding in Java) and logic design (including a full CPU design built from the ground up). The course emphasizes the relationships between applications programming, the theory of computation, real computers, and the field's history and evolution, including the nature of the contributions of Boole, Shannon, Turing, von Neumann, and others. All the features of this course are available for free. People who are interested in digging deeper into the content may wish to obtain the textbook Computer Science: An Interdisciplinary Approach (upon which the course is based) or to visit the website introcs.cs.princeton.edu for a wealth of additional material. This course does not offer a certificate upon completion.
Алгоритмы, часть I
Princeton University (via Coursera)
Данный курс охватывает ключевые знания об алгоритмах и структурах данных, которыми обязан владеть каждый профессиональный программист. При этом акцент сделан на практических областях применения и научном анализе эффективности алгоритмов, реализованных на Java. В части I рассматриваются элементарные структуры данных, а также алгоритмы сортировки и поиска. В части II освещаются алгоритмы обработки графов и строк. Все компоненты этого курса предоставляются бесплатно. При этом по завершении не выдаются какие-либо сертификаты.
Computer Science: Programming with a Purpose
Princeton University (via Coursera)
The basis for education in the last millennium was “reading, writing, and arithmetic;” now it is reading, writing, and computing. Learning to program is an essential part of the education of every student, not just in the sciences and engineering, but in the arts, social sciences, and humanities, as well. Beyond direct applications, it is the first step in understanding the nature of computer science’s undeniable impact on the modern world. This course covers the first half of our book Computer Science: An Interdisciplinary Approach (the second half is covered in our Coursera course Computer Science: Algorithms, Theory, and Machines). Our intent is to teach programming to those who need or want to learn it, in a scientific context. We begin by introducing basic programming elements such as variables, conditionals, loops, arrays, and I/O. Next, we turn to functions, introducing key concepts such as recursion, modular programming, and code reuse. Then, we present a modern introduction to object-oriented programming. We use the Java programming language and teach basic skills for computational problem solving that are applicable in many modern computing environments. Proficiency in Java is a goal, but we focus on fundamental concepts in programming, not Java per se. All the features of this course are available for free. People who are interested in digging deeper into the content may wish to obtain the textbook Computer Science: An Interdisciplinary Approach (upon which the course is based) or to visit the website introcs.cs.princeton.edu for a wealth of additional material. This course does not offer a certificate upon completion.
Global Systemic Risk
Princeton University (via Coursera)
What is globalization and how does it work? How can we understand the process as a whole? How are the parts of the world linked? What are the risks of living in a world where “no one is in charge”? This course introduces students to systems thinking, network theory, and risk analysis and uses these tools to better understand the process of globalization. Focusing on trade, finance, and epidemiology, it analyzes potential challenges to the current global order. The course will be of interest to those studying global affairs, system dynamics, and world governance. It offers a set of heuristics that students can use to analyze contemporary global challenges. Linking the recording of Abbey Road to the COVID-19 pandemic provides new insights into the apparently chaotic world around us. Complex systems form the backbone of our increasingly interconnected and interdependent society. What were once more localized economies, supply chains, and social-ecological systems are now rapidly globalizing, and interacting with one another across countless spatial and temporal scales as technologies expand at ever greater velocities. These tightly coupled systems deliver greater efficiency and prosperity, but at the cost of greater fragility and the threat of catastrophic failure. This “global systemic risk” has implications in all functional domains affecting our daily lives—from the global financial system to healthcare, to critical infrastructure networks. Organized with 7-10 minute classes grouped together into longer modules, the course will have a linear “core” curriculum presented at the introductory level, with the potential for optional offshoots that give learners a more in-depth look into certain areas with more technical content.
Analytic Combinatorics
Princeton University (via Coursera)
Analytic Combinatorics teaches a calculus that enables precise quantitative predictions of large combinatorial structures. This course introduces the symbolic method to derive functional relations among ordinary, exponential, and multivariate generating functions, and methods in complex analysis for deriving accurate asymptotics from the GF equations. All the features of this course are available for free. People who are interested in digging deeper into the content may wish to obtain the textbook Analytic Combinatorics (upon which the course is based) or to visit the website ac.cs.princeton.edu for a wealth of additional material. This course does not offer a certificate upon completion.
Food Ethics
Princeton University (via Coursera)
We are what we eat--morally as well as molecularly. So how should concerns about animals, workers, the environment, and community inform our food choices? Can we develop viable foodways for growing populations while respecting race, ethnic, and religious differences? What does food justice look like in a global industrial food system where there are massive differences in resources, education, and food security? The main goal of this course is not to prescribe answers to these questions but to give students the tools required to reflect on them effectively. These tools include a knowledge of four leading ethical theories and a grasp of key empirical issues regarding food production, distribution, and consumption.
Analysis of Algorithms
Princeton University (via Coursera)
This course teaches a calculus that enables precise quantitative predictions of large combinatorial structures. In addition, this course covers generating functions and real asymptotics and then introduces the symbolic method in the context of applications in the analysis of algorithms and basic structures such as permutations, trees, strings, words, and mappings. All the features of this course are available for free. People who are interested in digging deeper into the content may wish to obtain the textbook Analysis of Algorithms, Second Edition (upon which the course is based) or to visit the website aofa.cs.princeton.edu for a wealth of additional material. This course does not offer a certificate upon completion.
비트코인 및 암호화폐 기술
Princeton University (via Coursera)
비트코인의 특수성을 이해하기 위해서는 기술적 측면에서 비트코인이 어떻게 작동하는지를 알아야 합니다. 비트코인과 관련하여 다음과 같은 핵심 질문을 다루게 됩니다. 비트코인은 어떻게 작동하나요? 비트코인에는 어떤 차별점이 있나요? 여러분의 비트코인 보안은 어떠한가요? 비트코인 유저들의 익명성은 얼마나 보장되나요? 비트코인의 가격을 결정하는 요소는 무엇인가요? 암호화폐 규제가 가능할까요? 미래는 어떻게 될까요? 이 강좌를 수강하고 나면, 비트코인과 기타 임호화폐에 대한 주장들을 접할 때 사실과 허구를 구별하는 데 필요한 모든 지식을 알게 됩니다. 또한 비트코인과 상호작용하는 보안 소프트웨어를 엔지니어링하는 데 필요한 기본 개념을 얻게 됩니다. 아울러 여러분의 프로젝트에 비트코인 개념을 통합할 수 있게 될 것입니다. 강사: Arvind Narayanan, Princeton University 모든 강의 내용은 무료로 제공됩니다. 강좌를 마치더라도 수료증 등을 제공하지는 않습니다.
Computer Architecture
Princeton University (via Coursera)
In this course, you will learn to design the computer architecture of complex modern microprocessors. All the features of this course are available for free. It does not offer a certificate upon completion.
Writing Case Studies: Science of Delivery
Princeton University (via edX)
Writing Case Studies: Science of Delivery
The Art of Structural Engineering: Bridges
Princeton University (via edX)
The Art of Structural Engineering: Bridges
The Art of Structural Engineering: Vaults
Princeton University (via edX)
The Art of Structural Engineering: Vaults
Global History Lab
Princeton University (via edX)
Global History Lab
Global History of Capitalism
Princeton University (via edX)
Global History of Capitalism
HOPE: Human Odyssey to Political Existentialism
Princeton University (via edX)
HOPE: Human Odyssey to Political Existentialism
Making Government Work in Hard Places
Princeton University (via edX)
Making Government Work in Hard Places
Constitutional Interpretation
Princeton University (via edX)
Constitutional Interpretation
Civil Liberties
Princeton University (via edX)
Civil Liberties
Effective Altruism
Princeton University (via Coursera)
Effective altruism is built on the simple but unsettling idea that living a fully ethical life involves doing the most good one can. In this course you will examine this idea's philosophical underpinnings; meet remarkable people who have restructured their lives in accordance with it; and think about how effective altruism can be put into practice in your own life. All the features of this course are available for free. It does not offer a certificate upon completion.
Paradoxes of War
Princeton University (via Coursera)
The Paradoxes of War teaches us to understand that war is not only a normal part of human existence, but is arguably one of the most important factors in making us who we are. Through this course, I hope that you will come to appreciate that war is both a natural expression of common human emotions and interactions and a constitutive part of how we cohere as groups. That is, war is paradoxically an expression of our basest animal nature and the exemplar of our most vaunted and valued civilized virtues. You will learn some basic military history and sociology in this course as a lens for the more important purpose of seeing the broader social themes and issues related to war. I want you to both learn about war, but more importantly, use it as way of understanding your everyday social world. So, for example, the discussion of war and gender will serve to start you thinking about how expectations of masculinity are created and our discussion of nationalism will make clear how easy “us-them” dichotomies can be established and (ab)used. I will suggest some readings for you to complement the class and assign some activities through which you will be able to apply the theoretical insights from the course to your observations of everyday life. At the end of the course, you will start to see war everywhere and come to appreciate how much it defines our life. All the features of this course are available for free. It does not offer a certificate upon completion.
Bitcoin and Cryptocurrency Technologies
Princeton University (via Coursera)
To really understand what is special about Bitcoin, we need to understand how it works at a technical level. We’ll address the important questions about Bitcoin, such as: How does Bitcoin work? What makes Bitcoin different? How secure are your Bitcoins? How anonymous are Bitcoin users? What determines the price of Bitcoins? Can cryptocurrencies be regulated? What might the future hold? After this course, you’ll know everything you need to be able to separate fact from fiction when reading claims about Bitcoin and other cryptocurrencies. You’ll have the conceptual foundations you need to engineer secure software that interacts with the Bitcoin network. And you’ll be able to integrate ideas from Bitcoin in your own projects. Course Lecturers: Arvind Narayanan, Princeton University All the features of this course are available for free. It does not offer a certificate upon completion.
Algorithms, Part I
Princeton University (via Coursera)
This course covers the essential information that every serious programmer needs to know about algorithms and data structures, with emphasis on applications and scientific performance analysis of Java implementations. Part I covers elementary data structures, sorting, and searching algorithms. Part II focuses on graph- and string-processing algorithms. All the features of this course are available for free. People who are interested in digging deeper into the content may wish to obtain the textbook Algorithms, Fourth Edition (upon which the course is based) or visit the website algs4.cs.princeton.edu for a wealth of additional material. This course does not offer a certificate upon completion.
Imagining Other Earths
Princeton University (via Coursera)
Are we alone? This course introduces core concepts in astronomy, biology, and planetary science that enable the student to speculate scientifically about this profound question and invent their own solar systems. All the features of this course are available for free. It does not offer a certificate upon completion.
Algorithms, Part II
Princeton University (via Coursera)
This course covers the essential information that every serious programmer needs to know about algorithms and data structures, with emphasis on applications and scientific performance analysis of Java implementations. Part I covers elementary data structures, sorting, and searching algorithms. Part II focuses on graph- and string-processing algorithms. All the features of this course are available for free. People who are interested in digging deeper into the content may wish to obtain the textbook Algorithms, Fourth Edition (upon which the course is based) or visit the website algs4.cs.princeton.edu for a wealth of additional material. This course does not offer a certificate upon completion.