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MOOC
本系列課程從零開始,教授一般認為最適合初學者的程式語言「Python」,目標是讓大家在完成本課程之後,一方面獲得程式設計與運算思維的基本概念,一方面也能獨立寫出能解決運算問題的程式。本課程和一般程式設計課程最不同的地方,在於它是以解決商管領域的運算問題為導向,因此課程不會只含有質因數分解、紅球白球排列組合、三角不等式、萬年曆、數字排序等傳統程式設計課程的範例與作業,而是包含了生產、物流、存貨、投資、定價等問題,讓大家在學會程式設計的同時,也直接體會程式設計與資訊技術在商管領域的各種應用。 本系列課程共分為三門課程。本門課程做為第一門課程,將介紹程式設計的基本觀念、Python 語言的基本語法、選擇、迴圈、清單,並以作業管理領域的一些簡單演算法作結。
- Course related:
- MM2425 Introduction to Business Analytics
- Subjects:
- Computing
- Keywords:
- Python (Computer program language)
- Resource Type:
- MOOC
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Others
In these comprehensive video courses, created by Santiago Basulto, you will learn the whole process of data analysis. You'll be reading data from multiple sources (CSV, SQL, Excel), process that data using NumPy and Pandas, and visualize it using Matplotlib and Seaborn, Additionally, we've included a thorough Jupyter Notebook course, and a quick Python reference to refresh your programming skills.
- Course related:
- AMA1600 Fundamentals of AI and Data Analytics and AMA1751 Linear Algebra
- Subjects:
- Mathematics and Statistics and Computing
- Keywords:
- Computer programming Computer science Python (Computer program language)
- Resource Type:
- Others
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Others
freeCodeCamp is a proven path to your first software developer job. More than 40,000 people have gotten developer jobs after completing this – including at big companies like Google and Microsoft. If you are new to programming, we recommend you start at the beginning and earn these certifications in order. To earn each certification, build its 5 required projects and get all their tests to pass.You can add these certifications to your résumé or LinkedIn. But more important than the certifications is the practice you get along the way.If you feel overwhelmed, that is normal. Programming is hard. Practice is the key. Practice, practice, practice. And this curriculum will give you thousands of hours of hands-on programming practice. And if you want to learn more math and computer science theory, we also have thousands of hours of video courses on freeCodeCamp's YouTube channel. If you want to get a developer job or freelance clients, programming skills will be just part of the puzzle. You also need to build your personal network and your reputation as a developer. You can do this on Twitter and GitHub, and also on the freeCodeCamp forum. Happy coding.
- Subjects:
- Computing
- Keywords:
- Computer programming Programming languages (Electronic computers) Coding theory
- Resource Type:
- Others
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Others
W3Schools is optimized for learning, testing, and training. Examples might be simplified to improve reading and basic understanding. Tutorials, references, and examples are constantly reviewed to avoid errors, but we cannot warrant full correctness of all content.
- Subjects:
- Computing
- Keywords:
- Web publishing Web site development Web sites -- Design
- Resource Type:
- Others
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e-book
Think Stats is an introduction to Probability and Statistics for Python programmers. Think Stats emphasizes simple techniques you can use to explore real data sets and answer interesting questions. The book presents a case study using data from the National Institutes of Health. Readers are encouraged to work on a project with real datasets. If you have basic skills in Python, you can use them to learn concepts in probability and statistics. Think Stats is based on a Python library for probability distributions (PMFs and CDFs). Many of the exercises use short programs to run experiments and help readers develop understanding.
- Subjects:
- Computing and Mathematics and Statistics
- Keywords:
- Textbooks Statistics -- Computer programs
- Resource Type:
- e-book
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e-book
Think Python is a concise introduction to software design using the Python programming language. Intended for people with no programming experience, this book starts with the most basic concepts and gradually adds new material. Some of the ideas students find most challenging, like recursion and object-oriented programming, are divided into a sequence of smaller steps and introduced over the course of several chapters. This textbook has been used in classes atBard College,Olin College of Engineering, University of California, Santa Barbara, University of Maine, University of Northern Colorado.
- Subjects:
- Computing
- Keywords:
- Computer programming Python (Computer program language) Textbooks Programming languages (Electronic computers)
- Resource Type:
- e-book
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e-book
Think DSP is an introduction to Digital Signal Processing in Python. The premise of this book (and the other books in the Think X series) is that if you know how to program, you can use that skill to learn other things. The author is writing this book because he thinks the conventional approach to digital signal processing is backward: most books (and the classes that use them) present the material bottom-up, starting with mathematical abstractions like phasors.
- Subjects:
- Electrical Engineering and Computing
- Keywords:
- Signal processing -- Digital techniques -- Data processing Python (Computer program language) Textbooks
- Resource Type:
- e-book
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e-book
This book is about complexity science, data structures and algorithms, intermediate programming in Python, and the philosophy of science: Data structures and algorithms: A data structure is a collection that contains data elements organized in a way that supports particular operations. For example, a dictionary organizes key-value pairs in a way that provides fast mapping from keys to values, but mapping from values to keys is generally slower. An algorithm is a mechanical process for performing a computation. Designing efficient programs often involves the co-evolution of data structures and the algorithms that use them. For example, the first few chapters are about graphs, a data structure that is a good implementation of a graph---nested dictionaries---and several graph algorithms that use this data structure. Python programming: This book picks up where Think Python leaves off. I assume that you have read that book or have equivalent knowledge of Python. As always, I will try to emphasize fundmental ideas that apply to programming in many languages, but along the way you will learn some useful features that are specific to Python. Computational modeling: A model is a simplified description of a system that is useful for simulation or analysis. Computational models are designed to take advantage of cheap, fast computation. Philosophy of science: The models and results in this book raise a number of questions relevant to the philosophy of science, including the nature of scientific laws, theory choice, realism and instrumentalism, holism and reductionism, and Bayesian epistemology. This book focuses on discrete models, which include graphs, cellular automata, and agent-based models. They are often characterized by structure, rules and transitions rather than by equations. They tend to be more abstract than continuous models; in some cases there is no direct correspondence between the model and a physical system. Complexity science is an interdisciplinary field---at the intersection of mathematics, computer science and physics---that focuses on these kinds of models. That's what this book is about.
- Subjects:
- Computing
- Keywords:
- Computational complexity Python (Computer program language) Textbooks
- Resource Type:
- e-book
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e-book
Think Bayes is an introduction to Bayesian statistics using computational methods. The premise of this book, and the other books in the Think X series, is that if you know how to program, you can use that skill to learn other topics. Most books on Bayesian statistics use mathematical notation and present ideas in terms of mathematical concepts like calculus. This book uses Python code instead of math, and discrete approximations instead of continuous mathematics. As a result, what would be an integral in a math book becomes a summation, and most operations on probability distributions are simple loops. I think this presentation is easier to understand, at least for people with programming skills. It is also more general, because when we make modeling decisions, we can choose the most appropriate model without worrying too much about whether the model lends itself to conventional analysis. Also, it provides a smooth development path from simple examples to real-world problems.
- Subjects:
- Computing and Mathematics and Statistics
- Keywords:
- Bayesian statistical decision theory Python (Computer program language) Textbooks
- Resource Type:
- e-book
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e-book
This text is a practical guide for linguists, and programmers, who work with data in multilingual computational environments. We introduce the basic concepts needed to understand how writing systems and character encodings function, and how they work together at the intersection between the Unicode Standard and the International Phonetic Alphabet. Although these standards are often met with frustration by users, they nevertheless provide language researchers and programmers with a consistent computational architecture needed to process, publish and analyze lexical data from the world's languages. Thus we bring to light common, but not always transparent, pitfalls which researchers face when working with Unicode and IPA. Having identified and overcome these pitfalls involved in making writing systems and character encodings syntactically and semantically interoperable (to the extent that they can be), we created a suite of open-source Python and R tools to work with languages using orthography profiles that describe author- or document-specific orthographic conventions. In this cookbook we describe a formal specification of orthography profiles and provide recipes using open source tools to show how users can segment text, analyze it, identify errors, and to transform it into different written forms for comparative linguistics research.
- Subjects:
- Language and Languages and Computing
- Keywords:
- Language languages -- Orthography spelling Unicode (Computer character set) Textbooks
- Resource Type:
- e-book