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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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Courseware
This course is an introduction to principles and techniques of visual communication, and provides opportunities for science and engineering majors to acquire practical skills in the visual computer arts, in a studio environment. Students will learn how to create graphics for print and web, animations, and interactive media, and how to use these techniques to effectively communicate scientific and engineering concepts for learning and teaching. This class involves three hands-on creative projects, which will be presented in class.
- Subjects:
- Computing and Visualisation
- Keywords:
- Information visualization
- Resource Type:
- Courseware
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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 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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Others
Welcome to the Coding Train with Daniel Shiffman! A community dedicated to learning creative coding with beginner-friendly tutorials and projects on YouTube and more.
- Subjects:
- Computing
- Keywords:
- Coding theory Computer programming
- Resource Type:
- Others
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Video
Statistics, Machine Learning and Data Science can sometimes seem like very scary topics, but since each technique is really just a combination of small and simple steps, they are actually quite simple. My goal with StatQuest is to break down the major methodologies into easy to understand pieces. That said, I don't dumb down the material. Instead, I build up your understanding so that you are smarter.
- Course related:
- HTI34016 Introduction to Clinical Research
- Subjects:
- Computing and Mathematics and Statistics
- Keywords:
- Statistics Mathematical analysis Data mining Machine learning
- Resource Type:
- Video
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Video
In this lecture, we consider strategies for adversarial games such as chess. We discuss the minimax algorithm, and how alpha-beta pruning improves its efficiency. We then examine progressive deepening, which ensures that some answer is always available.
- Course related:
- COMP4431 Artificial Intelligence
- Subjects:
- Computing and Mathematics and Statistics
- Keywords:
- Artificial intelligence
- Resource Type:
- Video
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e-journal
In this journal platform, you can find the articles which published under the open license. The journal including the disciplines:
Biomedical & Life Science
Business & Economics
Chemistry & Materials Science
Computer Science & Communication
Earth & Environmental Science
Engineering
Medicine & Healthcare
Physics & Mathematics
Social Science & Humanities
- Subjects:
- Health Sciences, Environmental Sciences, Physics, Economics, Chemistry, Computing, Mathematics and Statistics, and Biology
- Keywords:
- Science Periodicals Industrial management Computer science Physics Mathematics Life sciences Economics Technology Chemistry Social sciences Environmental sciences Engineering Materials science Medicine
- Resource Type:
- e-journal
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Others
Distinguish yourself by learning to work with text data.
- Subjects:
- Computing
- Keywords:
- Python (Computer program language) Natural language processing (Computer science)
- Resource Type:
- Others
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e-book
The development of modern information systems is a demanding task. New technologies and tools are designed, implemented and presented in the market on a daily bases. User needs change dramatically fast and the IT industry copes to reach the level of efficiency and adaptability for its systems in order to be competitive and up-to-date. Thus, the realization of modern information systems with great characteristics and functionalities implemented for specific areas of interest is a fact of our modern and demanding digital society and this is the main scope of this book. Therefore, this book aims to present a number of innovative and recently developed information systems. It is titled "Modern Information Systems" and includes 8 chapters. This book may assist researchers on studying the innovative functions of modern systems in various areas like health, telematics, knowledge management, etc. It can also assist young students in capturing the new research tendencies of the information systems' development.
- Subjects:
- Environmental Sciences and Computing
- Keywords:
- Medical informatics Knowledge management Management information systems
- Resource Type:
- e-book
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e-journal
Maxwell Scientific Publication Corp. operates as an independent science and technology publisher with a global reputation for quality products and services to contribute to the advancement of research knowledge. In this journal platform, you can find the articles which published under the open license. The journal including the disciplines:
Agricultural Sciences
Business Management & Economics Computer & Information Technology
Engineering
Food Science and Technology
Mathematics
Medicines
Social Sciences
- Subjects:
- Health Sciences, Environmental Sciences, Physics, Economics, Chemistry, Computing, Mathematics and Statistics, Biology, Management, Medicine, and Food Science
- Keywords:
- Statistics Science Periodicals Industrial management Food science Agriculture Fisheries Medicine Mathematics Life sciences Economics Technology Dairying Social sciences Environmental sciences Engineering Aquatic sciences Information technology Food industry trade
- Resource Type:
- e-journal
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Video
An example of using least squares to do data fitting. In this example, we demonstrate how to 1) fit a straight line using ordinary least-squares method, and 2) estimate value of a new input based on the fitted line.
- Course related:
- LSGI3242A Digital Terrain Modelling
- Subjects:
- Computing and Mathematics and Statistics
- Keywords:
- Least squares Regression analysis
- Resource Type:
- Video
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MOOC
This course covers a wide variety of topics in machine learning and statistical modeling. While mathematical methods and theoretical aspects will be covered, the primary goal is to provide students with the tools and principles needed to solve the data science problems found in practice. This course also serves as a foundation on which more specialized courses and further independent study can build. This course was designed as part of the core curriculum for the Center for Data Science's Masters degree in Data Science. Other interested students who satisfy the prerequisites are welcome to take the class as well. Note that class is intended as a continuation of DS-GA-1001 Intro to Data Science, which covers some important, fundamental data science topics that may not be explicitly covered in this DS-GA class (e.g. data cleaning, cross-validation, and sampling bias).
- Course related:
- LGT6801 Guided Study in Logistics I
- Subjects:
- Computing and Mathematics and Statistics
- Keywords:
- Big data Data mining Machine learning Mathematical statistics -- Data processing
- Resource Type:
- MOOC
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MOOC
The Machine Learning Specialization is a foundational online program created in collaboration between DeepLearning.AI and Stanford Online. This beginner-friendly program will teach you the fundamentals of machine learning and how to use these techniques to build real-world AI applications.
This Specialization is taught by Andrew Ng, an AI visionary who has led critical research at Stanford University and groundbreaking work at Google Brain, Baidu, and Landing.AI to advance the AI field.
This 3-course Specialization is an updated version of Andrew’s pioneering Machine Learning course, rated 4.9 out of 5 and taken by over 4.8 million learners since it launched in 2012.
It provides a broad introduction to modern machine learning, including supervised learning (multiple linear regression, logistic regression, neural networks, and decision trees), unsupervised learning (clustering, dimensionality reduction, recommender systems), and some of the best practices used in Silicon Valley for artificial intelligence and machine learning innovation (evaluating and tuning models, taking a data-centric approach to improving performance, and more.) By the end of this Specialization, you will have mastered key concepts and gained the practical know-how to quickly and powerfully apply machine learning to challenging real-world problems. If you’re looking to break into AI or build a career in machine learning, the new Machine Learning Specialization is the best place to start.
- Course related:
- AAE5103 Artificial Intelligence in Aviation Industry
- Subjects:
- Computing
- Keywords:
- Machine learning Artificial intelligence
- Resource Type:
- MOOC
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Others
Extract human-understandable insights from any machine learning model.
- Subjects:
- Computing
- Keywords:
- Python (Computer program language) Machine learning
- Resource Type:
- Others
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Others
Kaggle offers a no-setup, customizable, Jupyter Notebooks environment. Access GPUs at no cost to you and a huge repository of community published data & code. Inside Kaggle you’ll find all the code & data you need to do your data science work. Use over 50,000 public datasets and 400,000 public notebooks to conquer any analysis in no time.
- Subjects:
- Computing
- Keywords:
- Machine learning Artificial intelligence Big data
- Resource Type:
- Others
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Others
Learn SQL for working with databases, using Google BigQuery to scale to massive datasets.
- Subjects:
- Computing
- Keywords:
- Database management SQL (Computer program language)
- Resource Type:
- Others
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Others
Learn to handle missing values, non-numeric values, data leakage and more. Your models will be more accurate and useful.
- Subjects:
- Computing
- Keywords:
- Python (Computer program language) Machine learning
- Resource Type:
- Others
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Others
Learn to code, design, and more—all on your own time
- Subjects:
- Computing
- Keywords:
- Coding theory Mobile apps Computer programming
- Resource Type:
- Others
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Video
When two people join a dating website they are matched according to shared interests and how they answer a number of personal questions. But how do sites calculate the likelihood of a successful relationship? Christian Rudder one of the founders of popular dating site OKCupid details the algorithm behind 'hitting it off.'
- Subjects:
- Computing and Mathematics and Statistics
- Keywords:
- Dating services Computer algorithms Online dating
- Resource Type:
- Video