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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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Video
This video presents an overview of the Fourier Transform, which is one of the most important transformations in all of mathematical physics and engineering. This series will introduce the analytic theory of the Fourier Transform, along with the Fast Fourier Transform (FFT) algorithm for efficient computations. We will explore lots of applications in image compression, audio analysis, and solving partial differential equations.
- Course related:
- EIE4413 Digital Signal Processing, EIE3331 Communication Fundamentals, and EE3312 Linear Systems
- Subjects:
- Mathematics and Statistics
- Keywords:
- Fourier transformations
- Resource Type:
- Video
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e-book
This is a new approach to an introductory statistical inference textbook, motivated by probability theory as logic. It is targeted to the typical Statistics 101 college student, and covers the topics typically covered in the first semester of such a course. It is freely available under the Creative Commons License, and includes a software library in Python for making some of the calculations and visualizations easier.
- Subjects:
- Mathematics and Statistics
- Keywords:
- Mathematical statistics 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
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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Courseware
How do populations grow? How do viruses spread? What is the trajectory of a glider? Many real-life problems can be described and solved by mathematical models. In this course, you will form a team with another student and work in a project to solve a real-life problem. You will learn to analyze your chosen problem, formulate it as a mathematical model (containing ordinary differential equations), solve the equations in the model, and validate your results. You will learn how to implement Euler’s method in a Python program. If needed, you can refine or improve your model, based on your first results. Finally, you will learn how to report your findings in a scientific way. This course is mainly aimed at Bachelor students from Mathematics, Engineering and Science disciplines. However it will suit anyone who would like to learn how mathematical modeling can solve real-world problems.
- Subjects:
- Mathematics and Statistics
- Keywords:
- Mathematical models
- Resource Type:
- Courseware