1. Python and Statistics for Financial Analysis
Offered by The Hong Kong University of Science and Technology
Course Overview: https://youtu.be/JgFV5qzAYno
Python is now becoming the number 1 programming language for data science. Due to python’s simplicity and high readability, it is gaining its importance in the financial industry. The course combines both python coding and statistical concepts and applies into analyzing financial data, such as stock data.
2. Python Programming: A Concise Introduction
Offered by Wesleyan University
The goal of the course is to introduce students to Python Version 3.x programming using hands on instruction. It will show how to install Python and use the Spyder IDE (Integrated Development Environment) for writing and debugging programs. The approach will be to present an example followed by a small exercise where the learner tries something similar to solidify a concept. At the end of each module there will be an exercise where the student is required to write simple programs and submit them for grading. It is intended for students with little or no programming background, although students with such a background should be able to move forward at their preferred pace.
The course is four modules long and is designed to be completed in four weeks.
3. Build a Data Science Web App with Streamlit and Python
Welcome to this hands-on project on building your first data science web app with the Streamlit library in Python. By the end of this project, you are going to be comfortable with using Python and Streamlit to build beautiful and interactive web apps with zero web development experience! We are going to load, explore, visualize and interact with data, and generate dashboards in less than 100 lines of Python code!
4. Foundations of Data Science: K-Means Clustering in Python
Offered by University of London
Organisations all around the world are using data to predict behaviours and extract valuable real-world insights to inform decisions. Managing and analysing big data has become an essential part of modern finance, retail, marketing, social science, development and research, medicine and government.
You will consider these fundamental concepts on an example data clustering task, and you will use this example to learn basic programming skills that are necessary for mastering Data Science techniques. During the course, you will be asked to do a series of mathematical and programming exercises and a small data clustering project for a given dataset.
5. Problem Solving, Python Programming, and Video Games
Offered by University of Alberta
This course is an introduction to computer science and programming in Python. Upon successful completion of this course, you will be able to:
- Take a new computational problem and develop a plan to solve it through problem understanding and decomposition.
- Follow a design creation process that includes specifications, algorithms, and testing.
- Code, test, and debug a program in Python, based on your design.
Important computer science concepts such as problem solving (computational thinking), problem decomposition, algorithms, abstraction, and software quality are emphasized throughout. The Python programming language and video games are used to demonstrate computer science concepts in a concrete and fun manner. However, a learner can take the knowledge and skills from this course and apply them to non-game problems, other programming languages, and other computer science courses.
6. Data Processing Using Python
Offered by Nanjing University
This course is mainly for non-computer majors. It starts with the basic syntax of Python, to how to acquire data in Python locally and from network, to how to present data, then to how to conduct basic and advanced statistic analysis and visualization of data, and finally to how to design a simple GUI to present and process data, advancing level by level.
This course, as a whole, based on Finance data and through the establishment of popular cases one after another, enables learners to more vividly feel the simplicity, elegance, and robustness of Python. Also, it discusses the fast, convenient and efficient data processing capacity of Python in humanities and social sciences fields like literature, sociology and journalism and science and engineering fields like mathematics and biology, in addition to business fields. Similarly, it may also be flexibly applied into other fields
7. Global Warming II: Create Your Own Models in Python
Offered by The University of Chicago
This class provides a series of Python programming exercises intended to explore the use of numerical modeling in the Earth system and climate sciences. The scientific background for these models is presented in a companion class, Global Warming I: The Science and Modeling of Climate Change. This class assumes that you are new to Python programming (and this is indeed a great way to learn Python!), but that you will be able to pick up an elementary knowledge of Python syntax from another class or from on-line tutorials.
8. Biology Meets Programming: Bioinformatics for Beginners
Offered by University of California San Diego
Are you interested in learning how to program (in Python) within a scientific setting?
This course will cover algorithms for solving various biological problems along with a handful of programming challenges helping you implement these algorithms in Python. It offers a gently-paced introduction to our Bioinformatics Specialization, preparing learners to take the first course in the Specialization, “Finding Hidden Messages in DNA”. Each of the four weeks in the course will consist of two required components. First, an interactive textbook provides Python programming challenges that arise from real biological problems. If you haven’t programmed in Python before, not to worry! We provide “Just-in-Time” exercises from the Codecademy Python track (https://www.codecademy.com/learn/python). And each page in our interactive textbook has its own discussion forum, where you can interact with other learners. Second, each week will culminate in a summary quiz.
9. Audio Signal Processing for Music Applications
Offered by Universitat Pompeu Fabra of Barcelona
and Stanford University
In this course you will learn about audio signal processing methodologies that are specific for music and of use in real applications. We focus on the spectral processing techniques of relevance for the description and transformation of sounds, developing the basic theoretical and practical knowledge with which to analyze, synthesize, transform and describe audio signals in the context of music applications.
10. Learn to Program: Crafting Quality Code
Offered by University of Toronto
Not all programs are created equal. In this course, we’ll focus on writing quality code that runs correctly and efficiently. We’ll design, code and validate our programs and learn how to compare programs that are addressing the same task.
11. Data-driven Astronomy using Python
offered by The University of Sydney
Science is undergoing a data explosion, and astronomy is leading the way. Modern telescopes produce terabytes of data per observation, and the simulations required to model our observable Universe push supercomputers to their limits. To analyse this data scientists need to be able to think computationally to solve problems. In this course you will investigate the challenges of working with large datasets: how to implement algorithms that work; how to use databases to manage your data; and how to learn from your data with machine learning tools. The focus is on practical skills — all the activities will be done in Python 3, a modern programming language used throughout astronomy.
12. Data Science in Stratified Healthcare and Precision Medicine using Python
Offered by The University of Edinburgh
An increasing volume of data is becoming available in biomedicine and healthcare, from genomic data, to electronic patient records and data collected by wearable devices. Recent advances in data science are transforming the life sciences, leading to precision medicine and stratified healthcare.
In this course, you will learn about some of the different types of data and computational methods involved in stratified healthcare and precision medicine. You will have a hands-on experience of working with such data. And you will learn from leaders in the field about successful case studies.
(i) Sequence Processing,
(ii) Image Analysis,
(iii) Network Modelling,
(iv) Probabilistic Modelling,
(v) Machine Learning,
(vi) Natural Language Processing,
(vii) Process Modelling and
(viii) Graph Data