He has held a position as Associate Professor in mathematics, as well as a consultant and data scientist in a variety of industries including science, finance, marketing, people analytics and health, among others. He is the author of Data Science and Analytics with Python and Advanced Data Scie...
A comprehensive overview of essential statistical concepts, useful statistical methods, data visualization, and modern computing tools for the climate sciences and many others such as geography and environmental engineering. It is an invaluable reference for students and researchers in climatology and its ...
namely similarity and distance measures, and provides a brief exploration of some advanced and applied topics, predominantly under an exploratory data analysis viewpoint, such as clustering. After this, we move on to Python tutorials covering data visualization techniques....
Visualization Seeing data Maps Seeing geographic data Infographics Explaining data Networks Connecting data Statistics Analyzing data Software Working with data Sources Getting data Design Making data readable Get the Book Visualize This: The FlowingData Guide to Design, Visualization, and Statistics ...
Matplotlib is a third-party library for data visualization. It works well in combination with NumPy, SciPy, and pandas. Note that, in many cases, Series and DataFrame objects can be used in place of NumPy arrays. Often, you might just pass them to a NumPy or SciPy statistical function. ...
Want to share your content on python-bloggers? click here. ShareTweet This week, I released Techtonique web app, a tool designed to help you make informed, data-driven decisions using Mathematics, Statistics, Machine Learning, and Data Visualization. As of September 2024, the tool is in its...
Matplotlib is a third-party library for data visualization. It works well in combination with NumPy, SciPy, and pandas. Note that, in many cases, Series and DataFrame objects can be used in place of NumPy arrays. Often, you might just pass them to a NumPy or SciPy statistical function. ...
In your courses you will learn core concepts such as statistics, programming languages (Python/R), data manipulation, and visualization. Along the way, your mentor will provide clarity, resources, and examples to boost your understanding.
Data Understanding: Requires the use of summary statistics and data visualization. Data Cleaning: Requires the use of outlier detection, imputation and more. Data Selection: Requires the use of data sampling and feature selection methods. Data Preparation: Requires the use of data transforms, scaling...
Statistics is a powerful tool for data analysis, visualization, and inference. Python is a popular programming language that offers a rich set of libraries and frameworks for statistical computing. Together, they can help you solve real-world problems and make informed decisions based on data. This...