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Data Science with Python: Numpy Basics

Introduces the basics of Numpy, a numerical computing library in Python, covering advantages, memory layout, operations, and linear algebra functions.

Numpy Tutorial

Covers the basics of Numpy, including importing the library, creating arrays, and performing linear algebra operations.

Python/NumPy Primer

Introduces Python basics and NumPy for scientific computing, covering data types, functions, arrays, indexing, and common operations.

NumPy Arrays and Graphical Representations: Introduction

Covers NumPy arrays and their graphical representations using Matplotlib, focusing on array creation, manipulation, and visualization techniques.

Python Complement: Numpy, Scipy, Matplotlib

Covers advanced Python topics like numpy operations, scipy linear algebra, and matplotlib for creating figures.

Excitonic Devices: Modeling and Applications

Covers excitonic device modeling and exciton control using 2D materials.

Regression: High Dimensions

Explores linear regression in high dimensions and practical house price prediction from a dataset.

Data Science for Engineers: Part 2

Explores data manipulation, exploration, and visualization in data science projects using Python.

Introduction to R Programming for Genetics & Genomics

Introduces a course on Genetics & Genomics, focusing on R programming with interactive exercises.

Data Science with Python: Modules and Numpy

Introduces Python modules and NumPy for efficient array operations and linear algebra in data science.

General Introduction to Data Science

Offers a comprehensive introduction to Data Science, covering Python, Numpy, Pandas, Matplotlib, and Scikit-learn, with a focus on practical exercises and collaborative work.

Introduction to NumPy: Part 1

Covers the basics of NumPy, including array construction and manipulation.

NumPy: Array Manipulation and Broadcasting

Covers array creation, indexing, manipulation, and broadcasting using NumPy in Python.

Introduction to NumPy and Matplotlib for Scientific Computing

Introduces NumPy and Matplotlib, essential tools for scientific computing and data visualization in Python.

Data Science: Python for Engineers - Part II

Explores data wrangling, numerical data handling, and scientific visualization using Python for engineers.

Introduction to NumPy: Basics of Scientific Computing

Introduces NumPy, focusing on array creation, manipulation, and its advantages for scientific computing.

Elements of Computational Complexity

Introduces computational complexity, decision problems, quantum complexity, and probabilistic algorithms, including NP-hard and NP-complete problems.

Python Recap: Modules and Objects

Covers Python modules, objects, and data containers like lists and NumPy arrays.

Block Pulled by a Spring

Explores the dynamics of a block pulled by a spring under various conditions.

Semiconductor Devices II: Defects Engineering

Covers the analysis of measurements and defects engineering in semiconductor devices, including density of states and defect probing.