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.

Data analysis is a process for inspecting, consolidating, transforming, and making sense of data in a way that guides the decision-making process. Scala has emerged as an important tool for efficiently performing various data analysis tasks. So, if you're interested to load raw datasets with Spark, and perform exploratory data analysis on those via plotting Scala libraries, then go for this Learning Path. Packt's Video Learning Paths are a series of individual video products put together in a logical and stepwise manner such that each video builds on the skills learned in the video before it. Let's take a quick look at your learning journey.

Medical Neuroscience explores the functional organization and neurophysiology of the human central nervous system, while providing a neurobiological framework for understanding human behavior. In this course, you will discover the organization of the neural systems in the brain and spinal cord that mediate sensation, motivate bodily action, and integrate sensorimotor signals with memory, emotion and related faculties of cognition. The overall goal of this course is to provide the foundation for understanding the impairments of sensation, action and cognition that accompany injury, disease or dysfunction in the central nervous system. The course will build upon knowledge acquired through prior studies of cell and molecular biology, general physiology and human anatomy, as we focus primarily on the central nervous system. This online course is designed to include all of the core concepts in neurophysiology and clinical neuroanatomy that would be presented in most first-year neuroscience courses in schools of medicine.

Subtitles are available for Introduction, Case Study and Design Patterns Concepts. Design and architecture of any software/solution provides the base and makes it flexible, extensible for future requirements. A good designed software/solution makes it easy to understand and maintain. Design patterns are known as best practices to design software for problems which are repeated in nature. This course "Design Patterns Through Python" gives you understanding of all 23 patterns described in Gang Of Four book - "Design Patterns: Elements of Reusable Object-Oriented Software", by Gamma, Helm, Johnson and Vlissides.

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The corporate training market is over $200 billion around the world[1] and it's going through a revolution. While we often think of training as programs or courses, a new paradigm has arrived, one I call "Learning in the Flow of Work." The corporate training industry has been around for decades and it has always been impacted by new technology. As the following chart shows, over the last 20 years we've been through four evolutions, each driven by technological and economic change. In the 1970s and 1980s, when I started my career, we learned in classrooms. The technology was slide projectors and "foils" (plastic laminated slides).

Welcome to the Advanced Linear Models for Data Science Class 2: Statistical Linear Models. This class is an introduction to least squares from a linear algebraic and mathematical perspective. Before beginning the class make sure that you have the following: - A basic understanding of linear algebra and multivariate calculus. After taking this course, students will have a firm foundation in a linear algebraic treatment of regression modeling. This will greatly augment applied data scientists' general understanding of regression models.

This course provides an introduction to basic computational methods for understanding what nervous systems do and for determining how they function. We will explore the computational principles governing various aspects of vision, sensory-motor control, learning, and memory. Specific topics that will be covered include representation of information by spiking neurons, processing of information in neural networks, and algorithms for adaptation and learning. We will make use of Matlab/Octave/Python demonstrations and exercises to gain a deeper understanding of concepts and methods introduced in the course. The course is primarily aimed at third- or fourth-year undergraduates and beginning graduate students, as well as professionals and distance learners interested in learning how the brain processes information.

The 2017 ASSISTments Data Mining competition aims to use data from a longitudinal study for predicting a brand-new outcome of students which had never been studied before by the educational data mining research community. Specifically, it facilitates research in developing predictive models that predict whether the first job of a student out of college belongs to a STEM (the acronym for science, technology, engineering, and mathematics) field. This is based on the student's learning history on the ASSISTments blended learning platform in the form of extensive clickstream data gathered during the middle school years. To tackle this challenge, we first estimate the expected knowledge state of students with respect to different mathematical skills using a deep knowledge tracing (DKT) model and an enhanced DKT (DKT+) model. We then combine the features corresponding to the DKT/DKT+ expected knowledge state with other features extracted directly from the student profile in the dataset to train several machine learning models for the STEM/non-STEM job prediction. Our experiments show that models trained with the combined features generally perform better than the models trained with the student profile alone. Detailed analysis of the student's knowledge state reveals that, when compared with non-STEM students, STEM students generally show a higher mastery level and a higher learning gain in mathematics.

Have you ever faced challenges in understanding language and planning sentences while performing Natural Language Processing? Do you wish to overcome these problems and go beyond the basic techniques like bag-of-words? This course is designed with advanced solutions that will take you from newbie to pro in performing Natural Language Processing with NLTK. In this course, you will come across various concepts covering natural language understanding, Natural Language Processing, and syntactic analysis. It consists of everything you need to efficiently use NLTK to implement text classification, identify parts of speech, tag words, and more.