With the recent advances in data science and artificial intelligence in every industry, including transportation infrastructure and highway operations, it is important for roadway professionals to learn the fundamental components of data science to implement them in their day-to-day practice. Contrary to the general belief, in order to understand and implement these tool and techniques in roadway construction, operations and management, no prior coding or computer programming experience is needed. The main goal of this online training is to introduce the fundamentals of practical data science relevant to transportation and roadway experts. Various aspects, such as the use of different data processing tools, data visualization, data mining and artificial intelligence will be discussed through online hands-on tutorials. Participants will be guided through various interactive course modules and hands-on tutorials to develop skills and knowledge to employ various data science tools on real-world example datasets.

Understanding both these perspectives is critical for virtually all data science analysis algorithms. Ignoring the primary key column (this is not really a numeric feature, so makes less sense to…

Have you ever looked up to a starry night sky, seen a shooting star and made a wish? Well, look again and look carefully. Are you sure it is a shooting star, or could it be something else? Can you tell for sure? Well, maybe if your wish comes true, then you can tell with certainty that it was a shooting star, no? This Fall semester at New College of Florida, 7 students in the Applied Data Science master's program joined the world-wide effort in analyzing data collected from cameras watching the night skies.

Practical Data Science with Python teaches you core data science concepts, with real-world and realistic examples, and strengthens your grip on the basic as well as advanced principles of data preparation and storage, statistics, probability theory, machine learning, and Python programming, helping you build a solid ... Are you aspiring to become a Data Scientist or Machine Learning Engineer? In this course, you will learn about core concepts of Data Science, Exploratory Data Analysis, Statistical Methods, role of Data, Python Language, challenges of Bias, Variance and Overfitting, choosing the right Performance Metrics, Model Evaluation Techniques, Model Optmization using Hyperparameter Tuning and Grid Search Cross Validation techniques, etc. You will learn how to perform detailed Data Analysis using Pythin, Statistical Techniques, Exploratory Data Analysis, using various Predictive Modelling Techniques such as a range of Classification Algorithms, Regression Models and Clustering Models. You will learn the scenarios and use cases of deploying Predictive models. This course covers Python for Data Science and Machine Learning in great detail and is absolutely essential for the beginner in Python.

In the first course of the Practical Data Science Specialization, you will learn foundational concepts for exploratory data analysis (EDA), automated machine learning (AutoML), and text classification algorithms. With Amazon SageMaker Clarify and Amazon SageMaker Data Wrangler, you will analyze a dataset for statistical bias, transform the dataset into machine-readable features, and select the most important features to train a multi-class text classifier. You will then perform automated machine learning (AutoML) to automatically train, tune, and deploy the best text-classification algorithm for the given dataset using Amazon SageMaker Autopilot. Next, you will work with Amazon SageMaker BlazingText, a highly optimized and scalable implementation of the popular FastText algorithm, to train a text classifier with very little code. Practical data science is geared towards handling massive datasets that do not fit in your local hardware and could originate from multiple sources.

With this practical guide, SAP veterans Greg Foss and Paul Modderman demonstrate how to use several data analysis tools to solve interesting problems with your SAP data.Data engineers and scientists will explore ways to add SAP data to their analysis processes, while SAP business analysts will learn practical methods for answering questions about the business. By focusing on grounded explanations of both SAP processes and data science tools, this book gives data scientists and business analysts powerful methods for discovering deep data truths.

This course teaches you how to use Amazon SageMaker to cover the different stages of the typical data science process, from analyzing and visualizing a data set, to preparing the data and feature engineering, down to the practical aspects of model building, training, tuning and deployment.

With this course, you'll have the keys to the entire Python Data Science kingdom!

With this course, you'll have the keys to the entire Python Data Science kingdom!

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