In this project, learners will have a guided look through Power Bi dynamic reports and visualizations for financial data analysis. As you view, load, and transform your data in Power Bi, you will learn which steps are key to making an effective financial report dashboard and how to connect your report for dynamic visualizations. Data reporting and visualization is the most critical step in a financial, business, or data analyst’s functions. The data is only as effective if it can be communicated effectively to key stakeholders in the organization. Effective communication of data starts here.

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.

In this hands-on project, we will train a Long Short Term (LSTM) Network to perform English to French Translation. This project could be practically used by travelers or people who are settling into a new country. Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.

Welcome to this 1 hour long guided project on data visualization using Bokeh. In this project you will learn the basics of Bokeh and create different plots and impressive data visualizations in detail. You will also learn Glyphs and how to Map Geo data using Bokeh. Please note that you will need prior programming experience ( beginner level) in Python. You will also need familiarity with Pandas. This is a practical, hands on guided project for learners who already have theoretical understanding of Pandas and Python.

This guided project is for beginners interested in taking their first steps with coding in the statistical language R. It assumes no previous knowledge of R, introduces the RStudio environment, and covers basic concepts, tools, and general syntax. By the end of the exercise, learners will build familiarity with RStudio and the fundamentals of the statistical coding language R.

Hello everyone and welcome to this new hands-on project on image classification with Amazon Web Services (AWS) AutoGluon. In this project, we will train several deep neural networks models to classify images using a powerful library known as AutoGluon. AutoGluon is the library behind AWS SageMaker autopilot and it allows for quick prototyping of several powerful models using a few lines of code.

Welcome to Introduction to Statistics & Data Analysis in Public Health! This course will teach you the core building blocks of statistical analysis - types of variables, common distributions, hypothesis testing - but, more than that, it will enable you to take a data set you've never seen before, describe its keys features, get to know its strengths and quirks, run some vital basic analyses and then formulate and test hypotheses based on means and proportions. You'll then have a solid grounding to move on to more sophisticated analysis and take the other courses in the series. You'll learn the popular, flexible and completely free software R, used by statistics and machine learning practitioners everywhere. It's hands-on, so you'll first learn about how to phrase a testable hypothesis via examples of medical research as reported by the media. Then you'll work through a data set on fruit and vegetable eating habits: data that are realistically messy, because that's what public health data sets are like in reality. There will be mini-quizzes with feedback along the way to check your understanding. The course will sharpen your ability to think critically and not take things for granted: in this age of uncontrolled algorithms and fake news, these skills are more important than ever. Prerequisites Some formulae are given to aid understanding, but this is not one of those courses where you need a mathematics degree to follow it. You will need only basic numeracy (for example, we will not use calculus) and familiarity with graphical and tabular ways of presenting results. No knowledge of R or programming is assumed.

This course encompasses a real-world practical approach to the ML Workflow: a case study approach that presents an ML team faced with several ML business requirements and use cases. This team must understand the tools required for data management and governance and consider the best approach for data preprocessing: from providing an overview of Dataflow and Dataprep to using BigQuery for preprocessing tasks. The team is presented with three options to build machine learning models for two specific use cases. This course explains why the team would use AutoML, BigQuery ML, or custom training to achieve their objectives. A deeper dive into custom training is presented in this course. We describe custom training requirements from training code structure, storage, and loading large datasets to exporting a trained model. You will build a custom training machine learning model, which allows you to build a container image with little knowledge of Docker. The case study team examines hyperparameter tuning using Vertex Vizier and how it can be used to improve model performance. To understand more about model improvement, we dive into a bit of theory: we discuss regularization, dealing with sparsity, and many other essential concepts and principles. We end with an overview of prediction and model monitoring and how Vertex AI can be used to manage ML models.

In this 1-hour long project-based course, you are going to be able to perform centrality network analysis and visualization on educational datasets, to generate different kinds of random graphs which represents social networks, and to manipulate the graph and subgraph structures, allowing you to break and get insights on complex structures. This guided project is for people who want to incorporate network data science skills into their technology portfolio. This is a topic of interest to researchers, marketers, consultants and practitioners associated with the knowledge areas of social science, marketing, social media, operational research and complexity science. Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.

In this class, Introduction to Designing Data Lakes on AWS, we will help you understand how to create and operate a data lake in a secure and scalable way, without previous knowledge of data science! Starting with the "WHY" you may want a data lake, we will look at the Data-Lake value proposition, characteristics and components. Designing a data lake is challenging because of the scale and growth of data. Developers need to understand best practices to avoid common mistakes that could be hard to rectify. In this course we will cover the foundations of what a Data Lake is, how to ingest and organize data into the Data Lake, and dive into the data processing that can be done to optimize performance and costs when consuming the data at scale. This course is for professionals (Architects, System Administrators and DevOps) who need to design and build an architecture for secure and scalable Data Lake components. Students will learn about the use cases for a Data Lake and, contrast that with a traditional infrastructure of servers and storage.