This is a self-paced lab that takes place in the Google Cloud console. In this lab, you learn to use BigQuery to find data, query the data-to-insights public dataset, and write and execute queries.

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.

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.

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.

This course is all about presenting the story of the data, using PowerPoint. You'll learn how to structure a presentation, to include insights and supporting data. You'll also learn some design principles for effective visuals and slides. You'll gain skills for client-facing communication - including public speaking, executive presence and compelling storytelling. Finally, you'll be given a client profile, a business problem, and a set of basic Excel charts, which you'll need to turn into a presentation - which you'll deliver with iterative peer feedback. This course was created by PricewaterhouseCoopers LLP with an address at 300 Madison Avenue, New York, New York, 10017.

This is a self-paced lab that takes place in the Google Cloud console. In this lab, you learn how to to create and use document processors using the Document AI API.

Welcome to this project-based course on Regression Analysis with Yellowbrick. In this project, we will build a machine learning model to predict the compressive strength of high performance concrete (HPC). Although, we will use linear regression, the emphasis of this project will be on using visualization techniques to steer our machine learning workflow. Visualization plays a crucial role throughout the analytical process. It is indispensable for any effective analysis, model selection, and evaluation. This project will make use of a diagnostic platform called Yellowbrick. It allows data scientists and machine learning practitioners to visualize the entire model selection process to steer towards better, more explainable models.Yellowbrick hosts several datasets from the UCI Machine Learning Repository. We’ll be working with the concrete dataset that is well suited for regression tasks. The dataset contains 1030 instances and 8 real valued attributes with a continuous target. We we will cover the following topics in our machine learning workflow: exploratory data analysis (EDA), feature and target analysis, regression modelling, cross-validation, model evaluation, and hyperparamter tuning. This course runs on Coursera's hands-on project platform called Rhyme. On Rhyme, you do projects in a hands-on manner in your browser. You will get instant access to pre-configured cloud desktops containing all of the software and data you need for the project. Everything is already set up directly in your internet browser so you can just focus on learning. For this project, you’ll get instant access to a cloud desktop with Python, Jupyter, Yellowbrick, and scikit-learn pre-installed. Notes: - You will be able to access the cloud desktop 5 times. However, you will be able to access instructions videos as many times as you want. - 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.

This course helps to build the foundational material to use mathematics as a tool to model, understand, and interpret the world around us. This is done through studying functions, their properties, and applications to data analysis. Concepts of precalculus provide the set of tools for the beginning student to begin their scientific career, preparing them for future science and calculus courses. This course is designed for all students, not just those interested in further mathematics courses. Students interested in the natural sciences, computer sciences, psychology, sociology, or similar will genuinely benefit from this introductory course, applying the skills learned to their discipline to analyze and interpret their subject material. Students will be presented with not only new ideas, but also new applications of an old subject. Real-life data, exercise sets, and regular assessments help to motivate and reinforce the content in this course, leading to learning and mastery.