The purpose of this course is to summarize new directions in Chinese history and social science produced by the creation and analysis of big historical datasets based on newly opened Chinese archival holdings, and to organize this knowledge in a framework that encourages learning about China in comparative perspective. Our course demonstrates how a new scholarship of discovery is redefining what is singular about modern China and modern Chinese history. Current understandings of human history and social theory are based largely on Western experience or on non-Western experience seen through a Western lens. This course offers alternative perspectives derived from Chinese experience over the last three centuries. We present specific case studies of this new scholarship of discovery divided into two stand-alone parts, which means that students can take any part without prior or subsequent attendance of the other part. Part 1 (https://www.coursera.org/learn/understanding-china-history-part-1) focuses on comparative inequality and opportunity and addresses two related questions ‘Who rises to the top?’ and ‘Who gets what?’. Part 2 (this course) turns to an arguably even more important question ‘Who are we?’ as seen through the framework of comparative population behavior - mortality, marriage, and reproduction – and their interaction with economic conditions and human values. We do so because mortality and reproduction are fundamental and universal, because they differ historically just as radically between China and the West as patterns of inequality and opportunity, and because these differences demonstrate the mutability of human behavior and values. Course Overview video: https://youtu.be/dzUPRyJ4ETk

This specialization is targeted to cloud sales, marketing managers, business executives, and operations and data center managers who need education around the specifics of the business aspects of operating a cloud at Cloud Service Providers (CSPs), Distributors, Resellers and Managed Service Providers who service cloud customers. This course is part of the Intel® Cloud Business Professional Specialization. Completion of specialization offers a badge via Credly. ● Cloud FinOps Overview and TCO Models: This lesson discusses cloud cost modeling and the emergence of the FinOps function. Also covered will be cloud cost management best practices and key personas involved of cloud economics. (Duration: 15 minutes) ● Cloud Service Provider Accounting Structures: This lesson will overview how the 3 large Cloud Services manage their enterprise accounting efforts including enterprise agreements and accounting entitlements. (Duration: 10 minutes) ● Native Cloud Billing Management and Reporting: This lesson addresses the usage invoice delivery, native cost explorer and usage reporting for each of the three large Cloud Service providers. (Duration: 15 minutes) ● Cloud Pricing Models: This lesson covers cloud pricing models for each of the 3 large Cloud Service Providers. Also addressed will be reservations, spot instances and native pricing estimators. (Duration: 30 minutes) ● Controlling Cloud Costs: This 3-part course will dive into the ideas and intricacies around controlling spending in the cloud. Included in this course are specific demos for the 3 large Cloud Service Providers. (Duration: 80 minutes) ● Cloud Multi-Tenant Management and Billback: This course discusses the business aspects of multi-tenant cloud billings. It covers showback vs billback billing models and prepaid instances and commitments. (Duration: 10 minutes)

In this 1-hour long project-based course, you will use the Keras API with TensorFlow as its backend to build and train a bidirectional LSTM neural network model to recognize named entities in text data. Named entity recognition models can be used to identify mentions of people, locations, organizations, etc. Named entity recognition is not only a standalone tool for information extraction, but it also an invaluable preprocessing step for many downstream natural language processing applications like machine translation, question answering, and text summarization. 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, and Keras 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.

By the end of this project, you will extract email text data from a file using a regular expression in a Java program. Java is a widely used programming language largely because of its versatility. One of the Developer tools often needed is file data extraction and Java contains methods to handle that task. For example, email files containing email addresses can often be difficult to analyze because of extraneous data. Error log files may also be more easily analyzed by matching specific data fields. 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 course, you learn how to use SAS Visual Analytics on SAS Viya to modify data for analysis, perform data discovery and analysis, and create interactive reports.

Apache Spark is one of the most widely used technologies in big data analytics. In this course, you will learn how to leverage your existing SQL skills to start working with Spark immediately. You will also learn how to work with Delta Lake, a highly performant, open-source storage layer that brings reliability to data lakes. By the end of this course, you will be able to use Spark SQL and Delta Lake to ingest, transform, and query data to extract valuable insights that can be shared with your team.

By the end of this project, you will learn to use data Exploration techniques in order to uncover some initial patterns, insights and interesting points in your dataset. We are going to use a dataset consisting 5 CSV files, consisting of the data related to players in FIFA video game. We will clean and prepare it by dropping useless columns, calculating new features for our dataset and filling up the null values properly. and then we will start our exploration and we'll do some visualizations. Note: This project 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 2 hours project you will learn how to build custom regional reports with Google Analytics. You will familiarize with Google Analytics and its usage, create a marketing custom regional dashboard with table and graph widgets, customize a standard geo report and scheduled the report you have designed to be sent monthly via email to a distributed regional marketing team.

This is a self-paced lab that takes place in the Google Cloud console. In this lab you'll learn how to assess an IT environment with StratoZone's scalable discovery.

This course introduces several tools for processing business data to obtain actionable insight. The most important tool is the mind of the data analyst. Accordingly, in this course, you will explore what it means to have an analytic mindset. You will also practice identifying business problems that can be answered using data analytics. You will then be introduced to various software platforms to extract, transform, and load (ETL) data into tools for conducting exploratory data analytics (EDA). Specifically, you will practice using PowerBI, Alteryx, and RStudio to conduct the ETL and EDA processes. The learning outcomes for this course include: 1. Development of an analytic mindset for approaching business problems. 2. The ability to appraise the value of datasets for addressing business problems using summary statistics and data visualizations. 3. The ability to competently operate business analytic software applications for exploratory data analysis.