This is a self-paced lab that takes place in the Google Cloud console. This lab deployings a Kubernetes Engine cluster, then a simple web application fronted by a load balancer is deployed to the cluster. The web app publishes messages provided by the user to a Cloud Pub/Sub topic. You will see the correlated telemetry data from HTTP requests to the app will be available in the Cloud Trace Console.

In this course, we define what machine learning is and how it can benefit your business. You'll see a few demos of ML in action and learn key ML terms like instances, features, and labels. In the interactive labs, you will practice invoking the pretrained ML APIs available as well as build your own Machine Learning models using just SQL with BigQuery ML.

Robotic Process Automation (or RPA) implementation is conducted over multiple critical phases. In the Discovery phase, you identify the business processes beneficial for automation. In the Design phase, you create an RPA plan for automating them. In the Development and Testing phase, you execute the RPA plan and develop bots, testing them thoroughly during development. Next, you need to deploy the bots and set them up for routine monitoring. These activities are performed next in the implementation lifecycle: in the Deployment and Maintenance phases. You can deploy bots in various devices and also monitor their performance live via the Web Control Room. This is a web-based application, with comprehensive workload management, granular security controls, and an intuitive analytics dashboard. It is the one central interface from where you can create and manage users and roles, monitor connected and disconnected devices and schedule bot execution. As you begin this course, you will be introduced to the user interface of the Web Control Room. You will explore various panels and components in its Features Panel. You will also study some of the best practices and troubleshooting procedures that you can apply while using the Web Control Room during RPA Deployment and Maintenance. The learning will be reinforced through concept description, hands-on tasks, and guided practice.

This course is an introduction to how to use relational databases in business analysis. You will learn how relational databases work, and how to use entity-relationship diagrams to display the structure of the data held within them. This knowledge will help you understand how data needs to be collected in business contexts, and help you identify features you want to consider if you are involved in implementing new data collection efforts. You will also learn how to execute the most useful query and table aggregation statements for business analysts, and practice using them with real databases. No more waiting 48 hours for someone else in the company to provide data to you – you will be able to get the data by yourself! By the end of this course, you will have a clear understanding of how relational databases work, and have a portfolio of queries you can show potential employers. Businesses are collecting increasing amounts of information with the hope that data will yield novel insights into how to improve businesses. Analysts that understand how to access this data – this means you! – will have a strong competitive advantage in this data-smitten business world.

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.

Organizations need skilled, forward-thinking Big Data practitioners who can apply their business and technical skills to unstructured data such as tweets, posts, pictures, audio files, videos, sensor data, and satellite imagery and more to identify behaviors and preferences of prospects, clients, competitors, and others. In this short course you'll gain practical skills when you learn how to work with Apache Spark for Data Engineering and Machine Learning (ML) applications. You will work hands-on with Spark MLlib, Spark Structured Streaming, and more to perform extract, transform and load (ETL) tasks as well as Regression, Classification, and Clustering. The course culminates in a project where you will apply your Spark skills to an ETL for ML workflow use-case. NOTE: This course requires that you have foundational skills for working with Apache Spark and Jupyter Notebooks. The Introduction to Big Data with Spark and Hadoop course from IBM will equip you with these skills and it is recommended that you have completed that course or similar prior to starting this one.

In this 2 hour long project, you will learn to search and extract relevant research articles and their linked references efficiently from a journal database to conduct a bibliometric literature review. Then with these extracted data, you will learn to create a citation network. The visualization tool Gephi will be used in this project for citation network analysis. You will also learn, how to modify the network to present more information visually about the extracted citation data. 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.

Deepen your understanding of the power and politics of data in the public sector, including how values — in addition to data and evidence — are always part of public sector decision-making. In this course, you will explore common ethical challenges associated with data, data analytics, and randomized controlled trials in the public sector. You will also navigate and understand the ethical issues related to data systems and data analysis by understanding frameworks, codes of ethics, and professional guidelines. Using two technical case studies, you will understand common ethical issues, including participation bias in populations and how slicing analysis is used to identify bias in predictive machine learning models. This course also serves as a capstone experience for the Data Analytics in the Public Sector with R Specialization, where you will conduct an applied policy options analysis using authentic data from a real-world case study. In this capstone exercise, you will review data as part of policy options analysis, create a visualization of the results, and make a recommendation. All coursework is completed in RStudio in Coursera without the need to install additional software. This is the fourth and final course within the Data Analytics in the Public Sector with R Specialization. The series is ideal for current or early-career professionals working in the public sector looking to gain skills in analyzing public data effectively. It is also ideal for current data analytics professionals or students looking to enter the public sector.

This course is designed to introduce data privacy to a wide audience and help each participant see how data privacy has evolved as a compelling concern to public and private organizations as well as individuals. In this course, you will hear from legal and technical experts and practitioners who encounter data privacy issues daily. This course will review theories of data privacy as well as data privacy in the context of social media and artificial intelligence. It will also explore data privacy issues in journalism, surveillance, new technologies like facial recognition and biometrics. Completion of the course will enable the participant to be eligible for CPE credit.

This course introduces simple and multiple linear regression models. These models allow you to assess the relationship between variables in a data set and a continuous response variable. Is there a relationship between the physical attractiveness of a professor and their student evaluation scores? Can we predict the test score for a child based on certain characteristics of his or her mother? In this course, you will learn the fundamental theory behind linear regression and, through data examples, learn to fit, examine, and utilize regression models to examine relationships between multiple variables, using the free statistical software R and RStudio.