This is a self-paced lab that takes place in the Google Cloud console. In this lab, you'll set up a Google Kubernetes Engine cluster, then deploy the Managed Service for Prometheus to ingest metrics from a simple application.

This course is designed to show you how use quantitative models to transform data into better business decisions. You’ll learn both how to use models to facilitate decision-making and also how to structure decision-making for optimum results. Two of Wharton’s most acclaimed professors will show you the step-by-step processes of modeling common business and financial scenarios, so you can significantly improve your ability to structure complex problems and derive useful insights about alternatives. Once you’ve created models of existing realities, possible risks, and alternative scenarios, you can determine the best solution for your business or enterprise, using the decision-making tools and techniques you’ve learned in this course.

In this 1-hour long project-based course, you will learn the basics of using Tableau Public software to visualize Citibike Trips Dataset. By the end of this project, you will have created a few visualizations and a collection of visualizations called a dashboard. 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.

This is a self-paced lab that takes place in the Google Cloud console. In this lab, you will learn how to migrate an application running on Apache Cassandra™ to DataStax Enterprise (DSE). To do this, you will deploy a Cassandra™ database and an application that writes data into it. You will then deploy a DataStax Enterprise database and connect the same application to the database. Finally, you will learn how to migrate data from Apache Cassandra™ to DSE using the The DataStax Bulk Loader dsbulk.

This course will help lay the foundation of your healthcare data journey and provide you with knowledge and skills necessary to work in the healthcare industry as a data scientist. Healthcare is unique because it is associated with continually evolving and complex processes associated with health management and medical care. We'll learn about the many facets to consider in healthcare and determine the value and growing need for data analysts in healthcare. We'll learn about the Triple Aim and other data-enabled healthcare drivers. We'll cover different concepts and categories of healthcare data and describe how ontologies and related terms such as taxonomy and terminology organize concepts and facilitate computation. We'll discuss the common clinical representations of data in healthcare systems, including ICD-10, SNOMED, LOINC, drug vocabularies (e.g., RxNorm), and clinical data standards. We’ll discuss the various types of healthcare data and assess the complexity that occurs as you work with pulling in all the different types of data to aid in decisions. We will analyze various types and sources of healthcare data, including clinical, operational claims, and patient generated data as well as differentiate unstructured, semi-structured and structured data within health data contexts. We'll examine the inner workings of data and conceptual harmony offer some solutions to the data integration problem by defining some important concepts, methods, and applications that are important to this domain.

This capstone is the last course in the Data Analytics in Accountancy Specialization. In this capstone course, you are going to take the knowledge and skills you have acquired from the previous courses and apply them to a real-world problem. You will be provided with a loan dataset from Lending Club which is the largest peer-to-peer lending platform. You will explore the characteristics of the features in the dataset through statistical analysis, exploratory data analysis and visualization. You will also create a machine learning model to predict whether a loan will be fully paid or not. Finally, you will construct a portfolio with the help of your analysis. The goal is to create a portfolio that achieves better return than the overall return of all loans on the Lending Club platform.

This is a self-paced lab that takes place in the Google Cloud console. In this lab, you will explore using the Managed Service for Prometheus to collect metrics from other infrastructure sources via exporters.

In this data-driven world, companies are often interested in knowing what is the "best" course of action, given the data. For example, manufacturers need to decide how many units of a product to produce given the estimated demand and raw material availability? Should they make all the products in-house or buy some from a third-party to meet the demand? Prescriptive Analytics is the branch of analytics that can provide answers to these questions. It is used for prescribing data-based decisions. The most important method in the prescriptive analytics toolbox is optimization. This course will introduce students to the basic principles of linear optimization for decision-making. Using practical examples, this course teaches how to convert a problem scenario into a mathematical model that can be solved to get the best business outcome. We will learn to identify decision variables, objective function, and constraints of a problem, and use them to formulate and solve an optimization problem using Excel solver and spreadsheet.

This course allows you to apply the SQL skills taught in “SQL for Data Science” to four increasingly complex and authentic data science inquiry case studies. We'll learn how to convert timestamps of all types to common formats and perform date/time calculations. We'll select and perform the optimal JOIN for a data science inquiry and clean data within an analysis dataset by deduping, running quality checks, backfilling, and handling nulls. We'll learn how to segment and analyze data per segment using windowing functions and use case statements to execute conditional logic to address a data science inquiry. We'll also describe how to convert a query into a scheduled job and how to insert data into a date partition. Finally, given a predictive analysis need, we'll engineer a feature from raw data using the tools and skills we've built over the course. The real-world application of these skills will give you the framework for performing the analysis of an AB test.

Welcome to this 2 hour long project-based course on Principal Component Analysis with NumPy and Python. In this project, you will do all the machine learning without using any of the popular machine learning libraries such as scikit-learn and statsmodels. The aim of this project and is to implement all the machinery of the various learning algorithms yourself, so you have a deeper understanding of the fundamentals. By the time you complete this project, you will be able to implement and apply PCA from scratch using NumPy in Python, conduct basic exploratory data analysis, and create simple data visualizations with Seaborn and Matplotlib. The prerequisites for this project are prior programming experience in Python and a basic understanding of machine learning theory. 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, NumPy, and Seaborn pre-installed.