One of the skills that characterizes great business data analysts is the ability to communicate practical implications of quantitative analyses to any kind of audience member. Even the most sophisticated statistical analyses are not useful to a business if they do not lead to actionable advice, or if the answers to those business questions are not conveyed in a way that non-technical people can understand. In this course you will learn how to become a master at communicating business-relevant implications of data analyses. By the end, you will know how to structure your data analysis projects to ensure the fruits of your hard labor yield results for your stakeholders. You will also know how to streamline your analyses and highlight their implications efficiently using visualizations in Tableau, the most popular visualization program in the business world. Using other Tableau features, you will be able to make effective visualizations that harness the human brain’s innate perceptual and cognitive tendencies to convey conclusions directly and clearly. Finally, you will be practiced in designing and persuasively presenting business “data stories” that use these visualizations, capitalizing on business-tested methods and design principles.

This course on integrating sensors with your Raspberry Pi is course 3 of a Coursera Specialization and can be taken separately or as part of the specialization. Although some material and explanations from the prior two courses are used, this course largely assumes no prior experience with sensors or data processing other than ideas about your own projects and an interest in building projects with sensors. This course focuses on core concepts and techniques in designing and integrating any sensor, rather than overly specific examples to copy. This method allows you to use these concepts in your projects to build highly customized sensors for your applications. Some of the ideas covered include calibrating sensors and the trade-offs between different mathematical methods of storing and applying calibration curves to your sensors. We also discuss accuracy, precision, and how to understand uncertainty in your measurements. We study methods of interfacing analog sensors with your Raspberry Pi (or other platform) with amplifiers and the theory and technique involved in reducing noise with spectral filters. Lastly, we borrow from the fields of data science, statistics, and digital signal processing, to post-process our data in Python.

Organizations large and small are inundated with data about consumer choices. But that wealth of information does not always translate into better decisions. Knowing how to interpret data is the challenge -- and marketers in particular are increasingly expected to use analytics to inform and justify their decisions. Marketing analytics enables marketers to measure, manage and analyze marketing performance to maximize its effectiveness and optimize return on investment (ROI). Beyond the obvious sales and lead generation applications, marketing analytics can offer profound insights into customer preferences and trends, which can be further utilized for future marketing and business decisions. This course, developed at the Darden School of Business at the University of Virginia, gives you the tools to measure brand and customer assets, understand regression analysis, and design experiments as a way to evaluate and optimize marketing campaigns. You'll leave the course with a solid understanding of how to use marketing analytics to predict outcomes and systematically allocate resources. You can follow my posts in Twitter, @rajkumarvenk, and on linkedin: https://www.linkedin.com/in/education-marketing. Thanks, Raj Professor of Business Administration at Darden

This course teaches you the fundamentals of computational phenotyping, a biomedical informatics method for identifying patient populations. In this course you will learn how different clinical data types perform when trying to identify patients with a particular disease or trait. You will also learn how to program different data manipulations and combinations to increase the complexity and improve the performance of your algorithms. Finally, you will have a chance to put your skills to the test with a real-world practical application where you develop a computational phenotyping algorithm to identify patients who have hypertension. You will complete this work using a real clinical data set while using a free, online computational environment for data science hosted by our Industry Partner Google Cloud.

One of the most exciting aspects of business analytics is finding patterns in the data using machine learning algorithms. In this course you will gain a conceptual foundation for why machine learning algorithms are so important and how the resulting models from those algorithms are used to find actionable insight related to business problems. Some algorithms are used for predicting numeric outcomes, while others are used for predicting the classification of an outcome. Other algorithms are used for creating meaningful groups from a rich set of data. Upon completion of this course, you will be able to describe when each algorithm should be used. You will also be given the opportunity to use R and RStudio to run these algorithms and communicate the results using R notebooks.

This is a self-paced lab that takes place in the Google Cloud console. In this lab, you create a Cloud Composer environment using the GCP Console. You then use the Airflow web interface to run a workflow that verifies a data file, creates and runs an Apache Hadoop wordcount job on a Dataproc cluster, and deletes the cluster.

Hello everyone and welcome to this new hands-on project on Machine Learning Regression with Amazon Web Services (AWS) AutoGluon. In this project, we will train several regression models using a super 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.

In this course, learners will be introduced to the field of statistics, including where data come from, study design, data management, and exploring and visualizing data. Learners will identify different types of data, and learn how to visualize, analyze, and interpret summaries for both univariate and multivariate data. Learners will also be introduced to the differences between probability and non-probability sampling from larger populations, the idea of how sample estimates vary, and how inferences can be made about larger populations based on probability sampling. At the end of each week, learners will apply the statistical concepts they’ve learned using Python within the course environment. During these lab-based sessions, learners will discover the different uses of Python as a tool, including the Numpy, Pandas, Statsmodels, Matplotlib, and Seaborn libraries. Tutorial videos are provided to walk learners through the creation of visualizations and data management, all within Python. This course utilizes the Jupyter Notebook environment within Coursera.

Code and run your first Python program in minutes without installing anything! This course is designed for learners with limited coding experience, providing a foundation for presenting data using visualization tools in Jupyter Notebook. This course helps learners describe and make inferences from data, and better communicate and present data. The modules in this course will cover a wide range of visualizations which allow you to illustrate and compare the composition of the dataset, determine the distribution of the dataset, and visualize complex data such as geographically-based data. Completion of Data Analysis in Python with pandas & matplotlib in Spyder before taking this course is recommended. To allow for a truly hands-on, self-paced learning experience, this course is video-free. Assignments contain short explanations with images and runnable code examples with suggested edits to explore code examples further, building a deeper understanding by doing. You’ll benefit from instant feedback from a variety of assessment items along the way, gently progressing from quick understanding checks (multiple choice, fill in the blank, and un-scrambling code blocks) to small, approachable coding exercises that take minutes instead of hours. Finally, an accumulative lab at the end of the course will provide you an opportunity to apply all learned concepts within a real-world context.

This course is designed to provide you with basic working knowledge for using Excel spreadsheets for Data Analysis. It covers some of the first steps for working with spreadsheets and their usage in the process of analyzing data. It includes plenty of videos, demos, and examples for you to learn, followed by step-by-step instructions for you to apply and practice on a live spreadsheet. Excel is an essential tool for working with data - whether for business, marketing, data analytics, or research. This course is suitable for those aspiring to take up Data Analysis or Data Science as a profession, as well as those who just want to use Excel for data analysis in their own domains. You will gain valuable experience in cleansing and wrangling data using functions and then analyze your data using techniques like filtering, sorting and creating pivot tables. This course starts with an introduction to spreadsheets like Microsoft Excel and Google Sheets and loading data from multiple formats. With this introduction you will then learn to perform some basic level data wrangling and cleansing tasks and continue to expand your knowledge of analyzing data through the use of filtering, sorting, and using pivot tables within the spreadsheet. By performing these tasks throughout the course, it will give you an understanding of how spreadsheets can be used as a data analysis tool and understand its limitations. There is a strong focus on practice and applied learning in this course. With each lab, you will gain hands-on experience in manipulating data and begin to understand the important role of spreadsheets. Clean and analyze your data faster by understanding functions in the formatting of data. You will then convert your data to a pivot table and learn its features to make your data organized and readable. The final project enables you to show off your newly acquired data analysis skills. By the end of this course you will have worked with several data sets and spreadsheets and demonstrated the basics of cleaning and analyzing data all without having to learn any code. Getting started with Excel is made easy in this course. It does not require any prior experience with spreadsheets or coding. Nor does it require downloads or installation of any software. All you need is a device with a modern web browser, and ability to create a Microsoft account to access Excel online at no-cost. However if you already have a desktop version of Excel, you can follow along quite easily as well.