Learn the general concepts of data mining along with basic methodologies and applications. Then dive into one subfield in data mining: pattern discovery. Learn in-depth concepts, methods, and applications of pattern discovery in data mining. We will also introduce methods for pattern-based classification and some interesting applications of pattern discovery.
This course provides you the opportunity to learn skills and content to practice and engage in scalable pattern discovery methods on massive transactional data, discuss pattern evaluation measures, and study methods for mining diverse kinds of patterns, sequential patterns, and sub-graph patterns.
The Data Mining Specialization teaches data mining techniques for both structured data which conform to a clearly defined schema, and unstructured data which exist in the form of natural language text. Specific course topics include pattern discovery, clustering, text retrieval, text mining and analytics, and data visualization. The Capstone project task is to solve real-world data mining challenges using a restaurant review data set from Yelp.
This course will cover the major techniques for mining and analyzing text data to discover interesting patterns, extract useful knowledge, and support decision making, with an emphasis on statistical approaches that can be generally applied to arbitrary text data in any natural language with no or minimum human effort.
In this course, you will analyze and apply essential design principles to your Tableau visualizations. This course assumes you understand the tools within Tableau and have some knowledge of the fundamental concepts of data visualization.
Discover the basic concepts of cluster analysis, and then study a set of typical clustering methodologies, algorithms, and applications. This includes partitioning methods such as k-means, hierarchical methods such as BIRCH, and density-based methods such as DBSCAN/OPTICS. Moreover, learn methods for clustering validation and evaluation of clustering quality. Finally, see examples of cluster analysis in applications.
In this third course of the specialization, we’ll drill deeper into the tools Tableau offers in the areas of charting, dates, table calculations and mapping. We’ll explore the best choices for charts, based on the type of data you are using. We’ll look at specific types of charts including scatter plots, Gantt charts, histograms, bullet charts and several others, and we’ll address charting guidelines.
In this course, we will show you exciting examples of collaborative, interactive web applications that use various types of media including sound, images and big data. We will show you how to build sites that provide precisely this functionality, using Meteor. We will also provide fully working example application code that you can use for your own commercial web projects. The course also provides a range of advice and suggestions about how to develop bespoke web applications which match the requirements of clients, where clients are people who commission the product or people who use the product.
The data science revolution has produced reams of new data from a wide variety of new sources. These new datasets are being used to answer new questions in way never before conceived. Visualization remains one of the most powerful ways draw conclusions from data, but the influx of new data types requires the development of new visualization techniques and building blocks.
In this first course of the specialization, you will discover just what data visualization is, and how we can use it to better see and understand data. Using Tableau, we’ll examine the fundamental concepts of data visualization and explore the Tableau interface, identifying and applying the various tools Tableau has to offer.
Leveraging the visualizations you created in the previous course, Visual Analytics with Tableau, you will create dashboards that help you identify the story within your data, and you will discover how to use Storypoints to create a powerful story to leave a lasting impression with your audience.
The analytical process does not end with models than can predict with accuracy or prescribe the best solution to business problems. Developing these models and gaining insights from data do not necessarily lead to successful implementations. This depends on the ability to communicate results to those who make decisions.
You'll begin this course by looking at some advanced Excel skills - including index formulas, logical text and nested functions. You'll also look at data connections to external databases, and Visual Basic for Applications (the programming language behind Excel). Once you're comfortable with that, you'll move on to preparing a spreadsheet for a client - giving it a clean design and making it easy to use and reproduce.
This course, which is designed to serve as the first course in the Recommender Systems specialization, introduces the concept of recommender systems, reviews several examples in detail, and leads you through non-personalized recommendation using summary statistics and product associations, basic stereotype-based or demographic recommendations, and content-based filtering recommendations.
MOOCs – Massive Open Online Courses – enable students around the world to take university courses online. This guide, by the instructors of edX’s most successful MOOC in 2013-2014, Principles of Written English (based on both enrollments and rate of completion), advises current and future students how to get the most out of their online study, covering areas such as what types of courses are offered and who offers them, what resources students need, how to register, how to work effectively with other students, how to interact with professors and staff, and how to handle assignments. This second edition offers a new chapter on how to stay motivated. This book is suitable for both native and non-native speakers of English, and is applicable to MOOC classes on any subject (and indeed, for just about any type of online study).