Data Science Foundations Data Mining

All data science begins with good data. Data mining is a framework for collecting, searching, and filtering raw data in ... (more)

All data science begins with good data. Data mining is a framework for collecting, searching, and filtering raw data in a systematic matter, ensuring you have clean data from the start. It also helps you parse large data sets, and get at the most meaningful, useful information. This course, Data Science Foundations: Data Mining, is designed to provide a solid point of entry to all the tools, techniques, and tactical thinking behind data mining. Barton Poulson covers data sources and types, the languages and software used in data mining (including R and Python), and specific task-based lessons that help you practice the most common data-mining techniques: text mining, data clustering, association analysis, and more. This course is an absolute necessity for those interested in joining the data science workforce, and for those who need to obtain more experience in data mining. (less)

Data mining is the area of data science that focuses on finding actionable patterns in large and diverse datasets: clust... (more)

Data mining is the area of data science that focuses on finding actionable patterns in large and diverse datasets: clusters of similar customers, trends over time that can only be spotted after disentangling seasonal and random effects, and new methods for predicting important outcomes. In this course, instructor Barton Poulson introduces you to data mining that uses the programming language Python. Barton goes over some preliminaries, such as the tools you may use for data mining. He discusses aspects of dimensionality reduction, then explains clustering, including hierarchical clustering, k-Means, DBSCAN, and more. Barton covers classification, including kNN and decision trees. He goes into association analysis and introduces you to Apriori, Eclat, and FP-Growth. Barton steps you through a time-series decomposition, then concludes with sentiment scoring and other text mining tools. (less)

Data science continues to grow in sophistication and demand at an exponential rate. Data mining is the area of data scie... (more)

Data science continues to grow in sophistication and demand at an exponential rate. Data mining is the area of data science that focuses on finding actionable patterns in large and diverse datasets: clusters of similar customers, trends over time that can only be spotted after disentangling seasonal and random effects, and new methods for predicting important outcomes. Instructor Barton Poulson focuses on data mining in R, presents a broad range of algorithms including machine learning methods, and offers important information on laws and policies that affect data mining. Barton gives an overview of dimensionality reduction. He introduces clustering, including hierarchical clustering, then goes into association analysis. He explains time-series mining and decomposition, then concludes with text mining, sentiment analysis, and sentiment scoring. (less)

Approach big data with confidence by mastering the core skills needed to put data to work for your business. This course... (more)

Approach big data with confidence by mastering the core skills needed to put data to work for your business. This course covers the basics of data engineering, system design, analytics, and business intelligence. Data science expert Ben Sullins explains how to collect and organize your data so you can deliver results that your organization can leverage. Ben starts by examining the modern data ecosystem and how it relates to running a smart and efficient data hub. Then, he shows you how to perform the principle tasks involved in managing, loading, extracting, and transforming data. He also takes you through staging, profiling, cleansing, and migrating data. Along the way, he provides actionable recommendations that applicable to data experts throughout an organization-analysts, engineers, scientists, modelers, and more. (less)