This is a list of MOOCs related to data science, business intelligence and big data that I’ve been compiling for about two years.
- Foundations of Business Strategy.
- Introduction to Recommender Systems.
- Introduction to Data Science.
- Data Mining with Weka.
- Computing for Data Analysis (R)
- Data Analysis (Coursera).
- The Analytics Edge.
- Functional Programming Principles in Scala (Spark, Stratosphere).
- Learning from Data.
- Machine learning.
- Introduction to infographics and visualization.
- Statistics One.
- Introduction to Statistics: Descriptive Statistics.
- Data Analysis for Genomics.
- Financial Analysis and Decision Making.
- Web Intelligence and Big Data.
- Wiretaps to Big Data: Privacy and Surveillance in the Age of Interconnection.
- Social and Economic Networks: Models and Analysis.
- Metadata: Organizing and Discovering Information.
- Data Analysis (SlideRule).
- Introduction to Statistics: Inference.
- Sabermetrics 101: Introduction to Baseball Analytics.
- Sabermetrics 101: Introduction to Baseball Analytics.
- Databases: Self-paced.
- Statistics in Medicine.
- Explore Statistics with R.
- Getting and Cleaning Data.
- The Data Scientist’s Toolbox.
- Tackling the Challenges of Big Data.
- Process Mining.
- Mining Massive Datasets.
- Introduction to Big Data with Apache Spark.
- Scalable Machine Learning with Apache Spark.
- Introduction to Computational Thinking and Data Science.
- Practical Machine Learning.
- Pattern Discovery in Data Mining.
- Text Retrieval and Search Engines.
- Cluster Analysis in Data Mining.
- Text Mining and Analytics.
- Data Visualization.
- Algorithms: Design and Analysis, Part 2.
- R programming.
- I “Heart” Stats: Learning to Love Statistics.
- Data Visualization and D3.js: Communicating with Data.
- Developing Data Products.
- Data Science and Machine Learning Essentials.
- Statistics for Business I and Statistics for Business II.
- Introduction to R programming.
- Advanced Statistics for the Life Sciences
- Genomic Data Science Specialization
- Regression Models
- Applied Regression Analysis
- Data Visualization and Communication with Tableau
- A Crash Course in Data Science
- Introduction to Natural Language Processing
- Statistical Inference