Top 20 Data Science MOOCs
Looking out for the next data science MOOC? Checkout from our extensive list of MOOCs which covers all data science disciplines which are offered by leading organizations.
Mining Massive Datasets (Stanford University) (Sep 12 – Oct 31, 2015)
Level: Expert Effort: 8-10 hrs/week
Status: Upcoming Duration: 7 weeks
Prerequisite: Calculus, data structure Tools: C++ or Java
If you are planning to develop your next distributed system which is suppose to turn big data into insights, this is the course you should be taking. The course covers MapReduce, PageRank, locality-sensitive hashing, and many more advance algorithms. It is an extensive course involves a great amount of dedication and coding. Though coding assignments are optional it is highly recommended to complete them.
Neural Networks for Machine Learning (University of Toronto)
Level: Intermediate-Expert Effort: 7-9 hrs/week
Status: Archived Duration: 8 weeks
Prerequisite: None Tools: Octave
If you want to explore the current “hot topic” deep learning, you should explore this course. Taught by the Prof. Geoffrey Hilton, whose research has been revolutionizing the field. The course covers all parts right from the perceptron till the auto-encoders. The course will explain the new learning procedures that are responsible for current advances in the field of neural network, including effective new procedures for learning multiple layers of non-linear features, and give you the skills and understanding required to apply these procedures in many other domains. If you want to learn deep learning in-depth, consider following these courses: CS224d: Deep Learning for Natural Language Processing and Nvidia’s Deep Learning Courses.
Convex Optimization (Stanford University)
Level: Intermediate-Expert Effort: 8-10 hrs/week
Status: Archived Duration: 10 weeks
Prerequisite: Probability, Optimization Tools: Matlab
Advance course, if you are interested in optimization problems. This course concentrates on recognizing and solving convex optimization problems that arise in applications. The syllabus includes: convex sets, functions, and optimization problems; basics of convex analysis; least-squares, linear and quadratic programs, semidefinite programming, minimax, extremal volume, and other problems; optimality conditions, duality theory, theorems of alternative, and applications; interior-point methods; applications to signal processing, statistics and machine learning, control and mechanical engineering, digital and analog circuit design, and finance.
Process Mining: Data science in Action (Eindhoven University of Technology) (7 Oct- 2 Dec 2015)
Level: Intermediate-Expert Effort: 4-6 hrs/week
Status: Upcoming Duration: 8 weeks
Prerequisite: None Tools: Prom, Disco
Process mining is the missing link between model-based process analysis and data-oriented analysis techniques. Through concrete data sets and easy to use software the course provides data science knowledge that can be applied directly to analyze and improve processes in a variety of domains. The course explains the key analysis techniques in process mining. Participants will learn various process discovery algorithms. These can be used to automatically learn process models from raw event data.
Bioinformatic Methods I & II (University of Toronto)
Level: Intermediate-Expert Effort: 12-18 hrs/week
Status: On-demand Duration: 10 weeks
Prerequisite: None Tools: No restriction
Good course to cover all your basics in Bioinformatics. This is a two part course deals with databases, Blast, multiple sequence alignments, phylogenetic, selection analysis and metagenomics. Later, in part II, it covers motif searching, protein-protein interactions, structural Bioinformatics, gene expression data analysis, and cis-element predictions.
Model Building and Validation (AT & T)
Level: Intermediate-Expert Effort: 6 hrs/week
Status: Open Duration: 8 weeks
Prerequisite: ML, modelling, python Tools: Python, SQL
In this course you will take a more general approach, walking through the questioning, modelling and validation steps of the model building process. The goal is to get you to practice thinking in depth about a problem and coming up with your own solutions. Many examples we will attempt may not have one correct answer but, will require you to work through the problems applying the methods we hope to illustrate throughout this class.
Intro to Hadoop and MapReduce (Cloudera)
Level: Beginners-Intermediate Effort: 6-10 hrs/week
Status: Open Duration: 4 weeks
Prerequisite: None Tools: Java, Apache Hadoop
Learn the fundamental principles behind it, and how you can use its power to make sense of your Big Data. How Hadoop fits into the world (recognize the problems it solves). Understand the concepts of HDFS and MapReduce (find out how it solves the problems). Write MapReduce programs using java and apache Hadoop.
Real-Time Analytics with Apache Storm (Tweeter)
Level: Intermediate-Expert Effort: 6-10 hrs/week
Status: Open Duration: 4 weeks
Prerequisite: Data Structures Tools: Java, python, d3
Realtime analytics picking up its pace, and this is the course which specifically explains the details, challenges and solutions. Starting from basic distributed concepts presented during our first Udacity-Twitter Storm Hackathon, link Storm concepts to Storm syntax to scalably drive Word Cloud visualizations with Vagrant, Ubuntu, Maven, Flask, Redis, and d3. Learn how to link to the public Twitter gardenhose stream to process live tweets, parse embedded URLs, and calculate Top worldwide hashtags. Extend beyond Storm basics by exploring multi-language capabilities in Python, integrate open source components, and implement real-time streaming joins.
Introduction to Recommender Systems (University of Minnesota)
Level: Beginners-Intermediate Effort: 8-10 hrs/week
Status: Self-paced Duration: 8 weeks
Prerequisite: None Tools: No restriction
Retrieval systems are widespread in current softwares, whether it is web search, movie recommendation, document searching. The algorithms you will study include content-based filtering, user-user collaborative filtering, item-item collaborative filtering, dimensionality reduction, and interactive critique-based recommenders. The approach will be hands-on, with six week projects, each of which will involve implementation and evaluation of some type of recommender.
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