Tag Archives: stanford

Coursera is Expanding – New Courses Starting Today

Since recently announcing $16M in funding, Coursera has been making quite a bit of noise. Last fall, Stanford University decided to freely offer a couple computer science classes online. The response was huge, and that led to the creation of Coursera.

The courses are no longer limited to computer science, and Stanford is no longer the only school involved. Here is a list of academic areas being offered and another list with the schools involved.

Academic Areas

Universities Involved

Although, not all of the courses will be directly related to data science, many of them are very close. Naturally Math, Statistics, and Computer Science areas have direct relations to data science. However, some of the other areas such as Networks, Biology, and Economics are some of the most popular application areas for data science. This is very exciting. My only concern is that the courses are a bit too much like traditional university courses with specific start/end dates and homework due dates. It will be interesting to see if the course structures change over time.

Anyhow, the following courses are starting today. Signup and start learning.

  • Machine Learning – A major focus area of data science
  • Computer Science 101 – probably a good starting point if you don’t know how to program
  • Compilers – good for understanding how programming languages work
  • Automata – hard to explain in 1 line, but it contains some fundamental principles in computer science
  • Intro to Logic – learn to reason systematically
  • Computer Vision – not sure of the relation to data science, but I am sure there is one, if you know, please leave a comment

Are you going to enroll in any of these courses?

Colleges with Data Science Degrees

Colleges and Universities are slowly starting to notice the demand for employees with data science skills. Most of the programs are not named data science, but they all focus on producing data people. Below are a couple of the programs I have noticed so far.

Do you know of any other programs?

School Program On-Campus Online Degrees
Stevens Institute of Technology Business Intelligence & Analytics Yes Yes M.S.
iSchool @ Syracuse University Data Science Yes Yes Grad Certificate
North Carolina State University Analytics Yes No M.S.
Northwestern University Predictive Analytics / Analytics No/Yes Yes/No M.S.
Stanford University Data Mining Some Courses Yes Grad Certificate
University of Cincinnati Business Analytics Yes No M.S.
Oxford University Data and Systems Analysis ? Yes Undergrad Adv. Diploma
University of California San Diego Data Mining No Yes Grad Certificate
University of Washington Data Science Yes Yes Certificate
Georgia Southern University Computer Science with concentration in Data and Knowledge Systems No Yes M.S.
University of San Francisco Analytics Yes No M.S.
Louisiana State University Analytics Yes No M.S.
Michigan State University Business Analytics Yes No M.S.
New York University Business Analytics Yes No M.S.
College of Charleston Discovery Informatics Yes No Undergraduate Minor
University of Dundee Data Science Yes No M.S.
UC Berkeley iSchool Information Management and Systems Yes No M.S.I.S. & Ph.D.
Carnegie Mellon University Business Intelligence and Data Analytics Yes No (Global) M.I.S.M.
Loras College Business Analytics Yes No MBA
Central Connecticut State University Data Mining No Yes M.S.
Imperial College London Data Science & Management Yes Yes MSc
George Mason University Informatics or Data Science yes No minor, B.S., M.S., PhD
New York University Data Science Yes No M.S.
The University of Manchester ACS: Data and Knowledge Management MSc Yes No MSc
University College London MSc Web Science and Big Data Analytics Yes No MSc
Dublin Institute of Technology MSc in Computing (Data Analytics) Yes No MSc
Sheffield Hallam University MSc Big Data Analytics Yes No MSc
Royal Holloway, University of London MSc in Big Data Yes No MSc
The University of Edinburgh Informatics MSc Yes No MSc
University College London MSc in Computational Statistics and Machine Learning Yes No MSc
University of Essex MSc Big Data and Text Analytics Yes No MSc
De Montfort University Business Intelligence Systems and Data Mining MSc/PG Dip/PG Cert Yes No MSc/Certificate
University of Greenwich MSc Data Warehousing and Data Mining Yes No MSc
University of East Anglia MSc Knowledge Discovery and Datamining Yes No MSc
University of St Andrews MSc. Applied Statistics and Datamining Yes No MSc
University College London PhD Studentships in Statistics Available: “Big Data” and its Applications Yes No PhD
Aalborg University MSc Data Engineering Yes No MSc
Linköping University MSc Statistics and Data Mining Yes No MSc
UCD Michael Smurfit Graduate Business School Msc in Business Analytics Yes No MSc
University of Milan-Bicocca Master of Business Intelligence & Decision Support Systems (MU1) Yes No MSc
Maastricht University MSc International Business (track: Business Intelligence) Yes No MSc
Aarhus University Business Intelligence MSc Yes No MSc
University of Helsinki MSc Algorithms and Machine Learning Yes No MSc
University of Antwerp MSc Computer Science (specialization: Databases) Yes No MSc
Dalarna University MSc Business Intelligence Yes No MSc
University of Bristol MSc Advanced Computing (Machine Learning and Data Mining) Yes No MSc
Aalto University MSc Machine Learning and Data Mining Yes No MSc
KTH Royal Institute of Technology MSc Machine Learning Yes No MSc
University of Westminster MSc Business Intelligence and Analytics Yes No MSc
Université Pierre et Marie Curie (UPMC) MSc Artificial intelligence and decision specialization (M2) Yes No MSc
Sheffield Hallam University MSc Database Professional Yes No MSc
Université de Nantes MSc Data Mining and Knowledge Management Yes No MSc
TELECOM SudParis MSc Automatic Data Processing Yes No MSc
University of Liverpool MRes Advanced Science (Computer Science pathway) (Data Mining) Yes No MSc
Carnegie Mellon University Very Large Information Systems Yes No M.S.
Brown University Big Data Yes No PhD
University of Washington Big Data Yes No PhD
Penn State University Social Data Analytics Yes No PhD
University of East London Data Mining and Knowledge Management Yes No MSc
University of Technology Syndney Master of Analytics Yes No MS
George Washington University Master in Business Analytics Yes No MS
Northwestern College (Iowa) Analytics Certificate No Yes Undergrad Certificate
The Chinese University of Hong Kong Data Science & Business Statistics Yes No MSc
Bournemouth University Applied Data Analytics Yes No MSc
University of Warwick Data Analytics Yes No MSc
University of Southampton Business Analytics and Management Sciences (BAMS) Yes No MSc
University of Kent Management Science (Business Analytics) Yes No MSc
University College Dublin Computer Science (Negotiated Learning) Yes No MSc
Swansea University Modelling, Uncertainty and Data Yes No MSc
Institute of Technology Blanchardstown Computing (Business Intelligence & Data Mining) Yes No MSc
St Joseph University, PA Business Intelligence Yes Yes MS
Pace University Customer Intelligence and Analytics Yes No MS
Fordham University Business Analytics Yes No MS
NYU Stern Business Analytics Yes No MS
USC CS with Data Science Yes No MS
Birmingham City University Business Intelligence Yes No MSc
University of Surrey Business Analytics Yes No MSc
Robert Gordon University Computing: Information Engineering Yes No MSc
The University of Manchester Information Management Yes No MSc
Sheffield Hallam University Web and Cloud Computing Yes No MSc
Brunel University Business Intelligence and Social Media Yes No MSc
Warwick Business School Business Analytics & Consulting Yes No MSc
University of Leicester Data Analysis for Business Intelligence Yes No MSc
University of Reading Advanced Computer Science Yes No MSc
Illinois Inst. of Tech Specializations in Data Science Yes No BS & MS

Here is another similar list of colleges with bigdata/data science programs.

Updated: New schools added and a link to another list of graduate programs. Last update May 2013

Stanford Machine Learning Class – What is covered

A few days ago, I mentioned that the Stanford Machine Learning class will be starting soon.  I thought I should quickly mention some of the topics covered.  The list also serves as a great outline for machine learning.

Supervised Learning

In supervised learning, one has a set of data with features and labels.

  • Linear Regression – one/multiple variables
  • Gradient Descent - a general algorithm for minimizing a function
  • Logistic Regression – This is useful when predicting classification type results.  For example, are you looking for a yes or no result.  Does the patient have cancer?  Will the customer buy my new product?  It can also be helpful for more than 2 results.  What color will a person choose (red, blue, green, silver)?
  • Neural Networks – A learning algorithm that is modeled after the brain.  Think of neurons.
  • Support Vector Machines

Unsupervised Learning

In unsupervised learning, one has a set of data with no features and labels.  Can some structure be found for the data?

  • Clustering – The most popular technique is K-means.
  • PCA (Principal Components Analysis) – speed up a learning algorithm

Anomaly Detection

This section covers methods to determine if data is bad.  Bad data is considered an anomaly.

Recommender Systems

Like the name says, recommender systems are used to make recommendations.  Companies like Netflix use recommender systems to recommend new movies to customers.  LinkedIn also recommends people to connect with.  This is a fairly hot topic in the tech world right now.

  • Content Based(Features)
    • Modified Linear Regression
  • Non-content Based(No Features)
    • Collaborative Filtering
    • Matrix Factorization

If any of these topics sound interesting to you, signup for the Stanford Machine Learning class.  Professor Andrew Ng will do an excellent job explaining the details.

Don’t Miss – Stanford Machine Learning

In a matter of days, Stanford will begin the second round of the free online machine learning course. I enrolled in the course last fall, and it exceded all expectations. Professor Andrew Ng is great. The prerequisites are minimal, so don’t worry if your math is a little rusty. Also, the videos are short (around 8 – 12 minutes). Therefore, you don’t need large blocks of time set aside. Just watch a video or two during your lunch and you should be able to keep up. There are programming assignments (optional) and review questions to go along with the videos.

Don’t worry if you fall behind. The videos will still be there. The material you learn is more important than the pace. If you don’t know machine learning, the Stanford class is a great opportunity to get started.

Here is Professor Ng’s introduction to the class.