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University of Washington

Introduction to Data Science

University of Washington via Coursera

This course may be unavailable.


Commerce and research are being transformed by data-driven discovery and prediction. Skills required for data analytics at massive levels – scalable data management on and off the cloud, parallel algorithms, statistical modeling, and proficiency with a complex ecosystem of tools and platforms – span a variety of disciplines and are not easy to obtain through conventional curricula. Tour the basic techniques of data science, including both SQL and NoSQL solutions for massive data management (e.g., MapReduce and contemporaries), algorithms for data mining (e.g., clustering and association rule mining), and basic statistical modeling (e.g., linear and non-linear regression).


Part 0: Introduction 
  • Examples, data science articulated, history and context, technology landscape
Part 1: Data Manipulation at Scale
  • Databases and the relational algebra 
  • Parallel databases, parallel query processing, in-database analytics 
  • MapReduce, Hadoop, relationship to databases, algorithms, extensions, languages  
  • Key-value stores and NoSQL; tradeoffs of SQL and NoSQL
Part 2: Analytics
  • Topics in statistical modeling: basic concepts, experiment design, pitfalls
  • Topics in machine learning: supervised learning (rules, trees, forests, nearest neighbor, regression), optimization (gradient descent and variants), unsupervised learning
Part 3: Communicating Results
  • Visualization, data products, visual data analytics 
  • Provenance, privacy, ethics, governance 
Part 4: Special Topics
  • Graph Analytics: structure, traversals, analytics, PageRank, community detection, recursive queries, semantic web
  • Guest Lectures

Taught by

Bill Howe


3.5 rating, based on 31 Class Central reviews

Start your review of Introduction to Data Science

  • Introduction to Data Science is a MOOC offered by the University of Washington on the Coursera platform. Introduction to data science is a misleading title for this course because it is not introductory level and it does not have a sensible flow tha…
  • Federico Leven
    The lectures by Prof. Howe were fun and of good length to watch on a busy schedule. They supported the projects quite well and I think that the professor did an excellent job with this course. Unfortunately, some topics like Machine Learning lacked…
  • Janu Verma
    This course gave an excellent platform to channelize my preparations for the industry. The very first week we had to do a project in Python, where we accessed Twitter API and did sentiment analysis on a sample of live tweets. Now people working in…
  • Suzallo
    Professor Howe's lecture style is not always engaging, and a lot of material is covered. There were often over 3 hours worth of lecture material to review during the week. Along with following links and reading supporting papers, this left very litt…
  • Olena Bosenok
    While course assignments are invaluable and aimed to build programming/analytical skills critical for understanding big data processing, machine learning, and data analysis, the video lectures are sketchy, boring, and not sufficient for assignment…
  • Cecilia Laio
    It was a mammouth ambition to cover the basics of data science in fewer days than Phileas Fogg took to travel around the world. The lectures, at time, felt disjointed, since there was so much material to cover. The breadth of coverage was phenomenal…
  • Anonymous
  • Berk Van Der Vier

    He just doesn't have the ability to explain stuff. The examples he give are confusing, slides are boring and most importantly he is not prepared as you can see from his unorganised thoughts. I almost always found myself searching for the subject in google after he mentioned about them because it was impossible to understand the concepts/algorithms/methods from him. The course is definitely not introduction level either. May be I did wrong by following Andrew Ng's course in very beginning since my expectation got very high and I easily get disappointed when I can't find the same quality in other courses. As a result, you will see/hear many things in this course but you will not learn them from this instructor.
  • Anonymous
    Having completed the Coursera JHU Data Science specialization that was focused on the R language, I wanted to dig deeper into the IT side of data science with this course. And as the course description listed acquaintance with Python, SQL or R as a prerequisite, I decided to go for it.

    I guess I wasn't prepared enough, as the first week's assignment of a sentiment analysis in Python left me completely baffled.

    Even though I found the introduction to the course very inspiring, as the lecturer obviously understands the challenges of defining Data Science very well, I had to drop this course because you cannot fulfill assignments within reasonable time without having prior knowledge of Python.

  • Anonymous
    The lectures cover a lot of topics which I like. The assignments are extremely difficult for people without strong background in Python.
  • A Learner
    Taken the course. Total farce. Instructor never replies to discussion forums.

    The asisgnments have wrong information. And videos have typos. Total mess. Stay away.
  • Anonymous
    Is hard to provide a good feeback to the "communicating data science result" course.
    The first week went down quiet well, the teacher was good, but in order to complete the assignment you need to already know Tableau or similar.
    Howevere the peer review of the assignment look like not working well as there is not enough people taking this course to be score and score (so you can not pass it)
    Last week assignment ... i am speachless. Can not complete, instruction look like not working and you need to already know the system.

    a complete waste of money, at least for me.
  • Anonymous

    although the lectures were quite interesting and I learned a lot the HW are badly written and extremely frustrating (not because they are hard, but because they are very old and confusing)
  • Anonymous
    Excellent overview of several topics in Data Science. Instructor was engaging and presented the material well. I have used many of the techniques taught in this course in subsequent work.
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    Ramesh Natarajan

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