Introduction to Data Science
University of Washington via Coursera
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Overview
Class Central Tips
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).
Syllabus
Part 0: Introduction
- Examples, data science articulated, history and context, technology landscape
- 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
- 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
- Visualization, data products, visual data analytics
- Provenance, privacy, ethics, governance
- Graph Analytics: structure, traversals, analytics, PageRank, community detection, recursive queries, semantic web
- Guest Lectures
Taught by
Bill Howe
Tags
Reviews
3.5 rating, based on 31 Class Central reviews
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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…
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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…
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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…
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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…
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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…
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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…
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DO NOT TAKE THIS COURSE. IT'S A COMPLETE MESS!!! SO MANY TYPOS EVERYWHERE!!! THE TEACHER JUST THREW A BUNCH OF IDEAS TOGETHER AND CREATED PRESENTATIONS A FOUR YEAR OLD COULD MAKE. BILL HOWE DOES NOT KNOW HOW TO COMMUNICATE IN GENERAL AND DID N…
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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. -
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.
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The lectures cover a lot of topics which I like. The assignments are extremely difficult for people without strong background in Python.
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Taken the course. Total farce. Instructor never replies to discussion forums.
The asisgnments have wrong information. And videos have typos. Total mess. Stay away. -
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. -
DO NOT TAKE THIS COURSE
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) -
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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