Given a phrase, count the occurrences of each word in that phrase.
For the purposes of this exercise you can expect that a word will always be one of:
When counting words you can assume the following rules:
Clean up user-entered phone numbers so that they can be sent SMS messages.
The North American Numbering Plan (NANP) is a telephone numbering system used by many countries in North America like the United States, Canada or Bermuda. All NANP-countries share the same international country code: 1.
NANP numbers are ten-digit numbers consisting of a three-digit Numbering Plan Area code, commonly known as area code, followed by a seven-digit local number. The first three digits of the local number represent the exchange code, followed by the unique four-digit number which is the subscriber number.
The format is usually represented as
(NXX)-NXX-XXXX
If you want to build something using a Raspberry Pi, you'll probably use resistors. For this exercise, you need to know two things about them:
In this exercise you are going to create a helpful program so that you don't have to remember the values of the bands. The program will take color names as input and output a two digit number, even if the input is more than two colors!
The band colors are encoded as follows:
Given a year, report if it is a leap year.
The tricky thing here is that a leap year in the Gregorian calendar occurs:
on every year that is evenly divisible by 4
except every year that is evenly divisible by 100
unless the year is also evenly divisible by 400
I hereby claim:
To claim this, I am signing this object:
| Average percent of files using implicits: 23.35670325912466 | |
| --- | |
| spark | |
| Total # Scala files: 2760 | |
| Total # files using implicits: 163 | |
| Percent of files using implicits 5.905797101449275% | |
| --- | |
| incubator-predictionio | |
| Total # Scala files: 404 |
| 93.33333333333333% of top 120 Scala GitHub projects make use of implicits. | |
| Only 6.666666666666667% of top 120 Scala GitHub projects don't use implicits at all | |
| 83.33333333333334% of top 120 Scala GitHub use implicit defs | |
| Total number of projects: 120 | |
| Number of projects not using implicits at all: | |
| 8 | |
| Number of projects using only implicit vals: |
To start, first download the assignment: timeusage.zip. For this assignment, you also need to download the data (164 MB):
http://alaska.epfl.ch/~dockermoocs/bigdata/atussum.csv
and place it in the folder src/main/resources/timeusage/ in your project
directory.
| My colleague Heather Miller and I have been discussing a new project that would | |
| focus on increasing the reliability and performance of applications based on | |
| Apache’s “Spark” engine for big data processing. The programming model we aim to | |
| improve seeks to achieve parallelism via distribution, by transmitting | |
| computations (closures) to a collection of sites where distributed data resides. | |
| The work we have in mind would have two areas of focus: (i) design, | |
| implementation, and evaluation of programming models that make this paradigm of | |
| shipping computations to distributed data more robust and usable, and less error | |
| prone (e.g., to avoid races, memory leaks, etc.) and (ii) design, | |
| implementation, and evaluation of tools for analyzing and refactoring of Spark |
| CS 6240: Parallel Data Processing | |
| This course covers techniques for managing and analyzing very large data sets, | |
| with an emphasis on approaches that scale out effectively as more compute nodes | |
| are added. Principles of distributed data management and strategies for | |
| problem-driven data partitioning are introduced through a selection of design | |
| patterns from various application domains, including graph analysis, databases, | |
| text processing, and data mining. Coursework includes hands-on programming | |
| experience with modern big-data processing technology such as MapReduce, Spark, | |
| HBase, and Cloud Computing. (This selection is subject to change as technology |