- Read Scrum Guide
- Review Scrum Glossary
- Watch Agile Team Practices with Scrum 03:45:00
- Watch Scrum Master Skills 04:07:00
- Read Scrum Mastery
- Read Agile Project Management with Scrum
- Watch Scrum Fundamentals and Advanced
- Take the Scrum Open Assessment x3 times
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df1 <- createDataFrame(data.frame(a=c(1,2,3))) | |
df2 <- createDataFrame(data.frame(b=c(2,3,4))) | |
df1 = withColumn(df1, "index", monotonically_increasing_id()) | |
df2 = withColumn(df2, "index", monotonically_increasing_id()) | |
df_new = drop( | |
merge(df1, df2, by="index"), | |
c("index_x", "index_y")) |
Google Analytics (GA) has two tiers:
- Standard (Freemium), free of charge
- 360 Suite (Premium), flat annual fee $150,000
Both have limitations, which I'd categorize under Data Quality and Technology:
- Data Quality
- Data Sampling
GA uses sampling in generating many of its reports. This sacrifies accuracy in favor of query performance.
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apt-get install -y libpq-dev python-dev pkg-config libcgraph5 libfreetype6-dev libpng-dev g++ curl libxft-dev graphviz libgraphviz-dev | |
python2 -m pip install numpy scipy pandas matplotlib pytidylib simplejson requests openpyxl psycopg2 BeautifulSoup networkx ipywidgets pygraphviz | |
python2 -m pip install seaborn pydot dill nltk rarfile facebook-sdk requests_oauthlib pytidylib requests_futures xlrd sklearn us |
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rules = { 2**x: ['{0:03b}'.format(x).translate(string.maketrans("10","x.")), (x > 0) and (x & 3 == x or x & 3 == 3) * 1] for x in range(8) } |
/usr/bin/ruby -e "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/master/install)"
echo "gem: --no-document" >> ~/.gemrc
curl -L https://get.rvm.io | bash -s stable --auto-dotfiles --autolibs=enable
rvm install ruby --latest
rvm requirements
brew update
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{ | |
// Tools -> Build Systems -> New Build Systems | |
"cmd" : ["gcc -std=c99 $file_name -o ${file_base_name} && ./${file_base_name} -lm -Wall"], | |
"selector" : "source.c", | |
"shell": true, | |
"working_dir" : "$file_path" | |
} |
L1 cache reference ......................... 0.5 ns
Branch mispredict ............................ 5 ns
L2 cache reference ........................... 7 ns
Mutex lock/unlock ........................... 25 ns
Main memory reference ...................... 100 ns
Compress 1K bytes with Zippy ............. 3,000 ns = 3 µs
Send 2K bytes over 1 Gbps network ....... 20,000 ns = 20 µs
SSD random read ........................ 150,000 ns = 150 µs
Read 1 MB sequentially from memory ..... 250,000 ns = 250 µs
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