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Let me tell you the difference between DDL,DML,TCL and DCL:


DDL COMMANDS:
CREATE,ALTER,DROP AND TRUNCATE ARE CALLED DDL COMMANDS. They are called Data Definition since they are used for defining the data. That is the structure of the data is known through these DDL commands.

DML COMMANDS:
DML commands are used for data manipulation. Some of the DML commands
insert,select,update,delete etc. Even though select is not exactly a DML language command oracle still recommends you to consider SELECT as an DML command.

TCL:
For revoking the transactions and to make the data commit to the database we use TCL. Some of the TCL commands are:
1. ROLLBACK
2. COMMIT

ROLLBACK is used for revoking the transactions until last commit.
COMMIT is used for commiting the transactions to the database.
Once we commit we cannot rollback. Once we rollback we cannot commit.
Commit and Rollback are generally used to commit or revoke the transactions that are with regard to DML commands.

DCL:
Data Control Language is used for the control of data. That is a user can access any data based on the priveleges given to him. This is done through DATA CONTROL LANGUAGE. Some of the DCL Commands are:
1. GRANT
2. REVOKE.

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Hypothesis Testing and T-Test


Hypothesis is an advanced testing library for Python. It lets you write tests which are parametrized by a source of examples, and then generates simple and comprehensible examples that make your tests fail.

http://hamelg.blogspot.in/2015/11/python-for-data-analysis-part-24.html

Python Introduction


Welcome to Google’s Python online tutorial. It is based on the introductory Python course offered internally. Originally created during the Python 2.4 days, we’ve tried to keep the content universal and exercises relevant, even for newer releases. As mentioned on the setup page, this material covers Python 2. While we recommend “avoiding” Python 3 for now, recognize that it is the future, as all new features are only going there. The good news is that developers learning either version can pick up the other without too much difficulty. If you want to know more about choosing Python 2 vs. 3, check out this post.

https://developers.google.com/edu/python/introduction

Compound interest


Jacob Bernoulli discovered this constant in 1683 by studying a question about compound interest:[5]

An account starts with $1.00 and pays 100 percent interest per year. If the interest is credited once, at the end of the year, the value of the account at year-end will be $2.00. What happens if the interest is computed and credited more frequently during the year?

If the interest is credited twice in the year, the interest rate for each 6 months will be 50%, so the initial $1 is multiplied by 1.5 twice, yielding $1.00×1.52 = $2.25 at the end of the year. Compounding quarterly yields $1.00×1.254 = $2.4414…, and compounding monthly yields $1.00×(1+1/12)12 = $2.613035… If there are ncompounding intervals, the interest for each interval will be 100%/n and the value at the end of the year will be $1.00×(1 + 1/n)n.

Bernoulli noticed that this sequence approaches a limit (the force of interest) with larger n and, thus, smaller compounding intervals. Compounding weekly (n = 52) yields $2.692597…, while compounding daily (n = 365) yields $2.714567…, just two cents more. The limit as n grows large is the number that came to be known as e; with continuous compounding, the account value will reach $2.7182818…. More generally, an account that starts at $1 and offers an annual interest rate of R will, after t years, yield eRt dollars with continuous compounding. (Here R is the decimal equivalent of the rate of interest expressed as a percentage, so for 5% interest, R = 5/100 = 0.05)

https://en.wikipedia.org/wiki/E_(mathematical_constant)

 

Sitworld Analytics Team

Free eBooks from Microsoft Press for data science.


The world around us—every business and nearly every industry—is being transformed by technology. SQL Server 2016 was built for this new world and to help businesses get ahead of today’s disruptions. With this free ebook, learn to install, configure, and use Microsoft’s SQL Server R Services in data science projects. R is one of the most popular, powerful data analytics languages and environments in use by data scientists.

https://mva.microsoft.com/ebooks/

Sitworld Analytics Team

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