Andrei Besedin - How To Learn Microsoft SQL Server Quickly!

How To Learn Microsoft SQL Quickly

By

Andrei Besedin

www.wiseexcel.com

Copyright 2018

Text Copyright [Andrei Besedin]
All rights reserved. No part of this guide may be reproduced in any form without permission in writing from the publisher except in the case of brief quotations embodied in critical articles or reviews.

Legal & Disclaimer

The information contained in this book and its contents is not designed to replace or take the place of any form of medical or professional advice; and is not meant to replace the need for independent medical, financial, legal or other professional advice or services, as may be required. The content and information in this book havebeen provided for educational and entertainment purposes only.

The content and information contained in this book havebeen compiled from sources deemed reliable, and it is accurate to the best of the Author's knowledge, information,and belief. However, the Author cannot guarantee its accuracy and validity and cannot be held liable for any errors and omissions. Furthermore, changes can be periodically made to this book as and when needed. Where appropriate and necessary, you must consult a professionalbefore using any of the suggested remedies, techniques, or information in this book.

Upon using the contents and information contained in this book, you agree to hold harmless the Author from and against any damages, costs, and expenses, including any legal fees potentially resulting from the application of any of the information provided by this book. This disclaimer applies to any loss, damages or injury caused by the use and application, whether directly or indirectly, of any advice or information presented, whether for breach of contract, tort, negligence, personal injury, criminal intent, or under any other cause of action.

You agree to accept all risks of using the information presented inside this book.

You agree that by continuing to read this book, where appropriate and/or necessary, you shall consult a professional before using any of the suggested remedies, techniques, or information in this book.

Using th bk

In this book, ull lrn how to ntll, configure, and u

Mrft SQL Server R Srv n data science rjt. Wr umng tht u have familiarity wth dt n and, most important, th R language. But if you dnt, wv ddd a tn here t help you get started with th wrful dt-nl nvrnmnt.

For the data science or R professional

Data science a rltvl nw term, and it h fw definitions. For th bk, wll use the nm tlf t define t. Thu a dt science rfnl is a technical rfnl who uses a ntf rh (k a utn, creates an hthr mr accurately a modeltests th hth, nd thn mmunt th rult) in th dt-nlt r, whether ung structured or untruturd data, or rh both.

Wr umng that u hv a bkgrund n gnrl mathematics, m lnr algebra, nd, f ur, an n-dth familiarity wth statistics. Wr l umng that you knw the R language andits processing nvrnmnt nd r fmlr wth how to load vru kg nd tht u undrtnd when t use R fr a given data solution. But even if you dnt hv those kll, read on; we hv m rur that u can u.

Evn if you hv a d bkgrund n ttt nd R, Mrft SQL Srvr might b new to you. To lrn how t wrk with t, takea lk at th section SQL Server nd Transact-SQL later in this htr. In th bk, well assume tht u hv a wrkng knowledge f hw SQL Srvr rt, nd how t read nd wrt Transact-SQLthe dlt f the SQL lngug tht Mrft implements in SQL Server.

In th two chapters that follow, wll hw u what SQL Srvr R Srv ll but nd hw u n install t. Youll lrn th client tools nd th way t work with R Srv, nd well fllw tht up wth a wlk-thrugh ung th dt n r.

One f th bt w to lrn t wrk with a rdut t dntrut m rtl xml n whh t ud. In th rest f th bk, weve ut tgthr representative, rl-wrld u cases tht dmntrt an nd-t-nd lutn fr a typical data n project. Th r examples ull fnd n thr data n tl, u huld b bl to extrapolate th nt f wht u lrd knw to how u n do th m thng in SQL Server ung R Srvw think ull fnd t has.

Solution example: customer churn

On of th most nnl uses fr prediction n is customer churn. Cutmr hurn dfnd th number f lt utmr dvdd b th number f new utmr gained. A long youre gnng new utmr ftr thn youre losing thm, tht a gd thng, rght? Its nt for multl rn. Th rmr rn customer hurn is a bad thing tht t t fr mr to gn a customer, or rgn a lt one than it d to keep n xtng utmr. Ovr tm, t muh utmr hurn n lwl drn th rft from a mn. Idntfng utmr hurn nd th ftr tht cause it r essential tasks fr a mn to t rftbl.

Intrtngl, utmr churn xtrlt ut to thr uses, well. Fr ntn, in a htl, u wnt utmr to hurnt nt m back. You wnt them t t healthy ftr thr htl vt. In th xml, well hw u how to lult and locate utmr hurn by using R nd SQL Server data.

Solution example: predictive maintenance and the Internet of Things

It rtl fr bun rtng r utilizing umnt t k those components running as fftvl bl because equipment dwntm or flur n hv a ngtv mt bnd jut th cost f repair. Predictive mntnn is defined a technique t forecast whn n n-rv mhn will fl that maintenance can be planned. It includes mr gnrl thnu that involve understanding fult, flur, nd tmng f maintenance. It is wdl ud across a vrt f industries, such r, energy, mnufturng, nd transportation nd logistics.

New rdtv mntnn techniques nlud tm-vrng features nd r not as bund to model-driven processes. Th mrgng Intrnt of Thng (IoT) technologies h opened u th door to a wrld of rtunt n th r, wth mr nr bng ntlld n dv nd more data bng lltd about th devices. A a rult, dt-drvn thnu nw promise to unlh th tntl f ung dt to undrtnd whn t rfrm maintenance.

In th example, w'll show u dffrnt w f formulating a predictive mntnn rblm nd thn hw u hw to solve thm by ung R and SQL Srvr.

Solution example: forecasting

Frtng dfnd as the r of making futur predictions b using htrl dt, nludng trnd, seasonal patterns, xgnu ftr, nd any vlbl futur dt. It widely ud in many applications nd critical bun dn dnd n having n urt forecast. Mtrlgt u it t gnrt weather predictions; CFO u it t generate rvnu forecasts; Wll Street nlt use it t predict tk prices, nd nvntr mngr u t t frt dmnd nd ul of mtrl.

Mn businesses td u ulttv judgmentbased frtng mthd nd tll mng thr frt in Microsoft Exl, or lll on n R wrkttn. Orgnztn f gnfnt challenges wth th rh bu th munt nd vlblt of rlvnt dt have grwn xnntll. Ung SQL Srvr R Services, it bl t rt tttll rlbl frt n an utmtd fhn giving organizations grtr confidence and bun rnvn.

In th tn, w will introduce b forecasting concepts,and nr nd thn illustrate hw t gnrt frt by using SQL Srvr R Srv.

If you r nw t R and ur interested n lrnng mr bfr u dive into these examples, rd on. You hv a fw things t lrn, but t nt too dffult f u tk wth it. A ur fvrt hlhr, And Griffith, wuld , Ant nothing good, . Althugh tht might not be grammatically correct, the sentiment that youre but t mbrk n a jurn wth a very wrful tool, nd wth grt wr m great rnblt. It wll tk time nd effort n ur part to lrn to u th tl rrtl.

R ud t r data and has wrful tttl capabilities. In most , whn you run a statistical formula n a set f numbr, ull gt n nwrwhh nt lw tru f mn languages. But when u process tttl dt, youre ftn left with n ddtnl t f t nvlvng interpreting and thn lng th nwr t a dn. Th mn that not nl r your dng kll t tk, ur rfnl ruttn , wll.

But, not t fr: thr are many lw-t nd even fr tn to brng you up t d. If ur a motivated lf-lrnr, ur n luk.

Thr n getting away frm mth when youre working wth R. To full mk u of th R language, ull nd three dln covered: gnrl math, linear lgbr, nd first- t second-year level experience wth statistics.

General math

Lt bgn with n undrtndng f b math, whh nlud th fllwng nt:

- Numbers Cuntng (nturl), whl, real, integers, rtnl, imaginary, mlx, binary, frtn nd scientific