LEADER 05285nam 2200637 450 001 9910460073803321 005 20200520144314.0 010 $a1-118-96895-6 010 $a1-118-96809-3 035 $a(CKB)3710000000230451 035 $a(EBL)1779318 035 $a(SSID)ssj0001375109 035 $a(PQKBManifestationID)11753935 035 $a(PQKBTitleCode)TC0001375109 035 $a(PQKBWorkID)11332466 035 $a(PQKB)10668146 035 $a(MiAaPQ)EBC1779318 035 $a(CaSebORM)9781118968086 035 $a(Au-PeEL)EBL1779318 035 $a(CaPaEBR)ebr10927736 035 $a(CaONFJC)MIL642282 035 $a(OCoLC)890441660 035 $a(EXLCZ)993710000000230451 100 $a20140916h20142014 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aCracking the tech career $einsider advice on landing a job at Google, Microsoft, Apple, or any top tech company /$fGayle Laakmann McDowell 205 $a2nd ed. 210 1$aHoboken, New Jersey :$cWiley,$d2014. 210 4$dİ2014 215 $a1 online resource (285 p.) 300 $aIncludes index. 311 $a1-322-11031-X 311 $a1-118-96808-5 327 $aCracking the Tech Career: Insider Advice on Landing a Job at Google, Microsoft, Apple, or any Top Tech Company; Contents; Chapter 1: Life at the World's Greatest Tech Companies; Life at Infinite Loop and Microsoft Way; Youthful; Perks; Work/Life Balance; Moving Up: Individual Contributors; The Differences; Big versus Little: Is a Start-Up Right for You?; The Good; The Bad; The Ugly; The Job Title: What Do You Want to Be When You Grow Up?; What Do You Need?; How Do You Enjoy Working?; What Are You Good At?; It's Not for Everyone; Chapter 2: Advanced Positioning and Preparation 327 $aA Positioning FrameworkRelevant Skills; Prestige/Credibility; Technical Connection; Something Special; University; Elite Schools: What's in a Name?; Majors; Minors; Learn to Code; Get Project Experience; Grade Point Average: Does It Matter and What Can You Do?; Doctor Who? Getting to Know Professors; Graduate School; The True Cost of Graduate School; Career Graduate Degrees; Preparing Now; The MBA; What's an MBA Worth?; Should You Get an MBA?; Preparing Now; Your "Story"; Part-Time Graduate Programs; Work Experience; Make an Impact; Become a [Half a] Generalist 327 $aSize Matters: Quantify Your ImpactPart-Time Jobs and Internships; Extracurriculars; Volunteering; Start Something; Questions and Answers; Well, There Go the College Hires; Will Code for Food; The Un-manager; Chapter 3: Getting in the Door; The Black Hole: Online Job Submission; Making the Best of the Black Hole; Getting a Personal Referral; The Informational Interview; Reach Out to Recruiters; Alumni Network and Beyond; Career Fairs; Professional Recruiters; When Things Get Ugly: What to Watch Out For; Additional Avenues; Start Elsewhere; Contract Roles; Get Creative; Official Groups 327 $aNetworkingAttributes of a Good Network; How to Build a Great Network; Where to Network; Social Networking; LinkedIn; Facebook; Twitter; Build an Online Portfolio; Questions and Answers; Applying from Afar; Distant Relations; Just Following Instructions; Chapter 4: Resumes; How Resumes Are Read; Nine Hallmarks of a Powerful Resume; 1. Short and Sweet; 2. Accomplishment Oriented; 3. Quantifiable Results; 4. Well Targeted; 5. Universally Meaningful; 6. Professional; 7. Well Formatted; 8. List Your Projects and Extracurriculars; 9. Be Different (If You Want); The Structure; The Objective 327 $aSummary (or Key Accomplishments)Work Experience; Projects and Leadership Experience; Education; Skills; Awards and Honors; How Do I Shorten My Resume?; Resume Action Words; Questions and Answers; It's a Family Matter; On the Up and Up; But Seriously; Chapter 5: Deconstructing the Resume; Resume #1; Sample Resume; Assessment; Improved Resume; Resume #2; Sample Resume; Assessment; Improved Resume; Resume #3; Sample Resume; Assessment; Improved Resume; Resume #4; Sample Resume; Assessment; Improved Resume; Chapter 6: Cover Letters; Why a Cover Letter?; The Three Types of Cover Letter 327 $aSolicited Cover Letter 330 $aBecome the applicant Google can''t turn down Cracking the Tech Career is the job seeker''s guide to landing a coveted position at one of the top tech firms. A follow-up to The Google Resume, this book provides new information on what these companies want, and how to show them you have what it takes to succeed in the role. Early planners will learn what to study, and established professionals will discover how to make their skillset and experience set them apart from the crowd. Author Gayle Laakmann McDowell worked in engineering at Google, and interviewed over 120 candidates as a member of the 606 $aComputer industry$xVocational guidance 608 $aElectronic books. 615 0$aComputer industry$xVocational guidance. 676 $a004.023 700 $aMcDowell$b Gayle Laakmann$0979007 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910460073803321 996 $aCracking the tech career$92231727 997 $aUNINA LEADER 03972nam 2200625Ia 450 001 9910829921703321 005 20170810191550.0 010 $a1-280-36700-8 010 $a9786610367009 010 $a0-470-31182-7 010 $a0-471-46166-0 010 $a0-471-24970-X 035 $a(CKB)111087027121356 035 $a(EBL)157071 035 $a(OCoLC)475872690 035 $a(SSID)ssj0000130321 035 $a(PQKBManifestationID)11146398 035 $a(PQKBTitleCode)TC0000130321 035 $a(PQKBWorkID)10082121 035 $a(PQKB)11452153 035 $a(MiAaPQ)EBC157071 035 $a(PPN)169570053 035 $a(EXLCZ)99111087027121356 100 $a20010706d2002 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aConvexity and optimization in R [superscript n]$b[electronic resource] /$fLeonard D. Berkovitz 210 $aNew York $cJ. Wiley$dc2002 215 $a1 online resource (283 p.) 225 1 $aPure and applied mathematicss 300 $aDescription based upon print version of record. 311 $a0-471-35281-0 320 $aIncludes bibliographical references (p. 261-262) and index. 327 $aCONVEXITY AND OPTIMIZATION IN R(n); CONTENTS; Preface; I Topics in Real Analysis; 1. Introduction; 2. Vectors in R(n); 3. Algebra of Sets; 4. Metric Topology of R(n); 5. Limits and Continuity; 6. Basic Property of Real Numbers; 7. Compactness; 8. Equivalent Norms and Cartesian Products; 9. Fundamental Existence Theorem; 10. Linear Transformations; 11. Differentiation in R(n); II Convex Sets in R(n); 1. Lines and Hyperplanes in R(n); 2. Properties of Convex Sets; 3. Separation Theorems; 4. Supporting Hyperplanes: Extreme Points; 5. Systems of Linear Inequalities: Theorems of the Alternative 327 $a6. Affine Geometry7. More on Separation and Support; III Convex Functions; 1. Definition and Elementary Properties; 2. Subgradients; 3. Differentiable Convex Functions; 4. Alternative Theorems for Convex Functions; 5. Application to Game Theory; IV Optimization Problems; 1. Introduction; 2. Differentiable Unconstrained Problems; 3. Optimization of Convex Functions; 4. Linear Programming Problems; 5. First-Order Conditions for Differentiable Nonlinear Programming Problems; 6. Second-Order Conditions; V Convex Programming and Duality; 1. Problem Statement 327 $a2. Necessary Conditions and Sufficient Conditions3. Perturbation Theory; 4. Lagrangian Duality; 5. Geometric Interpretation; 6. Quadratic Programming; 7. Duality in Linear Programming; VI Simplex Method; 1. Introduction; 2. Extreme Points of Feasible Set; 3. Preliminaries to Simplex Method; 4. Phase II of Simplex Method; 5. Termination and Cycling; 6. Phase I of Simplex Method; 7. Revised Simplex Method; Bibliography; Index 330 $aA comprehensive introduction to convexity and optimization in RnThis book presents the mathematics of finite dimensional constrained optimization problems. It provides a basis for the further mathematical study of convexity, of more general optimization problems, and of numerical algorithms for the solution of finite dimensional optimization problems. For readers who do not have the requisite background in real analysis, the author provides a chapter covering this material. The text features abundant exercises and problems designed to lead the reader to a fundamental understanding of t 410 0$aPure and applied mathematics (John Wiley & Sons : Unnumbered) 606 $aConvex sets 606 $aMathematical optimization 615 0$aConvex sets. 615 0$aMathematical optimization. 676 $a516/.08 676 $a519.3 700 $aBerkovitz$b Leonard David$f1924-$0283994 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910829921703321 996 $aConvexity and optimization in R$93935798 997 $aUNINA