Cracking the Data Science Interview / Najlacnejšie knihy
Cracking the Data Science Interview

Kód: 45158638

Cracking the Data Science Interview

Autor Aaren Stubberfield

Rise above the competition and excel in your next interview with this one-stop guide to Python, SQL, version control, statistics, machine learning, and much moreKey FeaturesAcquire highly sought-after skills of the trade, includin ... celý popis

29.23

Bežne: 29.85 €

Ušetríte 0.62 €


Skladom u dodávateľa
Odosielame za 9 - 15 dní
Pridať medzi želanie

Mohlo by sa vám tiež páčiť

Darujte túto knihu ešte dnes
  1. Objednajte knihu a vyberte Zaslať ako darček.
  2. Obratom obdržíte darovací poukaz na knihu, ktorý môžete ihneď odovzdať obdarovanému.
  3. Knihu zašleme na adresu obdarovaného, o nič sa nestaráte.

Viac informácií

Viac informácií o knihe Cracking the Data Science Interview

Nákupom získate 71 bodov

Anotácia knihy

Rise above the competition and excel in your next interview with this one-stop guide to Python, SQL, version control, statistics, machine learning, and much more

Key Features

Book Description

The data science job market is saturated with professionals of all backgrounds, including academics, researchers, bootcampers, and Massive Open Online Course (MOOC) graduates. This poses a challenge for companies seeking the best person to fill their roles. At the heart of this selection process is the data science interview, a crucial juncture that determines the best fit for both the candidate and the company.

Cracking the Data Science Interview provides expert guidance on approaching the interview process with full preparation and confidence. Starting with an introduction to the modern data science landscape, you'll find tips on job hunting, resume writing, and creating a top-notch portfolio. You'll then advance to topics such as Python, SQL databases, Git, and productivity with shell scripting and Bash. Building on this foundation, you'll delve into the fundamentals of statistics, laying the groundwork for pre-modeling concepts, machine learning, deep learning, and generative AI. The book concludes by offering insights into how best to prepare for the intensive data science interview.

By the end of this interview guide, you'll have gained the confidence, business acumen, and technical skills required to distinguish yourself within this competitive landscape and land your next data science job.

What you will learn

Who this book is for

Whether you're a seasoned professional who needs to brush up on technical skills or a beginner looking to enter the dynamic data science industry, this book is for you. To get the most out of this book, basic knowledge of Python, SQL, and statistics is necessary. However, anyone familiar with other analytical languages, such as R, will also find value in this resource as it helps you revisit critical data science concepts like SQL, Git, statistics, and deep learning, guiding you to crack through data science interviews.

Table of Contents

  1. Exploring the Modern Data Science Landscape
  2. Finding a Job in Data Science
  3. Programming with Python
  4. Visualizing Data and Data Storytelling
  5. Querying Databases with SQL
  6. Scripting with Shell and Bash Commands in Linux
  7. Using Git for Version Control
  8. Mining Data with Probability and Statistics
  9. Understanding Feature Engineering and Preparing Data for Modeling
  10. Mastering Machine Learning Concepts
  11. Building Networks with Deep Learning
  12. Implementing Machine Learning Solutions with MLOps
  13. Mastering the Interview Rounds
  14. Negotiating Compensation

Parametre knihy

Zaradenie knihy Knihy po nemecky Naturwissenschaften, Medizin, Informatik, Technik Informatik, EDV Informatik, EDV - Sonstiges

29.23

Obľúbené z iného súdka



Osobný odber Bratislava a 12744 dalších

Copyright ©2008-26 najlacnejsie-knihy.sk Všetky práva vyhradenéSúkromieCookies


Môj účet: Prihlásiť sa
Všetky knihy sveta na jednom mieste. Navyše za skvelé ceny.

Nákupný košík ( prázdny )

Vyzdvihnutie v Zásielkovni
zadarmo nad 59,99 €.

Nachádzate sa: