Cracking the Data Science Interview Unlock insider tips from industry experts to master the data science field 1st edition by Leondra Gonzalez, Aaren Stubberfield 1805120506 9781805120506

Cracking the Data Science Interview: Unlock insider tips from industry experts to master the data science field 1st edition by Leondra R. Gonzalez, Aaren Stubberfield – Ebook PDF Instant Download/DeliveryISBN:  1805120506, 9781805120506 

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Product details:

ISBN-10 :  1805120506 

ISBN-13 :  9781805120506

Author:  Leondra R. Gonzalez, Aaren Stubberfield 

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.

 

Cracking the Data Science Interview: Unlock insider tips from industry experts to master the data science field 1st Table of contents:

Part 1: Breaking into the Data Science Field
Chapter 1: Exploring Today’s Modern Data Science Landscape
What is data science?
Exploring the data science process
Data collection
Data exploration
Data modeling
Model evaluation
Model deployment and monitoring
Dissecting the flavors of data science
Data engineer
Dashboarding and visual specialist
ML specialist
Domain expert
Reviewing career paths in data science
The traditionalist
Domain expert
Off-the-beaten path-er
Tackling the experience bottleneck
Academic experience
Work experience
Understanding expected skills and competencies
Hard (technical) skills
Soft (communication) skills
Exploring the evolution of data science
New models
New environments
New computing
New applications
Summary
References
Chapter 2: Finding a Job in Data Science
Searching for your first data science job
Preparing for the road ahead
Finding job boards
Beginning to build a standout portfolio
Applying for jobs
Constructing the Golden Resume
The perfect resume myth
Understanding automated resume screening
Crafting an effective resume
Formatting and organization
Using the correct terminology
Prepping for landing the interview
Moore’s Law
Research, research, research
Branding
References
Part 2: Manipulating and Managing Data
Chapter 3: Programming with Python
Using variables, data types, and data structures
Assessment
Answers
Indexing in Python
Using string operations
Initializing a string
String indexing
Assessment
Answers
Assessment
Answers
Using Python control statements, loops, and list comprehensions
Conditional statements such as if, elif, and else
Loop statements such as for and while
List comprehension
Assessment
Answer
Assessment
Answer
Using user-defined functions
Breaking down the user-defined function syntax
Doing “stuff” with user-defined functions
Getting familiar with lambda functions
Creating good functions
Assessment
Answers
Handling files in Python
Opening files with pandas
Assessment
Answers
Wrangling data with pandas
Handling missing data
Selecting data
Sorting data
Merging data
Aggregation with groupby()
Assessment
Answer
Assessment
Answer
Assessment
Answer
Assessment
Answer
Assessment
Answer
Assessment
Answer
Summary
References
Chapter 4: Visualizing Data and Data Storytelling
Understanding data visualization
Bar charts
Line charts
Scatter plots
Histograms
Density plots
Quantile-quantile plots (Q-Q plots)
Box plots
Pie charts
Assessment
Answer
Assessment
Answer
Surveying tools of the trade
Power BI
Tableau
Shiny
ggplot2 (R)
Matplotlib (Python)
Seaborn (Python)
Assessment
Answer
Developing dashboards, reports, and KPIs
Assessment
Answer
Developing charts and graphs
Bar chart – Matplotlib
Bar chart – Seaborn
Scatter plot – Matplotlib
Scatter plot – Seaborn
Histogram plot – Matplotlib
Histogram plot – Seaborn
Assessment
Answer
Applying scenario-based storytelling
Assessment
Answer
Assessment
Answer
Summary
Chapter 5: Querying Databases with SQL
Introducing relational databases
Mastering SQL basics
The SELECT statement
The WHERE clause
The ORDER BY clause

 

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