Name: Cracking the Data Science Interview
Author: Leondra R. Gonzalez Aaren Stubberfield Angela Baltes
ISBN: 9781805120193

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Cracking the Data Science InterviewLeondra R. Gonzalez Aaren Stubberfield Angela Baltes

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Cracking the Data Science Interview

Leondra R. Gonzalez Aaren Stubberfield Angela Baltes

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Foreword
Contributors
About the authors
About the reviewer
Preface
Who this book is for
What this book covers
To get the most out of this book
Conventions used
Special Note
Get in touch
Share Your Thoughts
Download a free PDF copy of this book
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
Dissecting the flavors of data science
Reviewing career paths in data science
Tackling the experience bottleneck
Understanding expected skills and competencies
Exploring the evolution of data science
Summary
Chapter 2 Finding a Job in Data Science
Searching for your first data science job
Constructing the Golden Resume
Prepping for landing the interview
Part 2 Manipulating and Managing Data
Chapter 3 Programming with Python
Using variables, data types, and data structures
Indexing in Python
Using string operations
Using Python control statements, loops, and list comprehensions
Using user-defined functions
Handling files in Python
Wrangling data with pandas
Summary
Chapter 4 Visualizing Data and Data Storytelling
Understanding data visualization
Surveying tools of the trade
Developing dashboards, reports, and KPIs
Developing charts and graphs
Applying scenario-based storytelling
Summary
Chapter 5 Querying Databases with SQL
Introducing relational databases
Mastering SQL basics
Aggregating data with GROUP BY and HAVING
Creating fields with CASE WHEN
Analyzing subqueries and CTEs
Merging tables with joins
Calculating window functions
Approaching complex queries
Summary
Chapter 6 Scripting with Shell and Bash Commands in Linux
Introducing operating systems
Navigating system directories
Filing and directory manipulation
Scripting with Bash
Introducing control statements
Creating functions
Processing data and pipelines
Using cron
Summary
Chapter 7 Using Git for Version Control
Introducing repositories (repos)
Creating a repo
Detailing the Git workflow for data scientists
Using Git tags for data science
Understanding common operations
Summary
Part 3 Exploring Artificial Intelligence
Chapter 8 Mining Data with Probability and Statistics
Describing data with descriptive statistics
Introducing populations and samples
Understanding the Central Limit Thereom (CLT)
Shaping data with sampling distributions
Testing hypotheses
Understanding Type I and Type II errors
Summary
Chapter 9 Understanding Feature Engineering and Preparing Data for Modeling
Understanding feature engineering
Applying data transformations
Engineering categorical data and other features
Performing feature selection
Working with imbalanced data
Reducing the dimensionality
Summary
Chapter 10 Mastering Machine Learning Concepts
Introducing the machine learning workflow
Getting started with supervised machine learning
Getting started with unsupervised machine learning
Summarizing other notable machine learning models
Understanding the bias-variance trade-off
Tuning with hyperparameters
Summary
Chapter 11 Building Networks with Deep Learning
Introducing neural networks and deep learning
Weighing in on weights and biases
Activating neurons with activation functions
Unraveling backpropagation
Using optimizers
Understanding embeddings
Listing common network architectures
Introducing GenAI and LLMs
Summary
Chapter 12 Implementing Machine Learning Solutions with MLOps
Introducing MLOps
Understanding data ingestion
Learning the basics of data storage
Reviewing model development
Packaging for model deployment
Deploying a model with containers
Validating and monitoring the model
Using Azure ML for MLOps
Summary
Part 4 Getting the Job
Chapter 13 Mastering the Interview Rounds
Mastering early interactions with the recruiter
Mastering the different interview stages
Summary
Chapter 14 Negotiating Compensation
Understanding the compensation landscape
Negotiating the offer
Summary
Final words
Index
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Cracking the Data Science Interview