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.
Author(s): Leondra R. Gonzalez | Aaren Stubberfi eld
Publisher: Packt Publishing Limited
Year: 2024
Language: English
Pages: 540
Cracking the Data Science Interview
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
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
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
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
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
5
Querying Databases with SQL
Introducing relational databases
Mastering SQL basics
The SELECT statement
The WHERE clause
The ORDER BY clause
Assessment
Answer
Assessment
Answer
Aggregating data with GROUP BY and HAVING
The GROUP BY statement
The HAVING clause
Assessment
Answer
Creating fields with CASE WHEN
Analyzing subqueries and CTEs
Subqueries in the SELECT clause
Subqueries in the FROM clause
Subqueries in the WHERE clause
Subqueries in the HAVING clause
Distinguishing common table expressions (CTEs) from subqueries
Assessment
Answer
Assessment
Answer
Merging tables with joins
Inner joins
Left and right join
Full outer join
Multi-table joins
Assessment
Answer
Calculating window functions
OVER, ORDER BY, PARTITION, and SET
LAG and LEAD
Assessment
Answer
Assessment
Answer
ROW_NUMBER
RANK and DENSE_RANK
Assessment
Answer
Using date functions
Approaching complex queries
Assessment
Process and answer
Summary
6
Scripting with Shell and Bash Commands in Linux
Introducing operating systems
Navigating system directories
Introducing basic command-line prompts
Understanding directory types
Assessment
Answer
Assessment
Answer
Filing and directory manipulation
Assessment
Answer
Scripting with Bash
Assessment
Answer
Assessment
Answer
Introducing control statements
Assessment
Answer
Creating functions
Assessment
Answer
Processing data and pipelines
Using pipes
Assessment
Answer
Using cron
Assessment
Answer
Summary
7
Using Git for Version Control
Introducing repositories (repos)
Creating a repo
Cloning an existing remote repository
Creating a local repository from scratch
Linking local and remote repositories
Assessment
Answer
Assessment
Answer
Detailing the Git workflow for data scientists
Assessment
Answer
Assessment
Answer
Using Git tags for data science
Understanding Git tags
Using tagging as a data scientist
Understanding common operations
Assessment
Answer
Assessment
Answer
Summary
Part 3: Exploring Artificial Intelligence
8
Mining Data with Probability and Statistics
Describing data with descriptive statistics
Measuring central tendency
Measuring variability
Assessment
Answer
Assessment
Answer
Introducing populations and samples
Defining populations and samples
Representing samples
Reducing the sampling error
Assessment
Answer
Assessment
Answer
Understanding the Central Limit Thereom (CLT)
The CLT
Demonstrating the assumption of normality
Assessment
Answer
Assessment
Answer
Shaping data with sampling distributions
Probability distributions
Uniform distribution
Normal and student’s t-distributions
The binomial distribution
The Poisson distribution
Exponential distribution
Geometric distribution
The Weibull distribution
Assessment
Answer
Assessment
Answer
Testing hypotheses
Understanding one-sample t-tests
Understanding two-sample t-tests
Understanding paired sample t-tests
Understanding ANOVA and MANOVA
Chi-squared test
A/B tests
Assessment
Answer
Assessment
Answer
Understanding Type I and Type II errors
Type I error (false positive)
Type II error (false negative)
Striking a balance
Assessment
Answer
Assessment
Answer
Summary
References
9
Understanding Feature Engineering and Preparing Data for Modeling
Understanding feature engineering
Avoiding data leakage
Handling missing data
Scaling data
Applying data transformations
Introducing data transformations
Logarithm transformations
Power transformations
Box-Cox transformations
Exponential transformations
Engineering categorical data and other features
One-hot encoding
Label encoding
Target encoding
Calculated fields
Assessment
Answer
Assessment
Answer
Assessment
Answer
Performing feature selection
Types of feature selection
Recursive feature elimination
L1 regularization
Tree-based feature selection
The variance inflation factor
Working with imbalanced data
Understanding imbalanced data
Treating imbalanced data
Reducing the dimensionality
Principal component analysis
Singular value decomposition
t-SNE
Autoencoders
Summary
10
Mastering Machine Learning Concepts
Introducing the machine learning workflow
Problem statement
Model selection
Model tuning
Model predictions
Getting started with supervised machine learning
Regression versus classification
Linear regression – regression
Assessment
Answer
Assessment
Answer
Assessment
Answer
Assessment
Answer
Logistic regression
Assessment
Answer
Assessment
Answer
k-nearest neighbors (k-NN)
Assessment
Answer
Assessment
Answer
Assessment
Answer
Random forest
Assessment
Answer
Assessment
Answer
Assessment
Answer
Extreme Gradient Boosting (XGBoost)
Assessment
Answer
Assessment
Answer
Getting started with unsupervised machine learning
K-means
Assessment
Answer
Assessment
Answer
Density-based spatial clustering of applications with noise (DBSCAN)
Other clustering algorithms
Evaluating clusters
Assessment
Answer
Assessment
Answer
Assessment
Answer
Summarizing other notable machine learning models
Understanding the bias-variance trade-off
Assessment
Answer
Assessment
Answer
Assessment
Answer
Tuning with hyperparameters
Grid search
Random search
Bayesian optimization
Assessment
Answer
Assessment
Answer
Assessment
Answer
Summary
11
Building Networks with Deep Learning
Introducing neural networks and deep learning
Assessment
Answer
Assessment
Answer
Weighing in on weights and biases
Introduction to weights
Introduction to biases
Assessment
Answer
Activating neurons with activation functions
Common activation functions
Choosing the right activation function
Assessment
Answer
Assessment
Answer
Unraveling backpropagation
Gradient descent
What is backpropagation?
Loss functions
Gradient descent steps
The vanishing gradient problem
Assessment
Answer
Assessment
Answer
Using optimizers
Optimization algorithms
Network tuning
Assessment
Answer
Assessment
Answer
Understanding embeddings
Word embeddings
Training embeddings
Assessment
Answer
Listing common network architectures
Common networks
Tools and packages
Assessment
Answer
Introducing GenAI and LLMs
Unveiling language models
Transformers and self-attention
Assessment
Answer
Transfer Learning
GPT in action
Summary
12
Implementing Machine Learning Solutions with MLOps
Introducing MLOps
A model pipeline overview
Assessment
Answer
Understanding data ingestion
Learning the basics of data storage
Reviewing model development
Packaging for model deployment
Identifying requirements
Virtual environments
Tools and approaches for environment management
Deploying a model with containers
Using Docker
Assessment
Answer
Validating and monitoring the model
Validating the model deployment
Model monitoring
Thinking about governance
Using Azure ML for MLOps
Summary
Part 4: Getting the Job
13
Mastering the Interview Rounds
Mastering early interactions with the recruiter
Mastering the different interview stages
The hiring manager stage
The technical interview
Coding questions, step by step
Assessment
Answer
The panel stage
Summary
References
14
Negotiating Compensation
Understanding the compensation landscape
Negotiating the offer
Negotiation considerations
Responding to the offer
Maximum negotiable compensation and situational value
Assessment
Answer
Summary
Final words
Index
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