Cracking the Data Science Interview : Unlock Insider Tips from Industry Experts to Master the Data Science Field / Leondra R. Gonzalez and Aaren Stubberfield ; foreword by Angela Baltes.

Format:

Book

Author/Creator:

Gonzalez, Leondra R., author.

Stubberfield, Aaren, author.

Contributor:

Baltes, Angela, writer of foreword.

Language:

English

Subjects (All):

Employment interviewing.

Information science--Vocational guidance.

Information science.

Electronic data processing--Vocational guidance.

Electronic data processing.

Physical Description:

1 online resource (404 pages)

Edition:

First edition.

Place of Publication:

Birmingham, England : Packt Publishing, [2024]

Summary:

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 Acquire highly sought-after skills of the trade, including Python, SQL, statistics, and machine learning Gain the confidence to explain complex statistical, machine learning, and deep learning theory Extend your expertise beyond model development with version control, shell scripting, and model deployment fundamentals Purchase of the print or Kindle book includes a free PDF eBook 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 Explore data science trends, job demands, and potential career paths Secure interviews with industry-standard resume and portfolio tips Practice data manipulation with Python and SQL Learn about supervised and unsupervised machine learning models Master deep learning components such as backpropagation and activation functions Enhance your productivity by implementing code versioning through Git Streamline workflows using shell scripting for increased efficiency 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.

Contents:

Cover

Copyright

Foreword

Contributors

Table of Contents

Preface

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

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

Part 2: Manipulating and Managing Data

Chapter 3: Programming with Python

Using variables, data types, and data structures

Indexing in Python

Using string operations

Initializing a string

String indexing

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.

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

Handling files in Python

Opening files with pandas

Wrangling data with pandas

Handling missing data

Selecting data

Sorting data

Merging data

Aggregation with groupby()

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

Surveying tools of the trade

Power BI

Tableau

Shiny

ggplot2 (R)

Matplotlib (Python)

Seaborn (Python)

Developing dashboards, reports, and KPIs

Developing charts and graphs

Bar chart - Matplotlib

Bar chart - Seaborn

Scatter plot - Matplotlib

Scatter plot - Seaborn

Histogram plot - Matplotlib

Histogram plot - Seaborn

Applying scenario-based storytelling

Chapter 5: Querying Databases with SQL

Introducing relational databases

Mastering SQL basics

The SELECT statement

The WHERE clause

The ORDER BY clause

Aggregating data with GROUP BY and HAVING

The GROUP BY statement

The HAVING clause

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

Merging tables with joins

Inner joins

Left and right join

Full outer join

Multi-table joins

Calculating window functions

OVER, ORDER BY, PARTITION, and SET

LAG and LEAD

ROW_NUMBER

RANK and DENSE_RANK

Using date functions

Approaching complex queries

Summary.

Chapter 6: Scripting with Shell and Bash Commands in Linux

Introducing operating systems

Navigating system directories

Introducing basic command-line prompts

Understanding directory types

Filing and directory manipulation

Scripting with Bash

Introducing control statements

Creating functions

Processing data and pipelines

Using pipes

Using cron

Chapter 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

Detailing the Git workflow for data scientists

Using Git tags for data science

Understanding Git tags

Using tagging as a data scientist

Understanding common operations

Part 3: Exploring Artificial Intelligence

Chapter 8: Mining Data with Probability and Statistics

Describing data with descriptive statistics

Measuring central tendency

Measuring variability

Introducing populations and samples

Defining populations and samples

Representing samples

Reducing the sampling error

Understanding the Central Limit Thereom (CLT)

The CLT

Demonstrating the assumption of normality

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

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

Understanding Type I and Type II errors

Type I error (false positive)

Type II error (false negative)

Striking a balance

References.

Chapter 9: Understanding Feature Engineering and Preparing Data for Modeling

Chapter 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

Logistic regression

k-nearest neighbors (k-NN)

Random forest

Extreme Gradient Boosting (XGBoost)

Getting started with unsupervised machine learning

K-means

Density-based spatial clustering of applications with noise (DBSCAN)

Other clustering algorithms

Evaluating clusters

Summarizing other notable machine learning models

Understanding the bias-variance trade-off

Tuning with hyperparameters

Grid search

Random search

Bayesian optimization

Chapter 11: Building Networks with Deep Learning

Introducing neural networks and deep learning

Weighing in on weights and biases

Introduction to weights

Introduction to biases

Activating neurons with activation functions

Common activation functions

Choosing the right activation function

Unraveling backpropagation

Gradient descent

What is backpropagation?

Loss functions

Gradient descent steps

The vanishing gradient problem

Using optimizers

Optimization algorithms

Network tuning

Understanding embeddings

Word embeddings

Training embeddings

Listing common network architectures

Common networks

Tools and packages

Introducing GenAI and LLMs

Unveiling language models

Transformers and self-attention

Transfer Learning

GPT in action

Chapter 12: Implementing Machine Learning Solutions with MLOps

Introducing MLOps

A model pipeline overview

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

Validating and monitoring the model

Validating the model deployment

Model monitoring

Thinking about governance

Using Azure ML for MLOps

Part 4: Getting the Job

Chapter 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

The panel stage

Chapter 14: Negotiating Compensation

Understanding the compensation landscape

Negotiating the offer

Negotiation considerations

Responding to the offer

Maximum negotiable compensation and situational value

Final words

Index

Other Books You May Enjoy.

Notes:

Description based upon print version of record.

Chapter 5: Querying Databases with SQL

Includes bibliographical references and index.

Description based on print version record.

ISBN:

9781805120193

1805120190

OCLC:

1423131341