Cracking the Data Science Interview : Unlock Insider Tips from Industry Experts to Master the Data Science Field

Cracking the Data Science Interview Unlock Insider Tips from Industry Experts to Master the Data Science Field

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...

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Bibliographic Details
Other Authors: Gonzalez, Leondra R., author (author), Stubberfield, Aaren, author (writer of foreword), Baltes, Angela, writer of foreword
Format: eBook
Language:Inglés
Published: Birmingham, England : Packt Publishing [2024]
Edition:First edition
Subjects:
Employment interviewing.
Information science > Vocational guidance.
Electronic data processing > Vocational guidance.
See on Biblioteca Universitat Ramon Llull:https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009805127406719
Table of 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
  • 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
  • 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()
  • 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
  • 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
  • Summary
  • 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
  • Summary
  • 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
  • Summary
  • 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
  • Summary
  • 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
  • Summary
  • 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
  • Summary
  • 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
  • Summary
  • 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
  • Summary
  • References
  • Chapter 14: Negotiating Compensation
  • Understanding the compensation landscape
  • Negotiating the offer
  • Negotiation considerations
  • Responding to the offer
  • Maximum negotiable compensation and situational value
  • Summary
  • Final words
  • Index
  • Other Books You May Enjoy.

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