Pelayo Arbués

  • Permanent Notes
    • 10 Years Later. Lessons from My PhD Experience
    • A Balanced Approach to Seeking Help
    • A lucky manager
    • A recommender beast
    • AI Enhanced Knowledge Management
    • Advanced Course on Product Engineering
    • Agents explained
    • Agile for Data Science
    • Bayesian reasoning
    • Be helpful
    • Bluesky feels like a breath of fresh air for data folks
    • Building a Semi-Automated Link Blog for Weekly Reads
    • Building to Forecast in Data Science
    • Career advice on skill acquisition
    • Change Resistance as a Corporate Autoimmune Disease
    • Change Resistance
    • Como contratar DS y no desesperar en el intento
    • Corporate antibodies
    • DRI
    • Data Paranoid
    • Data Science Fundamentals
    • Data Science job crafting
    • Data is not objective
    • Data teamwork as a transport service
    • December Always Hits Hard
    • Deferred Responsability
    • Different managerial styles
    • Dont get too rusty
    • Dopamine rush
    • Embracing Incompetence
    • Energy Management Confession
    • Essential Books for New Managers in Tech
    • Explaining AI-infused products
    • First solo data scientist
    • Fostering collaboration between teams
    • Generating images with your LoRA like a Pro
    • Glue work
    • Growth mindset
    • Headspace for managers
    • How I Manage Myself and My Team Using Obsidian Tasks
    • How to Hire Data Scientists Without Losing Your Mind
    • Ideal data to solve a problem
    • Internal Networking
    • LoRA. Low-Rank Adaptation of LLMs
    • MacBook Pro preparation for SD training and inference
    • Make'em talk with prototypes
    • Manage the data before thinking of AI
    • Mentoring as a form of leverage
    • Mentors and me
    • My failure resume
    • My workflow for my public second brain
    • New icon for the blog
    • No Data Product Management
    • No public speaking in 2024
    • Of innocents and criminals
    • Office hours
    • Other People Problems
    • Owl Drawing and Data Generation
    • POSSE against Platform Nudges on Content Creation
    • Power and Prediction
    • Pride of my team
    • Public Speaking is a Game-Changer for Networking
    • Radical Candor and Crucial Conversations
    • Rationality takes us closer to the truth
    • Rethinking Our Contributions to Social Media Platforms
    • Reversible and irreversible decision making
    • Rock stars vs Superstars
    • Short term and long term metrics
    • Stable Diffusion technicalities
    • Strategies for Landing Your First Job in Data Science
    • Sucessful Model
    • Taming Impostor Syndrome
    • Team size trade-off. Coordination costs Vs collective intelligence
    • The Data Vantage Point
    • The Power of Yet
    • The Rational Company
    • The Rise of the Dataset Engineer
    • The bitter feeling of publishing a Peer-Reviewed Paper Again
    • The next generation of weak learners
    • There is always going to be something you cannot fix
    • Time to manage
    • Training a LoRa of your face with Stable Diffusion 1.5
    • Training a Personal LoRA on Replicate Using FLUX.1-dev
    • Two books I wish I had read before starting my PhD
    • Understanding low level data science
    • Verbund in Data Science
    • Verbund
    • When Management Communication Techniques Enter Personal Life
    • Why Software Engineers Should Learn a Bit of Data Science
    • Why You Should Dive into Hand-Labeling Yourself
    • 2024 Reading Wrapped
    • Write well to solve problems
    • You need a growth mindset to get honest feedback
  • Research
    • A Stochastic Frontier Analysis Approach for Estimating Energy Demand and Efficiency in the Transport Sector of Latin America and the Caribbean
    • A dynamic approach to road freight flows modeling in Spain
    • A geo-referenced micro-data set of real estate listings for Spain’s three largest cities
    • Determinants of ground transport modal choice in long-distance trips in Spain
    • Intra-urban house prices in Madrid following the financial crisis, an exploration of spatial inequality
    • The spatial productivity of transportation infrastructure
    • Using machine learning to identify spatial market segments
  • Public Appearances
    • 2019
      • Databeers-XXX
        • Busco Pisco
      • Spanish Cadastre h2o
        • Boosting Spanish Cadastre with Machine Learning
    • 2020
      • Doing-DS-Nuevos-Profesionales-Digitales
        • Doing Data Science: Lessons Learned
      • Pensamiento-Digital
        • Podcast Pensamiento Digital
      • Spatial Autocorrelation
        • Spatial Autocorrelation is everywhere
    • 2021
      • buscando-vocaciones
        • Buscando Vocaciones
      • business-applications-DS-uam
        • Business Applications of Data Science
      • carto-2021
        • Automatic Valuation of Spanish Cadastre
      • nova-ds-beyond-the-hype
        • Data Science beyond the Hype
      • open-expo-mesa-redonda-ai
        • El futuro inmediato (y real) de la Inteligencia Artificial
      • x-talks-ai
        • Interview at podcast xTalks.AI
    • 2022
      • BdE-2022
        • ML en modelos hedonicos de valoracion inmobiliaria
      • Cruzando-datos-2022
        • Datos cruzados
      • enel-ninja-talk
        • Está la casa de mis sueños sobrevalorada o es un chollo
      • epi-gijon-lecciones-aprendidas
        • Lecciones aprendidas haciendo Ciencia de Datos
      • geoawesomeness
        • Using location Data to create amazing user experiences online
      • nuclio-data-science-sin-humo
        • Data Science Sin Humo
      • talent-hackers-interview
        • Data Science Sin Humo
    • 2023
      • data-on-the-rocks
        • Lecciones Aprendidas haciendo productos de datos
      • de-economistas-a-ds
        • De Economistas a Data Scientists
      • dive-data
        • Data Science al descubierto
      • luce-gijon
        • Inteligencia Artificial, smart cities y uso de datos
      • mesa-redonda-ai
        • De Economistas a Data Scientists
      • mioti-ds-mitos
        • Data Science al descubierto
  • Photography
    • Photography

Recent Notes

  • Power and Prediction

    Apr 30, 2025

  • Why Software Engineers Should Learn a Bit of Data Science

    Apr 01, 2025

  • A recommender beast

    Feb 05, 2025

See 90 more →

Home

❯

Map Of Contents

❯

Data Science Fundamentals

❯

Resources

❯

Statistics 101: Probability

Statistics 101: Probability

Apr 15, 20251 min read

  • learning
  • resources

Competence: Maths Level: Foundation

Every aspiring Data Scientist should get started by getting a solid background in Statistics. There are two books that can work as great 101 materials:

  • Introduction to Probability by Joe Blitzstein and Jessica Hwang. It is now available at http://probabilitybook.net, with videos and other resources.
  • Probability and Statistics by Morris H. DeGroot and Mark J. Schervish. It is called the Bible by some stats teachers.

Graph View

Backlinks

  • Data Science Fundamentals

Now Reading

  • Don't Sell Shovels, Sell Treasure Maps

    May 05, 2025

See 1333 more →

Created with Quartz, © 2025

  • Bluesky
  • Linkedin
  • Mastodon
  • Twitter
  • Unsplash
  • GitHub
  • RSS