From Data to Dollars
Getting started with Data Analytics and AI in Startups
Versandkostenfrei!
Erscheint vorauss. 10. Februar 2026
38,99 €
inkl. MwSt.
PAYBACK Punkte
19 °P sammeln!
Turn raw data into real traction and real revenue.This hands-on guide is built for the realities of startup life where time is short, resources are limited, and every decision counts. Whether you're just getting started or scaling fast, this book is your essential toolkit for building a data-driven startup ready for growth and innovation and for turning insight into impact, and data into dollars.Drawing from years of experience as the first data hire at multiple high-growth startups across the US and Europe, author Piotr Sidoruk shares practical insights into building data systems that are bot...
Turn raw data into real traction and real revenue.
This hands-on guide is built for the realities of startup life where time is short, resources are limited, and every decision counts. Whether you're just getting started or scaling fast, this book is your essential toolkit for building a data-driven startup ready for growth and innovation and for turning insight into impact, and data into dollars.
Drawing from years of experience as the first data hire at multiple high-growth startups across the US and Europe, author Piotr Sidoruk shares practical insights into building data systems that are both affordable and scalable, agile and reliable. You ll explore how to create a strong data foundation free from the rigidity of traditional corporate models, and how to use analytics and AI to inform product development, customer insights, and investor communication ultimately turning data into measurable business outcomes and revenue growth.
What sets this book apart is its unified approach instead of siloing data engineering, analytics, and machine learning, it brings them together into one actionable playbook tailored for startup environments. With real-world examples from companies lsuch as Spotify, Airbnb, and Stripe, you ll see how data can become a strategic asset from day one. From your first data hire to your next funding round, this book is your manual for building a startup where data drives every decision.
What You Will Learn
Develop a data-driven strategy aligned with startup goals, using rapid, iterative methods to support fast growthSet up and manage data warehouses, build pipelines, and integrate diverse data sources seamlesslyDesign and scale flexible, cost-effective data infrastructure tailored to startup constraintsUse data to drive decision making that directly impacts customer retention and revenueBuild dashboards and reports in BI tools to track the metrics that matter most for customer engagement, retention, and growthMaster data storytelling to influence stakeholders and secure investor fundingDemystify and apply advanced techniques in AI and machine learning to enhance innovation and competitivenessCalculate and monitor essential startup metrics, such as Customer Acquisition Cost (CAC), Lifetime Value (LTV), retention, and growth KPIsCombine quantitative data with qualitative insights to deeply understand user behavior and optimize product experience
Who This Book Is For
Data engineers, analysts, and scientists who are the first data hires at startups or preparing to work in fast-paced, high-growth startup environments. Ideal for professionals looking to build scalable data systems, drive product insights, and grow their careers in early-stage tech companies.
This hands-on guide is built for the realities of startup life where time is short, resources are limited, and every decision counts. Whether you're just getting started or scaling fast, this book is your essential toolkit for building a data-driven startup ready for growth and innovation and for turning insight into impact, and data into dollars.
Drawing from years of experience as the first data hire at multiple high-growth startups across the US and Europe, author Piotr Sidoruk shares practical insights into building data systems that are both affordable and scalable, agile and reliable. You ll explore how to create a strong data foundation free from the rigidity of traditional corporate models, and how to use analytics and AI to inform product development, customer insights, and investor communication ultimately turning data into measurable business outcomes and revenue growth.
What sets this book apart is its unified approach instead of siloing data engineering, analytics, and machine learning, it brings them together into one actionable playbook tailored for startup environments. With real-world examples from companies lsuch as Spotify, Airbnb, and Stripe, you ll see how data can become a strategic asset from day one. From your first data hire to your next funding round, this book is your manual for building a startup where data drives every decision.
What You Will Learn
Develop a data-driven strategy aligned with startup goals, using rapid, iterative methods to support fast growthSet up and manage data warehouses, build pipelines, and integrate diverse data sources seamlesslyDesign and scale flexible, cost-effective data infrastructure tailored to startup constraintsUse data to drive decision making that directly impacts customer retention and revenueBuild dashboards and reports in BI tools to track the metrics that matter most for customer engagement, retention, and growthMaster data storytelling to influence stakeholders and secure investor fundingDemystify and apply advanced techniques in AI and machine learning to enhance innovation and competitivenessCalculate and monitor essential startup metrics, such as Customer Acquisition Cost (CAC), Lifetime Value (LTV), retention, and growth KPIsCombine quantitative data with qualitative insights to deeply understand user behavior and optimize product experience
Who This Book Is For
Data engineers, analysts, and scientists who are the first data hires at startups or preparing to work in fast-paced, high-growth startup environments. Ideal for professionals looking to build scalable data systems, drive product insights, and grow their careers in early-stage tech companies.
Andere Kunden interessierten sich für
