data science hard parts de vaughan daniel (30 resultados)

PAP. Condición: New. New Book. Shipped from UK. Established seller since 2000.

PAP. Condición: New. New Book. Shipped from UK. Established seller since 2000.

Condición: New. In.

Condición: NEW.

Paperback or Softback. Condición: New. Data Science: The Hard Parts: Techniques for Excelling at Data Science 0.91. Book.

Condición: New.

paperback. Condición: Very Good. VG Clean text.

Paperback. Condición: New. This practical guide provides a collection of techniques and best practices that are generally overlooked in most data engineering and data science pedagogy. A common misconception is that great data scientists are experts in the "big themes" of the discipline-machine learning and programming. But most of the time, these tools can only take us so far. In practice, the smaller tools and skills really separate a great data scientist from a not-so-great one.Taken as a whole, the lessons in this book make the difference between an average data scientist candidate and a qualified data scientist working in the field. Author Daniel Vaughan has collected, extended, and used these skills to create value and train data scientists from different companies and industries.With this book, you will:Understand how data science creates valueDeliver compelling narratives to sell your data science projectBuild a business case using unit economics principlesCreate new features for a ML model using storytellingLearn how to decompose KPIsPerform growth decompositions to find root causes for changes in a metricDaniel Vaughan is head of data at Clip, the leading paytech company in Mexico. He's the author of Analytical Skills for AI and Data Science (O'Reilly).

Condición: New. 2023. 1st Edition. paperback. . . . . .

Paperback / softback. Condición: New. New copy - Usually dispatched within 4 working days. 526.

Condición: New.

Paperback. Condición: New. This practical guide provides a collection of techniques and best practices that are generally overlooked in most data engineering and data science pedagogy. A common misconception is that great data scientists are experts in the "big themes" of the discipline-machine learning and programming. But most of the time, these tools can only take us so far. In practice, the smaller tools and skills really separate a great data scientist from a not-so-great one.Taken as a whole, the lessons in this book make the difference between an average data scientist candidate and a qualified data scientist working in the field. Author Daniel Vaughan has collected, extended, and used these skills to create value and train data scientists from different companies and industries.With this book, you will:Understand how data science creates valueDeliver compelling narratives to sell your data science projectBuild a business case using unit economics principlesCreate new features for a ML model using storytellingLearn how to decompose KPIsPerform growth decompositions to find root causes for changes in a metricDaniel Vaughan is head of data at Clip, the leading paytech company in Mexico. He's the author of Analytical Skills for AI and Data Science (O'Reilly).

Paperback. Condición: New. This practical guide provides a collection of techniques and best practices that are generally overlooked in most data engineering and data science pedagogy. A common misconception is that great data scientists are experts in the "big themes" of the discipline-machine learning and programming. But most of the time, these tools can only take us so far. In practice, the smaller tools and skills really separate a great data scientist from a not-so-great one.Taken as a whole, the lessons in this book make the difference between an average data scientist candidate and a qualified data scientist working in the field. Author Daniel Vaughan has collected, extended, and used these skills to create value and train data scientists from different companies and industries.With this book, you will:Understand how data science creates valueDeliver compelling narratives to sell your data science projectBuild a business case using unit economics principlesCreate new features for a ML model using storytellingLearn how to decompose KPIsPerform growth decompositions to find root causes for changes in a metricDaniel Vaughan is head of data at Clip, the leading paytech company in Mexico. He's the author of Analytical Skills for AI and Data Science (O'Reilly).

Condición: New.

Condición: As New. Unread book in perfect condition.

Condición: Good. Item in good condition. Textbooks may not include supplemental items i.e. CDs, access codes etc.

Paperback. Condición: New. This practical guide provides a collection of techniques and best practices that are generally overlooked in most data engineering and data science pedagogy. A common misconception is that great data scientists are experts in the "big themes" of the discipline-machine learning and programming. But most of the time, these tools can only take us so far. In practice, the smaller tools and skills really separate a great data scientist from a not-so-great one.Taken as a whole, the lessons in this book make the difference between an average data scientist candidate and a qualified data scientist working in the field. Author Daniel Vaughan has collected, extended, and used these skills to create value and train data scientists from different companies and industries.With this book, you will:Understand how data science creates valueDeliver compelling narratives to sell your data science projectBuild a business case using unit economics principlesCreate new features for a ML model using storytellingLearn how to decompose KPIsPerform growth decompositions to find root causes for changes in a metricDaniel Vaughan is head of data at Clip, the leading paytech company in Mexico. He's the author of Analytical Skills for AI and Data Science (O'Reilly).

Condición: As New. Unread book in perfect condition.

Condición: New. 2023. 1st Edition. paperback. . . . . . Books ship from the US and Ireland.

Paperback. Condición: Brand New. 250 pages. 9.00x7.00x0.50 inches. In Stock.

Taschenbuch. Condición: Neu. Neuware -This practical guide provides a collection of techniques and best practices that are generally overlooked in most data engineering and data science pedagogy. Taken as a whole, the lessons in this book make the difference between an average data scientist candidate and a qualified data scientist working in the field. 237 pp. Englisch.

Taschenbuch. Condición: Neu. Neuware -This practical guide provides a collection of techniques and best practices that are generally overlooked in most data engineering and data science pedagogy. Taken as a whole, the lessons in this book make the difference between an average data scientist candidate and a qualified data scientist working in the field. 237 pp. Englisch.

Taschenbuch. Condición: Neu. Neuware -This practical guide provides a collection of techniques and best practices that are generally overlooked in most data engineering and data science pedagogy. Taken as a whole, the lessons in this book make the difference between an average data scientist candidate and a qualified data scientist working in the field.

Condición: New. pp. 248.

Taschenbuch. Condición: Neu. Neuware - This practical guide provides a collection of techniques and best practices that are generally overlooked in most data engineering and data science pedagogy. Taken as a whole, the lessons in this book make the difference between an average data scientist candidate and a qualified data scientist working in the field.

Condición: New. pp. 248 1st edition NO-PA16APR2015-KAP.

Taschenbuch. Condición: Neu. Neuware -This practical guide provides a collection of techniques and best practices that are generally overlooked in most data engineering and data science pedagogy. Taken as a whole, the lessons in this book make the difference between an average data scientist candidate and a qualified data scientist working in the field.Libri GmbH, Europaallee 1, 36244 Bad Hersfeld 237 pp. Englisch.

Paperback. Condición: new. Paperback. This practical guide provides a collection of techniques and best practices that are generally overlooked in most data engineering and data science pedagogy. A common misconception is that great data scientists are experts in the "big themes" of the discipline-machine learning and programming. But most of the time, these tools can only take us so far. In practice, the smaller tools and skills really separate a great data scientist from a not-so-great one.Taken as a whole, the lessons in this book make the difference between an average data scientist candidate and a qualified data scientist working in the field. Author Daniel Vaughan has collected, extended, and used these skills to create value and train data scientists from different companies and industries.With this book, you will:Understand how data science creates valueDeliver compelling narratives to sell your data science projectBuild a business case using unit economics principlesCreate new features for a ML model using storytellingLearn how to decompose KPIsPerform growth decompositions to find root causes for changes in a metricDaniel Vaughan is head of data at Clip, the leading paytech company in Mexico. He's the author of Analytical Skills for AI and Data Science (O'Reilly). This practical guide provides a collection of techniques and best practices that are generally overlooked in most data engineering and data science pedagogy. Taken as a whole, the lessons in this book make the difference between an average data scientist candidate and a qualified data scientist working in the field. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.

Paperback. Condición: new. Paperback. This practical guide provides a collection of techniques and best practices that are generally overlooked in most data engineering and data science pedagogy. A common misconception is that great data scientists are experts in the "big themes" of the discipline-machine learning and programming. But most of the time, these tools can only take us so far. In practice, the smaller tools and skills really separate a great data scientist from a not-so-great one.Taken as a whole, the lessons in this book make the difference between an average data scientist candidate and a qualified data scientist working in the field. Author Daniel Vaughan has collected, extended, and used these skills to create value and train data scientists from different companies and industries.With this book, you will:Understand how data science creates valueDeliver compelling narratives to sell your data science projectBuild a business case using unit economics principlesCreate new features for a ML model using storytellingLearn how to decompose KPIsPerform growth decompositions to find root causes for changes in a metricDaniel Vaughan is head of data at Clip, the leading paytech company in Mexico. He's the author of Analytical Skills for AI and Data Science (O'Reilly). This practical guide provides a collection of techniques and best practices that are generally overlooked in most data engineering and data science pedagogy. Taken as a whole, the lessons in this book make the difference between an average data scientist candidate and a qualified data scientist working in the field. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

Paperback. Condición: Brand New. 250 pages. 9.00x7.00x0.50 inches. In Stock. This item is printed on demand.