Lau Sam (32 results)

Hardcover. Condition: New. From visionary writer Sam Sarkar comes a stunning reinvention of the Arthurian legend where the might of the Round Table rides the Old West with guns blazing. In the far reaches of the Old Pacific Northwest lies a world where lawlessness runs rampant and only a mystical six-shooter forged from the metal of the legendary Excalibur can balance the scales.With Caliber in hand and posse at his side, Arthur must fulfill his destiny as the Lawbringer to defeat evil and restore order to his home. Collecting the critically acclaimed miniseries that includes never-before seen character and production renderings, a spotlight gallery featuring additional artwork from celebrated artists Luis Royo (Heavy Metal), Tomm Coker (Daredevil) and Dave Wilkins (Marvel Comics Presents), plus a forward and an in-depth interview with creator Sam Sarkar. Hardcover is bagged and will be carefully / securely packaged then shipped via USPS Priority Mail to insure that it arrives to you perfectly and quickly. Hardcover is Brand New & Unread. Very Fine + condition. Beautiful!!! Please See Scans!. Garrie Gastonny, Stanley Lau (illustrator).…

Paperback. Condition: Very Good. 1. It's a well-cared-for item that has seen limited use. The item may show minor signs of wear. All the text is legible, with all pages included. It may have slight markings and/or highlighting.

Paperback. Condition: Fair. No Jacket. Readable copy. Pages may have considerable notes/highlighting. ~ ThriftBooks: Read More, Spend Less.

Condition: New.

Paperback or Softback. Condition: New. Learning Data Science: Data Wrangling, Exploration, Visualization, and Modeling with Python. Book.

Condition: As New. Unread book in perfect condition.

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

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

Condition: New.

Paperback. Condition: New. As an aspiring data scientist, you appreciate why organizations rely on data for important decisions-whether it's for companies designing websites, cities deciding how to improve services, or scientists discovering how to stop the spread of disease. And you want the skills required to distill a messy pile of data into actionable insights. We call this the data science lifecycle: the process of collecting, wrangling, analyzing, and drawing conclusions from data.Learning Data Science is the first book to cover foundational skills in both programming and statistics that encompass this entire lifecycle. It's aimed at those who wish to become data scientists or who already work with data scientists, and at data analysts who wish to cross the "technical/nontechnical" divide. If you have a basic knowledge of Python programming, you'll learn how to work with data using industry-standard tools like pandas.Refine a question of interest to one that can be studied with dataPursue data collection that may involve text processing, web scraping, etc.Glean valuable insights about data through data cleaning, exploration, and visualizationLearn how to use modeling to describe the dataGeneralize findings beyond the data.…

Condition: New.

Condition: new.

Paperback. Condition: new. Paperback. As an aspiring data scientist, you appreciate why organizations rely on data for important decisionswhether it's for companies designing websites, cities deciding how to improve services, or scientists discovering how to stop the spread of disease. And you want the skills required to distill a messy pile of data into actionable insights. We call this the data science lifecycle: the process of collecting, wrangling, analyzing, and drawing conclusions from data.Learning Data Science is the first book to cover foundational skills in both programming and statistics that encompass this entire lifecycle. It's aimed at those who wish to become data scientists or who already work with data scientists, and at data analysts who wish to cross the "technical/nontechnical" divide. If you have a basic knowledge of Python programming, you'll learn how to work with data using industry-standard tools like pandas.Refine a question of interest to one that can be studied with dataPursue data collection that may involve text processing, web scraping, etc.Glean valuable insights about data through data cleaning, exploration, and visualizationLearn how to use modeling to describe the dataGeneralize findings beyond the dataAbout the AuthorsSam Lau is a PhD candidate at UC San Diego. He designs novel interfaces for learning and teaching data science, and his research has been published in top-tier conferences in human-computer interaction and end-user programming. Sam instructed and helped design flagship data science courses at UC Berkeley. These courses have grown to serve thousands of students every year and their curriculum is used by universities across the world.Joseph (Joey) Gonzalez is an assistant professor in the EECS department at UC Berkeley and a founding member of the new UC Berkeley RISE Lab. His research interests are at the intersection of machine learning and data systems, including: dynamic deep neural networks for transfer learning, accelerated deep learning for high-resolution computer vision, and software platforms for autonomous vehicles.Joey is also co-founder of Turi Inc. (formerly GraphLab), which was based on his work on the GraphLab and PowerGraph Systems. Turi was recently acquired by Apple Inc.Deborah (Deb) Nolan is Professor of Statistics and Associate Dean for Undergraduate Studies in the Division of Computing, Data Science, and Society at the University of California, Berkeley, where she holds the Zaffaroni Family Chair in Undergraduate Education. Her research has involved the empirical process, high-dimensional modeling, and, more recently, technology in education and reproducible research. Her pedagogical approach connects research, practice and education, and she is co-author of 4 textbooks: Stat Labs, Teaching Statistics, Data Science in R, and Communicating with Data. Learning Data Science is the first book to cover foundational skills in both programming and statistics that encompass the entire data science lifecycle: the process of collecting, wrangling, analyzing, and drawing conclusions from data. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

Condition: New. In English.

Paperback. Condition: New. As an aspiring data scientist, you appreciate why organizations rely on data for important decisions-whether it's for companies designing websites, cities deciding how to improve services, or scientists discovering how to stop the spread of disease. And you want the skills required to distill a messy pile of data into actionable insights. We call this the data science lifecycle: the process of collecting, wrangling, analyzing, and drawing conclusions from data.Learning Data Science is the first book to cover foundational skills in both programming and statistics that encompass this entire lifecycle. It's aimed at those who wish to become data scientists or who already work with data scientists, and at data analysts who wish to cross the "technical/nontechnical" divide. If you have a basic knowledge of Python programming, you'll learn how to work with data using industry-standard tools like pandas.Refine a question of interest to one that can be studied with dataPursue data collection that may involve text processing, web scraping, etc.Glean valuable insights about data through data cleaning, exploration, and visualizationLearn how to use modeling to describe the dataGeneralize findings beyond the data.…

Paperback. Condition: New.

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

Condition: New.

Condition: As New. Unread book in perfect condition.

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

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

Condition: New.

Condition: NEW.

Paperback. Condition: Brand New. 550 pages. 9.19x7.00x1.21 inches. In Stock.

Condition: New.

Paperback. Condition: New. As an aspiring data scientist, you appreciate why organizations rely on data for important decisions-whether it's for companies designing websites, cities deciding how to improve services, or scientists discovering how to stop the spread of disease. And you want the skills required to distill a messy pile of data into actionable insights. We call this the data science lifecycle: the process of collecting, wrangling, analyzing, and drawing conclusions from data.Learning Data Science is the first book to cover foundational skills in both programming and statistics that encompass this entire lifecycle. It's aimed at those who wish to become data scientists or who already work with data scientists, and at data analysts who wish to cross the "technical/nontechnical" divide. If you have a basic knowledge of Python programming, you'll learn how to work with data using industry-standard tools like pandas.Refine a question of interest to one that can be studied with dataPursue data collection that may involve text processing, web scraping, etc.Glean valuable insights about data through data cleaning, exploration, and visualizationLearn how to use modeling to describe the dataGeneralize findings beyond the data.…

Condition: New. &Uumlber den AutorSam Lau is a PhD candidate at UC San Diego. He designs novel interfaces for learning and teaching data science, and his research has been published in top-tier conferences in human-computer interaction and end-user pro.

Paperback. Condition: new. Paperback. As an aspiring data scientist, you appreciate why organizations rely on data for important decisionswhether it's for companies designing websites, cities deciding how to improve services, or scientists discovering how to stop the spread of disease. And you want the skills required to distill a messy pile of data into actionable insights. We call this the data science lifecycle: the process of collecting, wrangling, analyzing, and drawing conclusions from data.Learning Data Science is the first book to cover foundational skills in both programming and statistics that encompass this entire lifecycle. It's aimed at those who wish to become data scientists or who already work with data scientists, and at data analysts who wish to cross the "technical/nontechnical" divide. If you have a basic knowledge of Python programming, you'll learn how to work with data using industry-standard tools like pandas.Refine a question of interest to one that can be studied with dataPursue data collection that may involve text processing, web scraping, etc.Glean valuable insights about data through data cleaning, exploration, and visualizationLearn how to use modeling to describe the dataGeneralize findings beyond the dataAbout the AuthorsSam Lau is a PhD candidate at UC San Diego. He designs novel interfaces for learning and teaching data science, and his research has been published in top-tier conferences in human-computer interaction and end-user programming. Sam instructed and helped design flagship data science courses at UC Berkeley. These courses have grown to serve thousands of students every year and their curriculum is used by universities across the world.Joseph (Joey) Gonzalez is an assistant professor in the EECS department at UC Berkeley and a founding member of the new UC Berkeley RISE Lab. His research interests are at the intersection of machine learning and data systems, including: dynamic deep neural networks for transfer learning, accelerated deep learning for high-resolution computer vision, and software platforms for autonomous vehicles.Joey is also co-founder of Turi Inc. (formerly GraphLab), which was based on his work on the GraphLab and PowerGraph Systems. Turi was recently acquired by Apple Inc.Deborah (Deb) Nolan is Professor of Statistics and Associate Dean for Undergraduate Studies in the Division of Computing, Data Science, and Society at the University of California, Berkeley, where she holds the Zaffaroni Family Chair in Undergraduate Education. Her research has involved the empirical process, high-dimensional modeling, and, more recently, technology in education and reproducible research. Her pedagogical approach connects research, practice and education, and she is co-author of 4 textbooks: Stat Labs, Teaching Statistics, Data Science in R, and Communicating with Data. Learning Data Science is the first book to cover foundational skills in both programming and statistics that encompass the entire data science lifecycle: the process of collecting, wrangling, analyzing, and drawing conclusions from data. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.…

Paperback. Condition: New. As an aspiring data scientist, you appreciate why organizations rely on data for important decisions-whether it's for companies designing websites, cities deciding how to improve services, or scientists discovering how to stop the spread of disease. And you want the skills required to distill a messy pile of data into actionable insights. We call this the data science lifecycle: the process of collecting, wrangling, analyzing, and drawing conclusions from data.Learning Data Science is the first book to cover foundational skills in both programming and statistics that encompass this entire lifecycle. It's aimed at those who wish to become data scientists or who already work with data scientists, and at data analysts who wish to cross the "technical/nontechnical" divide. If you have a basic knowledge of Python programming, you'll learn how to work with data using industry-standard tools like pandas.Refine a question of interest to one that can be studied with dataPursue data collection that may involve text processing, web scraping, etc.Glean valuable insights about data through data cleaning, exploration, and visualizationLearn how to use modeling to describe the dataGeneralize findings beyond the data.…

Taschenbuch. Condition: Neu. Neuware - As an aspiring data scientist, you appreciate why organizations rely on data for important decisions--whether it's for companies designing websites, cities deciding how to improve services, or scientists discovering how to stop the spread of disease. And you want the skills required to distill a messy pile of data into actionable insights. We call this the data science lifecycle: the process of collecting, wrangling, analyzing, and drawing conclusions from data. Learning Data Science is the first book to cover foundational skills in both programming and statistics that encompass this entire lifecycle. It's aimed at those who wish to become data scientists or who already work with data scientists, and at data analysts who wish to cross the 'technical/nontechnical' divide. If you have a basic knowledge of Python programming, you'll learn how to work with data using industry-standard tools like pandas. - Refine a question of interest to one that can be studied with data - Pursue data collection that may involve text processing, web scraping, etc. - Glean valuable insights about data through data cleaning, exploration, and visualization - Learn how to use modeling to describe the data - Generalize findings beyond the data.…