Data Science for Business: Predictive Modeling, Data Mining, Data Analytics, Data Warehousing, Data Visualization, Regression Analysis, Database Querying, and Machine Learning for Beginners (Paperback
Data science will help you make better decisions, know what products and services to release, and how to provide better service to your customers.
Data Science for Business: Predictive Modeling, Data Mining, Data Analytics, Data Warehousing, Data Visualization, Regression Analysis, Database Querying, and Machine Learning for Beginners (Paperback
Vara #: 253446743

Data Science for Business: Predictive Modeling, Data Mining, Data Analytics, Data Warehousing, Data Visualization, Regression Analysis, Database

Vara #: 253446743

ISK 4548

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What Stands Out

Comprehensive Coverage
Offers thorough insights into key data science concepts, including predictive modeling and machine learning, catering specifically to beginners seeking a solid foundation in the field.
Practical Applications
Focuses on real-world applications of data analytics and visualization, helping readers understand how to leverage data effectively in business decision-making scenarios.
Accessible Language
Written in clear, understandable language, making complex data science topics approachable for novices, ensuring they can grasp and apply the principles without prior expertise.

Upplýsingar um vöru

Shop Data Science for Business: Predictive Modeling, Data Mining, Data Analytics, Data Warehousing, Data Visualization, Regression Analysis, Database Querying, and Machine Learning for Beginners (Paperback online at a best price in Iceland. 760130470
  • Do you want to learn about data science but aren't in the mood to read a boring textbook? Data science has a huge impact on how companies conduct business, and those who don't learn about this revolutionaryfield could be left behind. You see, data science will help you make better decisions, know what products and services to release, and how to provide better service to your customers. And it is all done by collecting and sorting through a large amount of information, so you have the right sources behind you when you make a major decision. In this guidebook, you will discover more about data science and how to get started in this field. This book will discuss the following topics: What is data science?How Big Data works and why it is so importantHow to do an explorative data analysisWorking with data miningHow to mine text to get the dataSome amazing machine learning algorithms to help with data scienceHow to do data modelingData visualizationHow to use data science to help your business growTips to help you get started with data scienceAnd much, much more!So if you are ready to get started with data science, click add to cart!
Book formatPaperback
Fiction/nonfictionNon-Fiction
GenreComputing & Internet
Publication dateSeptember, 2018
Pages102
PublisherAmazon Digital Services LLC - Kdp
Original languagesEnglish
LanguageEnglish
Is collectibleN
Assembled product dimensions (l x w x h)6.00 x 0.21 x 9.00 Inches (15.2 x 0.5 x 22.9 cm)
Assembled product weight0.32 lb (150 grams)
Bisac subject headingComputers

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Herbert Jones All Books Editorial Review

Data Science for Business: Predictive Modeling, Data Mining, Data Analytics, Data Warehousing, Data Visualization, Regression Analysis, Database Querying, and Machine Learning for Beginners is a comprehensive guide that navigates the essential elements of data science, making it an ideal choice for beginners. This non-fiction paperback, published in September 2018, spans 102 pages and covers crucial topics such as data analytics and visualization, empowering readers to comprehend complex concepts in a practical way. Users have found the clear explanation of predictive modeling particularly beneficial, as it simplifies entry into this technical field.

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Kostir

  • Ideal for beginners in data science
  • Comprehensive coverage of key data topics
  • Clear explanations of complex concepts
  • Practical guidance on data visualization
  • Portable size for easy reading

Gallar

  • Could have included more examples for clarity

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