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portada Learn Data Analytics For Beginners: Data Analyst, Deep Learning, Neural Network, Python Data Analytics
Type
Physical Book
Language
English
Pages
136
Format
Paperback
Dimensions
22.9 x 15.2 x 0.8 cm
Weight
0.21 kg.
ISBN13
9781089671534

Learn Data Analytics For Beginners: Data Analyst, Deep Learning, Neural Network, Python Data Analytics

Landon Adrian (Author) · Independently Published · Paperback

Learn Data Analytics For Beginners: Data Analyst, Deep Learning, Neural Network, Python Data Analytics - Kumar, Sathish ; Adrian, Landon

Physical Book

£ 10.84

  • Condition: New
Origin: U.S.A. (Import costs included in the price)
It will be shipped from our warehouse between Monday, June 17 and Wednesday, July 03.
You will receive it anywhere in United Kingdom between 1 and 3 business days after shipment.

Synopsis "Learn Data Analytics For Beginners: Data Analyst, Deep Learning, Neural Network, Python Data Analytics"

Data science has taken the world by storm. Every field of study and area of business has been affected as people increasingly realize the value of the incredible quantities of data being generated. But to extract value from those data, one needs to be trained in the proper data science skills. The R programming language has become the de facto programming language for data science. Its flexibility, power, sophistication, and expressiveness have made it an invaluable tool for data scientists around the world. This book is about the fundamentals of R programming.Finally, you'll learn some down-to-earth practical skills that will help you overcome the common challenges that frequently derail data science projects Reproducibility is the idea that data analyses should be published or made available with their data and software code so that others may verify the findings and build upon them. The need for reproducible report writing is increasing dramatically as data analyses become more complex, involving larger datasets and more sophisticated computations. Reproducibility allows for people to focus on the actual content of a data analysis, rather than on superficial details reported in a written summary. In addition, reproducibility makes an analysis more useful to others because the data and code that actually conducted the analysis are available.

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