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Data Science for Mathematicians

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€132.99
advanced machine learning techniques
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Agent Based Modeling
automatic-update
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B01=Nathan Carter
Basic statistics
Big Data
Boston Housing Data
Boston Housing Dataset
Category1=Non-Fiction
Category=PBKA
clustering algorithms
Clustering Solution
Column Stochastic Matrix
Computer science
Convolutional Layer
COP=United Kingdom
curriculum development in data science
Data Analysis
Data Collection
Data science
Deep Learning Framework
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dimensionality reduction
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eq_nobargain
Fractal Dimension
Gradient Descent
Interior Point Methods
Klein Bottle
Language_English
Linear algebra
Linearly Independent
LU Decomposition
Mathematicians
Metric Space
Multilayer Perceptron
operations research methods
PA=Available
Persistent Homology
Price_€100 and above
Primal Dual Interior Point Algorithm
PS=Active
QR Decomposition
Roc Curve
scientific data modeling
Simple Recurrent Neural Network
softlaunch
Stochastic Gradient Descent
TDA
topological data analysis
Version Control

Product details

  • ISBN 9780367528492
  • Weight: 840g
  • Dimensions: 156 x 234mm
  • Publication Date: 26 Aug 2024
  • Publisher: Taylor & Francis Ltd
  • Publication City/Country: GB
  • Product Form: Paperback
  • Language: English
Delivery/Collection within 10-20 working days

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Description

Mathematicians have skills that, if deepened in the right ways, would enable them to use data to answer questions important to them and others, and report those answers in compelling ways. Data science combines parts of mathematics, statistics, computer science. Gaining such power and the ability to teach has reinvigorated the careers of mathematicians. This handbook will assist mathematicians to better understand the opportunities presented by data science. As it applies to the curriculum, research, and career opportunities, data science is a fast-growing field. Contributors from both academics and industry present their views on these opportunities and how to advantage them.

About
Nathan Carter is a professor at Bentley University.