Data Science
- Page ID
- 118727
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\(\newcommand{\avec}{\mathbf a}\) \(\newcommand{\bvec}{\mathbf b}\) \(\newcommand{\cvec}{\mathbf c}\) \(\newcommand{\dvec}{\mathbf d}\) \(\newcommand{\dtil}{\widetilde{\mathbf d}}\) \(\newcommand{\evec}{\mathbf e}\) \(\newcommand{\fvec}{\mathbf f}\) \(\newcommand{\nvec}{\mathbf n}\) \(\newcommand{\pvec}{\mathbf p}\) \(\newcommand{\qvec}{\mathbf q}\) \(\newcommand{\svec}{\mathbf s}\) \(\newcommand{\tvec}{\mathbf t}\) \(\newcommand{\uvec}{\mathbf u}\) \(\newcommand{\vvec}{\mathbf v}\) \(\newcommand{\wvec}{\mathbf w}\) \(\newcommand{\xvec}{\mathbf x}\) \(\newcommand{\yvec}{\mathbf y}\) \(\newcommand{\zvec}{\mathbf z}\) \(\newcommand{\rvec}{\mathbf r}\) \(\newcommand{\mvec}{\mathbf m}\) \(\newcommand{\zerovec}{\mathbf 0}\) \(\newcommand{\onevec}{\mathbf 1}\) \(\newcommand{\real}{\mathbb R}\) \(\newcommand{\twovec}[2]{\left[\begin{array}{r}#1 \\ #2 \end{array}\right]}\) \(\newcommand{\ctwovec}[2]{\left[\begin{array}{c}#1 \\ #2 \end{array}\right]}\) \(\newcommand{\threevec}[3]{\left[\begin{array}{r}#1 \\ #2 \\ #3 \end{array}\right]}\) \(\newcommand{\cthreevec}[3]{\left[\begin{array}{c}#1 \\ #2 \\ #3 \end{array}\right]}\) \(\newcommand{\fourvec}[4]{\left[\begin{array}{r}#1 \\ #2 \\ #3 \\ #4 \end{array}\right]}\) \(\newcommand{\cfourvec}[4]{\left[\begin{array}{c}#1 \\ #2 \\ #3 \\ #4 \end{array}\right]}\) \(\newcommand{\fivevec}[5]{\left[\begin{array}{r}#1 \\ #2 \\ #3 \\ #4 \\ #5 \\ \end{array}\right]}\) \(\newcommand{\cfivevec}[5]{\left[\begin{array}{c}#1 \\ #2 \\ #3 \\ #4 \\ #5 \\ \end{array}\right]}\) \(\newcommand{\mattwo}[4]{\left[\begin{array}{rr}#1 \amp #2 \\ #3 \amp #4 \\ \end{array}\right]}\) \(\newcommand{\laspan}[1]{\text{Span}\{#1\}}\) \(\newcommand{\bcal}{\cal B}\) \(\newcommand{\ccal}{\cal C}\) \(\newcommand{\scal}{\cal S}\) \(\newcommand{\wcal}{\cal W}\) \(\newcommand{\ecal}{\cal E}\) \(\newcommand{\coords}[2]{\left\{#1\right\}_{#2}}\) \(\newcommand{\gray}[1]{\color{gray}{#1}}\) \(\newcommand{\lgray}[1]{\color{lightgray}{#1}}\) \(\newcommand{\rank}{\operatorname{rank}}\) \(\newcommand{\row}{\text{Row}}\) \(\newcommand{\col}{\text{Col}}\) \(\renewcommand{\row}{\text{Row}}\) \(\newcommand{\nul}{\text{Nul}}\) \(\newcommand{\var}{\text{Var}}\) \(\newcommand{\corr}{\text{corr}}\) \(\newcommand{\len}[1]{\left|#1\right|}\) \(\newcommand{\bbar}{\overline{\bvec}}\) \(\newcommand{\bhat}{\widehat{\bvec}}\) \(\newcommand{\bperp}{\bvec^\perp}\) \(\newcommand{\xhat}{\widehat{\xvec}}\) \(\newcommand{\vhat}{\widehat{\vvec}}\) \(\newcommand{\uhat}{\widehat{\uvec}}\) \(\newcommand{\what}{\widehat{\wvec}}\) \(\newcommand{\Sighat}{\widehat{\Sigma}}\) \(\newcommand{\lt}{<}\) \(\newcommand{\gt}{>}\) \(\newcommand{\amp}{&}\) \(\definecolor{fillinmathshade}{gray}{0.9}\)Data science is an interdisciplinary field that uses scientific methods, algorithms, and systems to extract insights and knowledge from structured and unstructured data. It combines elements of statistics, computer science, and domain expertise to analyze large volumes of data, build predictive models, and inform decision-making. Key components include data collection, cleaning, exploration, visualization, and modeling using tools like Python, R, and machine learning frameworks. Data scientists often work with big data technologies and collaborate across disciplines to solve complex problems in industries ranging from healthcare and finance to marketing and technology.
- Principles of Data Science (OpenStax)
- Principles of Data Science is intended to support one- or two-semester courses in data science. It is appropriate for data science majors and minors as well as students concentrating in business, finance, health care, engineering, the sciences, and a number of other fields where data science has become critically important.
- Front Matter
- 1: What Are Data and Data Science?
- 2: Collecting and Preparing Data
- 3: Descriptive Statistics- Statistical Measurements and Probability Distributions
- 4: Inferential Statistics and Regression Analysis
- 5: Time Series and Forecasting
- 6: Decision-Making Using Machine Learning Basics
- 7: Deep Learning and AI Basics
- 8: Ethics Throughout the Data Science Cycle
- 9: Visualizing Data
- 10: Reporting Results
- 11: Appendix
- 12: Answer Key
- Back Matter
- The Crystal Ball - Instruction Manual I: Introduction to Data Science (Davies)
- A perfect introduction to the exploding field of Data Science for the curious, first-time student. The author brings his trademark conversational tone to the important pillars of the discipline: exploratory data analysis, choices for structuring data, causality, machine learning principles, and introductory Python programming using open-source Jupyter Notebooks.
- Front Matter
- 1: Introduction
- 2: A trip to Jupyter
- 3: Three Kinds of Atomic Data
- 4: Memory pictures
- 5: Calculations
- 6: Scales of Measure
- 7: Three Kinds of Aggregate Data
- 8: Arrays in Python (1 of 2)
- 9: Arrays in Python (2 of 2)
- 10: Interpreting Data
- 11: Associative Arrays in Python (1 of 3)
- 12: Associative Arrays in Python (2 of 3)
- 13: Associative Arrays in Python (3 of 3)
- 14: Loops
- 15: Exploratory Data Analysis- univariate
- 16: Tables in Python (1 of 3)
- 17: Tables in Python (2 of 3)
- 18: Tables in Python (3 of 3)
- 19: Exploratory Data Analysis: bivariate (1 of 2)
- 20: Exploratory Data Analysis: Bivariate (2 of 2)
- 21: Branching
- 22: Functions (1 of 2)
- 23: Functions (2 of 2)
- 24: Recoding and Transforming
- 25: Machine Learning - Concepts
- 26: Classification - Concepts
- 27: Decision Trees (1 of 2)
- 28: Decision Trees (2 of 2)
- 29: Evaluating a Classifier
- Back Matter
Thumbnail: Algo-r-(h)-i-(y)-thms, 2018. Installation view at ON AIR, Tomás Saraceno's solo exhibition at Palais de Tokyo, Paris, 2018. (Unsplash License; Alina Grubnyak via Unsplash)