000			04956nam a22002417a 4500
999			_c5005 _d5005
005			20230322102109.0
008			230322b ||||| |||| 00| 0 eng d
020			_a9789354640506
082			_a006.312 _bMAH
100			_aMaheswari, B. Uma _911456
245			_aIntroduction to data science: _bpractical approach with R and Python
260			_bWiley India Pvt. Ltd. _aNew Delhi _c2023
300			_axxvi, 557 p.
365			_aINR _b729.00
504			_aTable of content Chapter 1 Introduction to Data Science 1.1 Data Science 1.2 Brief History of Data Science 1.3 Increasing Attention to Data Science 1.4 Fundamental Fields of Study Related to Data Science 1.5 Data Science and Related Terminologies 1.6 Types of Analytics 1.7 Applications of Data Science 1.8 Data Science Process Model Chapter 2 Introduction to R and Python 2.1 Introduction 2.2 R and RStudio Environment 2.3 Basics of R 2.4 Python Language and Python Environment 2.5 Basics of Python Chapter 3 Exploratory Data Analysis 3.1 Introduction 3.2 Steps in Data Preprocessing 3.3 Understanding Data 3.3.1 Steps Involved in EDA Using R Programming 3.4 Looking at the Data 3.5 Visualizing Data 3.6 Dealing with Outliers 3.7 Dealing with Missing Values 3.8 Standardizing Data 3.9 Steps Involved in EDA Using Python Programming 3.10 Looking at the Data 3.11 Visualization the Data 3.12 Treatment of Outliers Chapter 4 Data Visualization 4.1 Introduction 4.2 Data Visualization for Machine Learning 4.3 Data Visualization Techniques 4.4 Simple Data Visualization Using R 4.5 Data Visualization Using Ggplots in R 4.6 Data Visualization Using Python 4.7 Matplotlib Library 4.8 Seaborn Library Chapter 5 Dimensionality Reduction Techniques 5.1 Dimensionality Reduction 5.2 Independent and Dependent Variables 5.3 Relationship between Variables: Correlation 5.5 Factor Analysis 5.5.4 Rotated Factor Matrix 5.6 Application of Factor Analysis Using Python Programming Chapter 6 Types of Machine Learning Algorithms 6.1 Introduction 6.2 Supervised and Unsupervised Learning Algorithms 6.3 Supervised Learning Algorithm 6.4 Unsupervised Learning Algorithm Chapter 7 Unsupervised Learning Algorithms 7.1 Introduction 7.2 Association Rule Mining 7.3 Conjoint Analysis 7.4 Clustering 7.5 K Means Clustering Chapter 8 Text Analytics 8.1 Introduction 8.2 Unstructured Data 8.3 Word Cloud 8.4 Sentiment Analysis 8.5 Web and Social Media Analytics Chapter 9 Supervised Learning Algorithms: Linear and Logistic Regression 9.1 Introduction 9.2 Simple Linear Regression 9.3 Multiple Linear Regression 9.4 Logistic Regression Chapter 10 Supervised Learning Algorithms: Decision Tree and Random Forest 10.1 Decision Tree 10.2 Classification and Regression Technique 10.3 Random Forest Chapter 11 Supervised Learning Algorithm: KNN, Naïve Bayes, and Linear Discriminant Analysis 11.1 K-Nearest Neighbors 11.2 Naïve Bayes Algorithm 11.3 Linear Discriminant Analysis Chapter 12 Support Vector Machines and Artificial Neural Networks 12.1 Support Vector Machines 12.2 Artificial Neural Networks Chapter 13 Time Series Forecasting 13.1 Introduction 13.2 Time Series Data 13.3 Visualizing the Time Series Data 13.4 Components of Time Series Data 13.5 Stationarity of the Data 13.6 Exponential Smoothening Model 13.7 Holt–Winters Model 13.8 ARIMA Model Chapter 14 Ensemble Methods 14.1 Introduction 14.2 Dealing with Imbalanced Data 14.3 Ensemble Methods 14.4 Bias Variance Tradeoff 14.5 Bagging 14.6 Boosting 14.7 Synthetic Minority over Sampling Technique (SMOTE) Chapter 15 Artificial Intelligence 15.1 Introduction 15.2 Artificial Intelligence 15.3 Deep Learning 15.4 Convolutional Neural Networks 15.5 Reinforcement Learning Chapter 16 Applications of Analytics 16.1 Introduction 16.2 Application of Analytics in Healthcare 16.3 Application of Analytics in Agriculture 16.4 Application of Analytics in Business 16.5 Application of Analytics in Sports 16.6 Application of Analytics in Governance
520			_aIntroduction to Data Science: Practical Approach with R and Python covers all the fundamental concepts of Data Science in a concise manner. It offers a mix of insights and golden rules which would be needed in analyzing data. This book serves as a practical guide for Science/Engineering/MBA students – both at the undergraduate and postgraduate level interested in Data Science domain.
650			_aMachine learning _92343
650			_aPython (Computer program language) _912414
650			_aBig data _9212
650			_aData mining _9365
700			_aSujatha, R. _912415
942			_2ddc _cBK