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Author: MEENACHISUNDARAM.M Publisher: MEENACHI SUNDARAM ISBN: Category : Computers Languages : en Pages : 242
Book Description
PYTHON WITH DATA SCIENCE By M.Meenachi Sundaram TABLE OF CONTENTS PYTHON WITH DATA SCIENCE.. 13 CHAPTER 1: STATISTICS CONCEPTS.. 13 1. Population and sample. 13 2. Normal distribution. 14 3. Measures of central tendency. 15 4. Variance and standard deviation. 16 5. Covariance and correlation. 16 6. Central limit theorem.. 18 7. P-value. 19 8. Expected value of random variables. 21 9. Conditional probability. 23 10. Bayes’ theorem.. 24 IMPORTANT 5 STATISTICAL CONCEPTS FOR EVERY DATA SCIENTIST.. 25 1. Descriptive statistics. 25 2. Probability distributions. 28 3. Dimensionality reduction. 31 4. Under-sampling and Over-sampling. 32 5. Bayesian statistics. 33 PYTHON STATISTICS MODULE.. 34 Statistics Methods. 34 CHAPTER 2: PROBABILITY.. 35 Python, Random Numbers and Probability. 35 Random Numbers with Python. 36 Random Numbers Satisfying sum-to-one Condition. 40 Generating Random Strings or Passwords with Python. 41 Random Integer Numbers. 42 Random Choices with Python. 45 Random Samples with Python. 48 True Random Numbers. 50 Weighted Random Choices. 52 CHAPTER 3: STANDARD DEVIATION.. 58 Python statistics.stdev() Method. 58 Definition and Usage. 58 Syntax. 59 Parameter Values. 59 Parameter 59 Description. 59 Data. 59 Required. The data values to be used (can be any sequence, list or iterator) 59 Xbar. 59 Optional. The mean of the given data. If omitted (or set to None), the mean is automatically calculated 59 Technical Details. 59 Return Value: 59 CHAPTER 4: BIAS AND VARIANCE.. 60 What are Bias and Variance?. 60 Bias and Variance using Python. 60 CHAPTER 5: DISTANCE METRICS.. 63 Understanding Distance Metrics Used in Machine Learning. 63 We will study: 63 What Are Distance Metrics?. 63 Types of Distance Metrics in Machine Learning. 64 Euclidean Distance. 65 Formula for Euclidean Distance. 66 Manhattan Distance. 68 Formula for Manhattan Distance. 68 Minkowski Distance. 70 Formula for Minkowski Distance. 70 Hamming Distance. 72 Conclusion. 74 Points. 74 CHAPTER 6: OUTLIER ANALYSIS.. 76 Outlier detection is the process of identifying data points that have extreme values compared to the rest of the distribution. Learn three methods of outlier detection in Python. 76 What Is Outlier Detection?. 76 Benefits of Outlier Detection. 76 Methods for Outlier Detection in Python. 77 Prerequisite to Outlier Detection: Reading in Data. 77 Using Box Plots for Outlier Detection. 78 Using Isolation Forests for Outlier Detection. 81 Using OneClassSVM for Outlier Detection. 83 Mastering Outlier Detection. 84 Outlier 85 What are Outliers?. 86 When are outliers dangerous?. 87 Which statistics are affected by the outliers?. 90 When to drop or keep outliers?. 91 Table of Contents. 94 How to Treat Outliers?. 94 Trimming. 94 Capping. 94 Discretization. 94 How to Detect Outliers?. 96 For Normal Distributions. 96 For Skewed Distributions. 96 For Other Distributions. 96 How to Detect and Remove Outliners in Python. 97 Z-score Treatment 97 IQR Based Filtering. 99 Percentile Method. 102 Conclusion. 105 Frequently Asked Questions. 106 CHAPTER 7: MISSNG VALUE TREATMENTS.. 107 How to Handle Missing Data. 107 Why Fill in the Missing Data?. 107 How to Know If the Data Has Missing Values?. 109 Different Methods of Dealing with Missing Data. 111 1. Deleting the column with missing data. 111 2. Deleting the row with missing data. 112 3. Filling the Missing Values – Imputation. 114 4. Other imputation methods. 116 5. Imputation with an additional column. 116 6. Filling with a Regression Model 119 Conclusion. 122 Frequently Asked Questions. 122 Pandas – Replace NaN Values with Zero in a Column. 123 1. Example of Replace NaN with Zero. 123 2. Replace NaN Values with Zero on pandas DataFrame. 124 3. Replace NaN Values with Zero on a Single or Multiple Columns. 125 4. Replace NaN Values with Zeroes Using replace() 126 5. Using DataFrame.replace() on All Columns. 126 6. Complete Example For Replace NaN Values with Zeroes in a Column. 128 CHAPTER 8: CORRELATION.. 130 NumPy, SciPy, and pandas: Correlation With Python. 130 Correlation. 130 Example: NumPy Correlation Calculation. 133 Example: SciPy Correlation Calculation. 135 Example: pandas Correlation Calculation. 136 This page and Next page just for reference. 139 Linear Correlation. 139 Pearson Correlation Coefficient 139 Linear Regression: SciPy Implementation. 142 Pearson Correlation: NumPy and SciPy Implementation. 145 Pearson Correlation: pandas Implementation. 147 Rank Correlation. 152 Rank: SciPy Implementation. 153 Rank Correlation: NumPy and SciPy Implementation. 155 Rank Correlation: pandas Implementation. 157 Visualization of Correlation. 158 X-Y Plots with a Regression Line. 159 Heatmaps of Correlation Matrices. 161 Conclusion. 162 CHAPTER 9: ERROR METRICS (ERROR MEASURES) 164 Mean Squared Error 164 Mean Absolute Error 164 Mean Absolute Percent Error 164 Measuring Regression Errors with Python. 165 Measuring Regression Errors. 165 Six Error Metrics for Measuring Regression Errors. 166 Mean Absolute Error (MAE) 167 Mean Absolute Percentage Error (MAPE) 168 Mean Squared Error (MSE) 168 Median Absolute Error (MedAE) 169 Root Mean Squared Error (RMSE) 170 Median Absolute Percentage Error (MdAPE) 170 Implementing Regression Error Metrics in Python: Time Series Prediction. 171 Step #1 Generate Synthetic Time Series Data. 171 Step #2 Preparing the Data. 172 Step #3 Training a Time Series Regression Model 174 Step #4 Making Test Predictions. 174 Step #5 Calculating the Regression Error Metrics: Implementation and Evaluation. 175 CHAPTER 10: REGRESSION.. 177 Linear Regression. 178 Logistic Regression. 178 Polynomial Regression. 179 Ridge Regression. 179 Lasso Regression. 179 Regression Applications. 180 Difference between Regression and Classification in data mining. 180 Regression. 181 CHAPTER 11: MACHINE LEARNING.. 183 Machine Learning vs. Deep Learning vs. Neural Networks. 183 Machine learning methods. 184 Supervised machine learning. 184 Unsupervised machine learning. 185 Semi-supervised learning. 185 Common machine learning algorithms. 186 Real-world machine learning use cases. 187 Data Structure for Machine Learning. 188 What is Data Structure?. 190 Types of Data Structure. 190 1. Linear Data structure: 191 2. Non-linear Data Structures. 193 Dynamic array data structure: 196 How is Data Structure used in Machine Learning?. 197 Conclusion. 197 SUPERVISED LEARNING.. 198 Supervised Machine Learning. 198 How Supervised Learning Works?. 198 Steps Involved in Supervised Learning: 200 Types of supervised Machine learning Algorithms: 200 1. Regression. 201 2. Classification. 201 Advantages of Supervised learning: 202 Disadvantages of supervised learning: 202 Linear Regression. 202 How does it Work?. 204 R for Relationship. 208 Predict Future Values. 210 Bad Fit?. 211 Logistic Regression. 215 How does it work?. 215 Probability. 217 Function Explained. 217 Results Explained. 219 How to Save a Machine Learning Model 219 Two Ways to Save a Model from scikit-learn: 219 UNSUPERVISED LEARNING.. 225 Unsupervised Machine Learning. 225 Why use Unsupervised Learning?. 227 Working of Unsupervised Learning. 227 Types of Unsupervised Learning Algorithm: 228 Unsupervised Learning algorithms: 229 Advantages of Unsupervised Learning. 229 Disadvantages of Unsupervised Learning. 229 Supervised vs. Unsupervised Learning. 230 Preparing Data for Unsupervised Learning. 231 Clustering. 232 Hierarchical Clustering. 234 Difference between K-Means and Hierarchical clustering. 236 t-SNE Clustering. 237 DBSCAN Clustering. 238 OTHER MACHINE LEARNING (ML) ALGORITHMS.. 240 ABOUT THE AUTHOR.. 241 PYTHON WITH DATA SCIENCE CHAPTER 1: STATISTICS CONCEPTS Data science is an interdisciplinary field. One of the building blocks of data science is statistics. Without a decent level of statistics knowledge, it would be highly difficult to understand or interpret the data. Statistics helps us explain the data. Statistics is used to infer results about a population based on a sample drawn from that population. Furthermore, machine learning and statistics have plenty of overlaps. Statistics concepts helps us to become a data scientist.
Author: MEENACHISUNDARAM.M Publisher: MEENACHI SUNDARAM ISBN: Category : Computers Languages : en Pages : 242
Book Description
PYTHON WITH DATA SCIENCE By M.Meenachi Sundaram TABLE OF CONTENTS PYTHON WITH DATA SCIENCE.. 13 CHAPTER 1: STATISTICS CONCEPTS.. 13 1. Population and sample. 13 2. Normal distribution. 14 3. Measures of central tendency. 15 4. Variance and standard deviation. 16 5. Covariance and correlation. 16 6. Central limit theorem.. 18 7. P-value. 19 8. Expected value of random variables. 21 9. Conditional probability. 23 10. Bayes’ theorem.. 24 IMPORTANT 5 STATISTICAL CONCEPTS FOR EVERY DATA SCIENTIST.. 25 1. Descriptive statistics. 25 2. Probability distributions. 28 3. Dimensionality reduction. 31 4. Under-sampling and Over-sampling. 32 5. Bayesian statistics. 33 PYTHON STATISTICS MODULE.. 34 Statistics Methods. 34 CHAPTER 2: PROBABILITY.. 35 Python, Random Numbers and Probability. 35 Random Numbers with Python. 36 Random Numbers Satisfying sum-to-one Condition. 40 Generating Random Strings or Passwords with Python. 41 Random Integer Numbers. 42 Random Choices with Python. 45 Random Samples with Python. 48 True Random Numbers. 50 Weighted Random Choices. 52 CHAPTER 3: STANDARD DEVIATION.. 58 Python statistics.stdev() Method. 58 Definition and Usage. 58 Syntax. 59 Parameter Values. 59 Parameter 59 Description. 59 Data. 59 Required. The data values to be used (can be any sequence, list or iterator) 59 Xbar. 59 Optional. The mean of the given data. If omitted (or set to None), the mean is automatically calculated 59 Technical Details. 59 Return Value: 59 CHAPTER 4: BIAS AND VARIANCE.. 60 What are Bias and Variance?. 60 Bias and Variance using Python. 60 CHAPTER 5: DISTANCE METRICS.. 63 Understanding Distance Metrics Used in Machine Learning. 63 We will study: 63 What Are Distance Metrics?. 63 Types of Distance Metrics in Machine Learning. 64 Euclidean Distance. 65 Formula for Euclidean Distance. 66 Manhattan Distance. 68 Formula for Manhattan Distance. 68 Minkowski Distance. 70 Formula for Minkowski Distance. 70 Hamming Distance. 72 Conclusion. 74 Points. 74 CHAPTER 6: OUTLIER ANALYSIS.. 76 Outlier detection is the process of identifying data points that have extreme values compared to the rest of the distribution. Learn three methods of outlier detection in Python. 76 What Is Outlier Detection?. 76 Benefits of Outlier Detection. 76 Methods for Outlier Detection in Python. 77 Prerequisite to Outlier Detection: Reading in Data. 77 Using Box Plots for Outlier Detection. 78 Using Isolation Forests for Outlier Detection. 81 Using OneClassSVM for Outlier Detection. 83 Mastering Outlier Detection. 84 Outlier 85 What are Outliers?. 86 When are outliers dangerous?. 87 Which statistics are affected by the outliers?. 90 When to drop or keep outliers?. 91 Table of Contents. 94 How to Treat Outliers?. 94 Trimming. 94 Capping. 94 Discretization. 94 How to Detect Outliers?. 96 For Normal Distributions. 96 For Skewed Distributions. 96 For Other Distributions. 96 How to Detect and Remove Outliners in Python. 97 Z-score Treatment 97 IQR Based Filtering. 99 Percentile Method. 102 Conclusion. 105 Frequently Asked Questions. 106 CHAPTER 7: MISSNG VALUE TREATMENTS.. 107 How to Handle Missing Data. 107 Why Fill in the Missing Data?. 107 How to Know If the Data Has Missing Values?. 109 Different Methods of Dealing with Missing Data. 111 1. Deleting the column with missing data. 111 2. Deleting the row with missing data. 112 3. Filling the Missing Values – Imputation. 114 4. Other imputation methods. 116 5. Imputation with an additional column. 116 6. Filling with a Regression Model 119 Conclusion. 122 Frequently Asked Questions. 122 Pandas – Replace NaN Values with Zero in a Column. 123 1. Example of Replace NaN with Zero. 123 2. Replace NaN Values with Zero on pandas DataFrame. 124 3. Replace NaN Values with Zero on a Single or Multiple Columns. 125 4. Replace NaN Values with Zeroes Using replace() 126 5. Using DataFrame.replace() on All Columns. 126 6. Complete Example For Replace NaN Values with Zeroes in a Column. 128 CHAPTER 8: CORRELATION.. 130 NumPy, SciPy, and pandas: Correlation With Python. 130 Correlation. 130 Example: NumPy Correlation Calculation. 133 Example: SciPy Correlation Calculation. 135 Example: pandas Correlation Calculation. 136 This page and Next page just for reference. 139 Linear Correlation. 139 Pearson Correlation Coefficient 139 Linear Regression: SciPy Implementation. 142 Pearson Correlation: NumPy and SciPy Implementation. 145 Pearson Correlation: pandas Implementation. 147 Rank Correlation. 152 Rank: SciPy Implementation. 153 Rank Correlation: NumPy and SciPy Implementation. 155 Rank Correlation: pandas Implementation. 157 Visualization of Correlation. 158 X-Y Plots with a Regression Line. 159 Heatmaps of Correlation Matrices. 161 Conclusion. 162 CHAPTER 9: ERROR METRICS (ERROR MEASURES) 164 Mean Squared Error 164 Mean Absolute Error 164 Mean Absolute Percent Error 164 Measuring Regression Errors with Python. 165 Measuring Regression Errors. 165 Six Error Metrics for Measuring Regression Errors. 166 Mean Absolute Error (MAE) 167 Mean Absolute Percentage Error (MAPE) 168 Mean Squared Error (MSE) 168 Median Absolute Error (MedAE) 169 Root Mean Squared Error (RMSE) 170 Median Absolute Percentage Error (MdAPE) 170 Implementing Regression Error Metrics in Python: Time Series Prediction. 171 Step #1 Generate Synthetic Time Series Data. 171 Step #2 Preparing the Data. 172 Step #3 Training a Time Series Regression Model 174 Step #4 Making Test Predictions. 174 Step #5 Calculating the Regression Error Metrics: Implementation and Evaluation. 175 CHAPTER 10: REGRESSION.. 177 Linear Regression. 178 Logistic Regression. 178 Polynomial Regression. 179 Ridge Regression. 179 Lasso Regression. 179 Regression Applications. 180 Difference between Regression and Classification in data mining. 180 Regression. 181 CHAPTER 11: MACHINE LEARNING.. 183 Machine Learning vs. Deep Learning vs. Neural Networks. 183 Machine learning methods. 184 Supervised machine learning. 184 Unsupervised machine learning. 185 Semi-supervised learning. 185 Common machine learning algorithms. 186 Real-world machine learning use cases. 187 Data Structure for Machine Learning. 188 What is Data Structure?. 190 Types of Data Structure. 190 1. Linear Data structure: 191 2. Non-linear Data Structures. 193 Dynamic array data structure: 196 How is Data Structure used in Machine Learning?. 197 Conclusion. 197 SUPERVISED LEARNING.. 198 Supervised Machine Learning. 198 How Supervised Learning Works?. 198 Steps Involved in Supervised Learning: 200 Types of supervised Machine learning Algorithms: 200 1. Regression. 201 2. Classification. 201 Advantages of Supervised learning: 202 Disadvantages of supervised learning: 202 Linear Regression. 202 How does it Work?. 204 R for Relationship. 208 Predict Future Values. 210 Bad Fit?. 211 Logistic Regression. 215 How does it work?. 215 Probability. 217 Function Explained. 217 Results Explained. 219 How to Save a Machine Learning Model 219 Two Ways to Save a Model from scikit-learn: 219 UNSUPERVISED LEARNING.. 225 Unsupervised Machine Learning. 225 Why use Unsupervised Learning?. 227 Working of Unsupervised Learning. 227 Types of Unsupervised Learning Algorithm: 228 Unsupervised Learning algorithms: 229 Advantages of Unsupervised Learning. 229 Disadvantages of Unsupervised Learning. 229 Supervised vs. Unsupervised Learning. 230 Preparing Data for Unsupervised Learning. 231 Clustering. 232 Hierarchical Clustering. 234 Difference between K-Means and Hierarchical clustering. 236 t-SNE Clustering. 237 DBSCAN Clustering. 238 OTHER MACHINE LEARNING (ML) ALGORITHMS.. 240 ABOUT THE AUTHOR.. 241 PYTHON WITH DATA SCIENCE CHAPTER 1: STATISTICS CONCEPTS Data science is an interdisciplinary field. One of the building blocks of data science is statistics. Without a decent level of statistics knowledge, it would be highly difficult to understand or interpret the data. Statistics helps us explain the data. Statistics is used to infer results about a population based on a sample drawn from that population. Furthermore, machine learning and statistics have plenty of overlaps. Statistics concepts helps us to become a data scientist.
Author: Gareth James Publisher: Springer Nature ISBN: 3031387473 Category : Mathematics Languages : en Pages : 617
Book Description
An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance, marketing, and astrophysics in the past twenty years. This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, deep learning, survival analysis, multiple testing, and more. Color graphics and real-world examples are used to illustrate the methods presented. This book is targeted at statisticians and non-statisticians alike, who wish to use cutting-edge statistical learning techniques to analyze their data. Four of the authors co-wrote An Introduction to Statistical Learning, With Applications in R (ISLR), which has become a mainstay of undergraduate and graduate classrooms worldwide, as well as an important reference book for data scientists. One of the keys to its success was that each chapter contains a tutorial on implementing the analyses and methods presented in the R scientific computing environment. However, in recent years Python has become a popular language for data science, and there has been increasing demand for a Python-based alternative to ISLR. Hence, this book (ISLP) covers the same materials as ISLR but with labs implemented in Python. These labs will be useful both for Python novices, as well as experienced users.
Author: José Unpingco Publisher: Springer ISBN: 3030185451 Category : Technology & Engineering Languages : en Pages : 396
Book Description
This book, fully updated for Python version 3.6+, covers the key ideas that link probability, statistics, and machine learning illustrated using Python modules in these areas. All the figures and numerical results are reproducible using the Python codes provided. The author develops key intuitions in machine learning by working meaningful examples using multiple analytical methods and Python codes, thereby connecting theoretical concepts to concrete implementations. Detailed proofs for certain important results are also provided. Modern Python modules like Pandas, Sympy, Scikit-learn, Tensorflow, and Keras are applied to simulate and visualize important machine learning concepts like the bias/variance trade-off, cross-validation, and regularization. Many abstract mathematical ideas, such as convergence in probability theory, are developed and illustrated with numerical examples. This updated edition now includes the Fisher Exact Test and the Mann-Whitney-Wilcoxon Test. A new section on survival analysis has been included as well as substantial development of Generalized Linear Models. The new deep learning section for image processing includes an in-depth discussion of gradient descent methods that underpin all deep learning algorithms. As with the prior edition, there are new and updated *Programming Tips* that the illustrate effective Python modules and methods for scientific programming and machine learning. There are 445 run-able code blocks with corresponding outputs that have been tested for accuracy. Over 158 graphical visualizations (almost all generated using Python) illustrate the concepts that are developed both in code and in mathematics. We also discuss and use key Python modules such as Numpy, Scikit-learn, Sympy, Scipy, Lifelines, CvxPy, Theano, Matplotlib, Pandas, Tensorflow, Statsmodels, and Keras. This book is suitable for anyone with an undergraduate-level exposure to probability, statistics, or machine learning and with rudimentary knowledge of Python programming.
Author: Peter Bruce Publisher: "O'Reilly Media, Inc." ISBN: 1491952911 Category : Computers Languages : en Pages : 322
Book Description
Statistical methods are a key part of of data science, yet very few data scientists have any formal statistics training. Courses and books on basic statistics rarely cover the topic from a data science perspective. This practical guide explains how to apply various statistical methods to data science, tells you how to avoid their misuse, and gives you advice on what's important and what's not. Many data science resources incorporate statistical methods but lack a deeper statistical perspective. If you’re familiar with the R programming language, and have some exposure to statistics, this quick reference bridges the gap in an accessible, readable format. With this book, you’ll learn: Why exploratory data analysis is a key preliminary step in data science How random sampling can reduce bias and yield a higher quality dataset, even with big data How the principles of experimental design yield definitive answers to questions How to use regression to estimate outcomes and detect anomalies Key classification techniques for predicting which categories a record belongs to Statistical machine learning methods that “learn” from data Unsupervised learning methods for extracting meaning from unlabeled data
Author: Sayan Mukhopadhyay Publisher: Apress ISBN: 1484234502 Category : Computers Languages : en Pages : 195
Book Description
Gain a broad foundation of advanced data analytics concepts and discover the recent revolution in databases such as Neo4j, Elasticsearch, and MongoDB. This book discusses how to implement ETL techniques including topical crawling, which is applied in domains such as high-frequency algorithmic trading and goal-oriented dialog systems. You’ll also see examples of machine learning concepts such as semi-supervised learning, deep learning, and NLP. Advanced Data Analytics Using Python also covers important traditional data analysis techniques such as time series and principal component analysis. After reading this book you will have experience of every technical aspect of an analytics project. You’ll get to know the concepts using Python code, giving you samples to use in your own projects. What You Will Learn Work with data analysis techniques such as classification, clustering, regression, and forecasting Handle structured and unstructured data, ETL techniques, and different kinds of databases such as Neo4j, Elasticsearch, MongoDB, and MySQL Examine the different big data frameworks, including Hadoop and Spark Discover advanced machine learning concepts such as semi-supervised learning, deep learning, and NLP Who This Book Is For Data scientists and software developers interested in the field of data analytics.
Author: John Paul Mueller Publisher: John Wiley & Sons ISBN: 1118843983 Category : Computers Languages : en Pages : 432
Book Description
Unleash the power of Python for your data analysis projects with For Dummies! Python is the preferred programming language for data scientists and combines the best features of Matlab, Mathematica, and R into libraries specific to data analysis and visualization. Python for Data Science For Dummies shows you how to take advantage of Python programming to acquire, organize, process, and analyze large amounts of information and use basic statistics concepts to identify trends and patterns. You’ll get familiar with the Python development environment, manipulate data, design compelling visualizations, and solve scientific computing challenges as you work your way through this user-friendly guide. Covers the fundamentals of Python data analysis programming and statistics to help you build a solid foundation in data science concepts like probability, random distributions, hypothesis testing, and regression models Explains objects, functions, modules, and libraries and their role in data analysis Walks you through some of the most widely-used libraries, including NumPy, SciPy, BeautifulSoup, Pandas, and MatPlobLib Whether you’re new to data analysis or just new to Python, Python for Data Science For Dummies is your practical guide to getting a grip on data overload and doing interesting things with the oodles of information you uncover.
Author: Laura Igual Publisher: Springer ISBN: 3319500171 Category : Computers Languages : en Pages : 227
Book Description
This accessible and classroom-tested textbook/reference presents an introduction to the fundamentals of the emerging and interdisciplinary field of data science. The coverage spans key concepts adopted from statistics and machine learning, useful techniques for graph analysis and parallel programming, and the practical application of data science for such tasks as building recommender systems or performing sentiment analysis. Topics and features: provides numerous practical case studies using real-world data throughout the book; supports understanding through hands-on experience of solving data science problems using Python; describes techniques and tools for statistical analysis, machine learning, graph analysis, and parallel programming; reviews a range of applications of data science, including recommender systems and sentiment analysis of text data; provides supplementary code resources and data at an associated website.
Author: Daniel J. Denis Publisher: John Wiley & Sons ISBN: 1119578183 Category : Mathematics Languages : en Pages : 304
Book Description
Applied Univariate, Bivariate, and Multivariate Statistics Using Python A practical, “how-to” reference for anyone performing essential statistical analyses and data management tasks in Python Applied Univariate, Bivariate, and Multivariate Statistics Using Python delivers a comprehensive introduction to a wide range of statistical methods performed using Python in a single, one-stop reference. The book contains user-friendly guidance and instructions on using Python to run a variety of statistical procedures without getting bogged down in unnecessary theory. Throughout, the author emphasizes a set of computational tools used in the discovery of empirical patterns, as well as several popular statistical analyses and data management tasks that can be immediately applied. Most of the datasets used in the book are small enough to be easily entered into Python manually, though they can also be downloaded for free from www.datapsyc.com. Only minimal knowledge of statistics is assumed, making the book perfect for those seeking an easily accessible toolkit for statistical analysis with Python. Applied Univariate, Bivariate, and Multivariate Statistics Using Python represents the fastest way to learn how to analyze data with Python. Readers will also benefit from the inclusion of: A review of essential statistical principles, including types of data, measurement, significance tests, significance levels, and type I and type II errors An introduction to Python, exploring how to communicate with Python A treatment of exploratory data analysis, basic statistics and visual displays, including frequencies and descriptives, q-q plots, box-and-whisker plots, and data management An introduction to topics such as ANOVA, MANOVA and discriminant analysis, regression, principal components analysis, factor analysis, cluster analysis, among others, exploring the nature of what these techniques can vs. cannot do on a methodological level Perfect for undergraduate and graduate students in the social, behavioral, and natural sciences, Applied Univariate, Bivariate, and Multivariate Statistics Using Python will also earn a place in the libraries of researchers and data analysts seeking a quick go-to resource for univariate, bivariate, and multivariate analysis in Python.
Author: Yves J. Hilpisch Publisher: "O'Reilly Media, Inc." ISBN: 1492024295 Category : Computers Languages : en Pages : 682
Book Description
The financial industry has recently adopted Python at a tremendous rate, with some of the largest investment banks and hedge funds using it to build core trading and risk management systems. Updated for Python 3, the second edition of this hands-on book helps you get started with the language, guiding developers and quantitative analysts through Python libraries and tools for building financial applications and interactive financial analytics. Using practical examples throughout the book, author Yves Hilpisch also shows you how to develop a full-fledged framework for Monte Carlo simulation-based derivatives and risk analytics, based on a large, realistic case study. Much of the book uses interactive IPython Notebooks.
Author: Peter Farrell Publisher: Packt Publishing Ltd ISBN: 1800208367 Category : Computers Languages : en Pages : 739
Book Description
With examples and activities that help you achieve real results, applying calculus and statistical methods relevant to advanced data science has never been so easy Key FeaturesDiscover how most programmers use the main Python libraries when performing statistics with PythonUse descriptive statistics and visualizations to answer business and scientific questionsSolve complicated calculus problems, such as arc length and solids of revolution using derivatives and integralsBook Description Are you looking to start developing artificial intelligence applications? Do you need a refresher on key mathematical concepts? Full of engaging practical exercises, The Statistics and Calculus with Python Workshop will show you how to apply your understanding of advanced mathematics in the context of Python. The book begins by giving you a high-level overview of the libraries you'll use while performing statistics with Python. As you progress, you'll perform various mathematical tasks using the Python programming language, such as solving algebraic functions with Python starting with basic functions, and then working through transformations and solving equations. Later chapters in the book will cover statistics and calculus concepts and how to use them to solve problems and gain useful insights. Finally, you'll study differential equations with an emphasis on numerical methods and learn about algorithms that directly calculate values of functions. By the end of this book, you'll have learned how to apply essential statistics and calculus concepts to develop robust Python applications that solve business challenges. What you will learnGet to grips with the fundamental mathematical functions in PythonPerform calculations on tabular datasets using pandasUnderstand the differences between polynomials, rational functions, exponential functions, and trigonometric functionsUse algebra techniques for solving systems of equationsSolve real-world problems with probabilitySolve optimization problems with derivatives and integralsWho this book is for If you are a Python programmer who wants to develop intelligent solutions that solve challenging business problems, then this book is for you. To better grasp the concepts explained in this book, you must have a thorough understanding of advanced mathematical concepts, such as Markov chains, Euler's formula, and Runge-Kutta methods as the book only explains how these techniques and concepts can be implemented in Python.
Author: MEENACHISUNDARAM.M
Author: MEENACHISUNDARAM.M
Author: Gareth James
Author: José Unpingco
Author: Peter Bruce
Author: Sayan Mukhopadhyay
Author: John Paul Mueller
Author: Laura Igual
Author: Daniel J. Denis
Author: Yves J. Hilpisch
Author: Peter Farrell