Now that people are aware that data can make the difference in an election or a business model, data science as an occupation is gaining ground. But how can you get started working in a wide-ranging, interdisciplinary field that's so clouded in hype? This insightful book, based on Columbia University's Introduction to Data Science class, tells you what you need to know.

In many of these chapter-long lectures, data scientists from companies such as Google, Microsoft, and eBay share new algorithms, methods, and models by presenting case studies and the code they use. If you're familiar with linear algebra, probability, and statistics, and have programming experience, this book is an ideal introduction to data science.

Topics include:

• Statistical inference, exploratory data analysis, and the data science process
• Algorithms
• Spam filters, Naive Bayes, and data wrangling
• Logistic regression
• Financial modeling
• Recommendation engines and causality
• Data visualization
• Social networks and data journalism
• Data engineering, MapReduce, Pregel, and Hadoop

Doing Data Science is collaboration between course instructor Rachel Schutt, Senior VP of Data Science at News Corp, and data science consultant Cathy O'Neil, a senior data scientist at Johnson Research Labs, who attended and blogged about the course.

What time is it?

• One hour increments.
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• 15 minute increments.
• 5 minute increments.
• 1 minute increments.

Draw the hands on the clocks

• One hour increments.
• Half-hour increments.
• 15 minute increments.
• 5 minute increments.
• 1 minute increments.

Answer key included

100 Pages

Are you looking to learn more about Time Series, but struggling to find them in traditional Data Science textbooks?

This book is your answer.

Time Series is an exciting and important part of Data Analysis. Time Series Data is more readily available than most forms of data and answers questions that cross-sectional data struggle to do. It also has more real world application in the prediction of future events. However it is not generally found in a traditional data science toolkit. There is also limited centralized resources on the applications of Time Series, especially using traditional programming languages such as Python.

This book solves all these problems, and more. It starts off with basic concepts in Time Series, and switches to more advanced topics. It shows you how to set up Python from start, and goes through over 20 examples of applying both simple and advanced Time Series concepts with Python code.

Innovation Diffusion Models

Understand innovation diffusion models and their role in business success

Innovation diffusion models are statistical models that predict the medium- and long-term sales performance of new products on a market. They account for numerous factors that contribute to the life cycle of a new product and are subject to continuous reassessment as markets transform and the business world becomes more complex. In a modern market environment where product life cycles are becoming ever shorter, the latest innovation diffusion models are essential for businesses looking to perfect their decision-making processes.

Innovation Diffusion Models: Theory and Practice provides a comprehensive and up-to-date guide to these models and their potential to impact product development. It focuses on the latest product diffusion models, which combine time series analysis with nonlinear regression techniques to create increasingly refined predictions. Its combination of mathematical theory and business practice makes it an indispensable tool across many sectors of industry and commerce.

Innovation Diffusion Models readers will also find:

• Real-world examples demonstrating the kinds of data sets generated by new product growth models and their potential applications
• Discussion of the factors underlying the decision to select a given growth model for a particular product
• Clear, detailed explanation of each model's explanatory ability

Innovation Diffusion Models is an essential volume for practitioners in any field of industry or commerce, as well as for graduate students and researchers in business and finance.

Features

- Worked examples

- Clear programming code, usable by R novices

- Without mathematical formulae

- structural equation modeling

- survival analysis

- longitudinal analysis

- multivariate analysis

- GLM, Poisson regression, multilevel modeling

- power analysis, reliability

- Further reading recommendations in each chapter

- Data sets on the website

Features

- Worked examples

- Clear programming code, usable by R novices

- Without mathematical formulae

- structural equation modeling

- survival analysis

- longitudinal analysis

- multivariate analysis

- GLM, Poisson regression, multilevel modeling

- power analysis, reliability

- Further reading recommendations in each chapter

- Data sets on the website

Tales of the Tail: Legendary Cats Throughout History is an enchanting exploration into the feline realm, delving into the captivating stories of extraordinary cats that have left an indelible mark on human history. This whimsical tome unveils the rich tapestry of legends, folklore, and real-life anecdotes surrounding these enigmatic creatures, celebrating their mystique and the profound impact they have had on various cultures.

Within the pages of Tales of the Tail, readers will embark on a journey through time, discovering mythical cats that have been revered as divine entities, guardians, and symbols of good fortune. From ancient civilizations to modern times, the book weaves together narratives of cats that have transcended the ordinary, earning their place as revered beings in the annals of history.

The tales within this collection are not limited to mere whimsy; they are intertwined with historical events, cultural beliefs, and the evolving roles that cats have played in societies around the world. Whether exploring the sacred cats of ancient Egypt, the mischievous Cheshire Cat of Wonderland fame, or the famed literary cat companions of renowned authors, each story is a testament to the profound connections between humans and their feline counterparts.

Each chapter unfolds like a storybook, with vivid descriptions and captivating illustrations bringing these legendary cats to life. From the graceful and regal to the cunning and magical, Tales of the Tail pays homage to the diverse personalities and qualities that have made cats such beloved companions throughout the ages.

This enchanting collection is a celebration of the enduring fascination with cats and their timeless allure. Tales of the Tail invites readers to immerse themselves in the magical world of legendary felines, where history, mythology, and the timeless charm of cats converge in a purrfectly delightful tapestry of tales.

Data Science students and practitioners want to find a forecast that works and don't want to be constrained to a single forecasting strategy, Time Series for Data Science: Analysis and Forecasting discusses techniques of ensemble modelling for combining information from several strategies. Covering time series regression models, exponential smoothing, Holt-Winters forecasting, and Neural Networks. It places a particular emphasis on classical ARMA and ARIMA models that is often lacking from other textbooks on the subject.

This book is an accessible guide that doesn't require a background in calculus to be engaging but does not shy away from deeper explanations of the techniques discussed.

Features:

• Provides a thorough coverage and comparison of a wide array of time series models and methods: Exponential Smoothing, Holt Winters, ARMA and ARIMA, deep learning models including RNNs, LSTMs, GRUs, and ensemble models composed of combinations of these models.
• Introduces the factor table representation of ARMA and ARIMA models. This representation is not available in any other book at this level and is extremely useful in both practice and pedagogy.
• Uses real world examples that can be readily found via web links from sources such as the US Bureau of Statistics, Department of Transportation and the World Bank.
• There is an accompanying R package that is easy to use and requires little or no previous R experience. The package implements the wide variety of models and methods presented in the book and has tremendous pedagogical use.

Math the clock to the time

• One hour increments.
• Half-hour increments.
• 15 minute increments.
• 5 minute increments.
• 1 minute increments.

Answer key included

100 Pages

Data Science students and practitioners want to find a forecast that works and don't want to be constrained to a single forecasting strategy, Time Series for Data Science: Analysis and Forecasting discusses techniques of ensemble modelling for combining information from several strategies. Covering time series regression models, exponential smoothing, Holt-Winters forecasting, and Neural Networks. It places a particular emphasis on classical ARMA and ARIMA models that is often lacking from other textbooks on the subject.

This book is an accessible guide that doesn't require a background in calculus to be engaging but does not shy away from deeper explanations of the techniques discussed.

Features:

• Provides a thorough coverage and comparison of a wide array of time series models and methods: Exponential Smoothing, Holt Winters, ARMA and ARIMA, deep learning models including RNNs, LSTMs, GRUs, and ensemble models composed of combinations of these models.
• Introduces the factor table representation of ARMA and ARIMA models. This representation is not available in any other book at this level and is extremely useful in both practice and pedagogy.
• Uses real world examples that can be readily found via web links from sources such as the US Bureau of Statistics, Department of Transportation and the World Bank.
• There is an accompanying R package that is easy to use and requires little or no previous R experience. The package implements the wide variety of models and methods presented in the book and has tremendous pedagogical use.

Explore all the tools and templates needed for data scientists to drive success in their biotechnology careers with this comprehensive guide

Key Features:

• Learn the applications of machine learning in biotechnology and life science sectors
• Discover exciting real-world applications of deep learning and natural language processing
• Understand the general process of deploying models to cloud platforms such as AWS and GCP

Book Description:

The booming fields of biotechnology and life sciences have seen drastic changes over the last few years. With competition growing in every corner, companies around the globe are looking to data-driven methods such as machine learning to optimize processes and reduce costs. This book helps lab scientists, engineers, and managers to develop a data scientist's mindset by taking a hands-on approach to learning about the applications of machine learning to increase productivity and efficiency in no time.

You'll start with a crash course in Python, SQL, and data science to develop and tune sophisticated models from scratch to automate processes and make predictions in the biotechnology and life sciences domain. As you advance, the book covers a number of advanced techniques in machine learning, deep learning, and natural language processing using real-world data.

By the end of this machine learning book, you'll be able to build and deploy your own machine learning models to automate processes and make predictions using AWS and GCP.

What You Will Learn:

• Get started with Python programming and Structured Query Language (SQL)
• Develop a machine learning predictive model from scratch using Python
• Fine-tune deep learning models to optimize their performance for various tasks
• Find out how to deploy, evaluate, and monitor a model in the cloud
• Understand how to apply advanced techniques to real-world data
• Discover how to use key deep learning methods such as LSTMs and transformers

Who this book is for:

This book is for data scientists and scientific professionals looking to transcend to the biotechnology domain. Scientific professionals who are already established within the pharmaceutical and biotechnology sectors will find this book useful. A basic understanding of Python programming and beginner-level background in data science conjunction is needed to get the most out of this book.

Deep learning is an important element of artificial intelligence, especially in applications such as image classification in which various architectures of neural network, e.g., convolutional neural networks, have yielded reliable results. This book introduces deep learning for time series analysis, particularly for cyclic time series. It elaborates on the methods employed for time series analysis at the deep level of their architectures. Cyclic time series usually have special traits that can be employed for better classification performance. These are addressed in the book. Processing cyclic time series is also covered herein.

An important factor in classifying stochastic time series is the structural risk associated with the architecture of classification methods. The book addresses and formulates structural risk, and the learning capacity defined for a classification method. These formulations and the mathematical derivations will help the researchers in understanding the methods and even express their methodologies in an objective mathematical way. The book has been designed as a self-learning textbook for the readers with different backgrounds and understanding levels of machine learning, including students, engineers, researchers, and scientists of this domain. The numerous informative illustrations presented by the book will lead the readers to a deep level of understanding about the deep learning methods for time series analysis.

This book gives a brief survey of the theory of multidimensional (multivariate), weakly stationary time series, with emphasis on dimension reduction and prediction.

This book proposes some novel models based on the autoregressive and moving average structures under various distributional assumptions of the innovation series for analysing non-stationary bivariate time series of counts.

Time series of count responses are recorded for different correlated variables which may be marginally dispersed relative to their means, may exhibit different levels of dispersion and may be commonly influenced by one or more dynamic explanatory variables. Analysis of such type of bivariate time series data is quite challenging and the challenge mounts up further if these time series are non-stationary. This book proposes some bivariate models that allow for different levels of dispersion as well as non-stationarity. Specifically, BINAR(1) and BINMA(1) models under Poisson, NB and COM-Poisson innovations are constructed and tested. Another important contribution of this book is in developing a novel estimation procedure for estimating the parameters of the proposed BINAR(1) and BINMA(1) models. Hence, a new estimation approach based on the GQL is proposed. Monte-Carlo simulations are implemented to assess the performance of the GQL. In some simple cases of stationarity, we also compare the GQL with the other estimation techniques such as CMLE and FGLS.

This book is a useful resource for undergraduate students, postgraduate students, researchers and academics in the field of time series models.

The recent advent of rapid technological changes, scientific developments, and educational expansions have created complex heterogeneities, environmental uncertainties, and socio-economic-ecological inequalities globally. The innovative beneficial resources upgrade and update the existing varieties of structural features such as hereditary, random environmental, spatial and atmospheric perturbations in human population dynamics processes and predator-prey systems. The highly interconnected system under operating random environmental conditions is represented by nonlinear nonstationary large-scale multi-level hierarchical network-centric dynamic processes of Ito-Doob and finite Markovian types with network-centric structural perturbations. For instance, complex spatial, behavioral, and epidemiological structures in human populations vary from citizen to visitor; practicing and adhering to different disease preventive measures at sites in meta-populations; and different ages, stages and resistance levels to infections, respectively. An advantage of the presented results in simple algebraic system parameters form is easy verification and application to planning, prevention, policies, stabilization, monitoring and diseases management.

Introduction to Time Series Using Stata, Revised Edition provides a step-by-step guide to essential time-series techniques-from the incredibly simple to the quite complex- and, at the same time, demonstrates how these techniques can be applied in the Stata statistical package. The emphasis is on an understanding of the intuition underlying theoretical innovations and an ability to apply them. Real-world examples illustrate the application of each concept as it is introduced, and care is taken to highlight the pitfalls, as well as the power, of each new tool. The Revised Edition has been updated for Stata 16.

Are you looking to learn more about Time Series, but struggling to find them in traditional Data Science textbooks?

This book is your answer.

Time Series is an exciting and important part of Data Analysis. Time Series Data is more readily available than most forms of data and answers questions that cross-sectional data struggle to do. It also has more real world application in the prediction of future events. However it is not generally found in a traditional data science toolkit. There is also limited centralized resources on the applications of Time Series, especially using traditional programming languages such as Python.

This book solves all these problems, and more. It starts off with basic concepts in Time Series, and switches to more advanced topics. It shows you how to set up Python from start, and goes through over 20 examples of applying both simple and advanced Time Series concepts with Python code.

Here's What's Included In this Book:

What is a Time Series?

4 Different Elements of a Time Series

Why Python is the best way to Implement Time Series

Step by Step Guide to Installing Python and Importing Time Series Data

6 Different Techniques to Analyze Time Series Data

3 Advanced Time Series Concepts for Time Series Prediction

Time Series Visualization Techniques in Python

Even if you've never implemented Time Series before, you will still find this book useful.

Empirical likelihood (EL) for time series and spatial data is an active area of research in statistics and econometrics. This book provides an accessible account of the empirical likelihood methodology and an up-to-date account of the latest advances on the topic. Applications for time series, econometrics, high-dimensional data and spatial data are covered as well as frequency domain empirical likelihood and long-range dependence.

The book is intended for statisticians who want to learn about EL methodology and conduct research in this area. It is also useful for practitioners who want to apply the EL methodology in their work, but do not have the time to cover the literature scattered over various journal articles. In addition to statisticians, researchers working in economics, finance, electrical engineering (signal processing, communications), climatology and environmental sciences will find the book useful.

This edition contains a large number of additions and corrections scattered throughout the text, including the incorporation of a new chapter on state-space models. The companion diskette for the IBM PC has expanded into the software package ITSM: An Interactive Time Series Modelling Package for the PC, which includes a manual and can be ordered from Springer-Verlag. * We are indebted to many readers who have used the book and programs and made suggestions for improvements. Unfortunately there is not enough space to acknowledge all who have contributed in this way; however, special mention must be made of our prize-winning fault-finders, Sid Resnick and F. Pukelsheim. Special mention should also be made of Anthony Brockwell, whose advice and support on computing matters was invaluable in the preparation of the new diskettes. We have been fortunate to work on the new edition in the excellent environments provided by the University of Melbourne and Colorado State University. We thank Duane Boes particularly for his support and encouragement throughout, and the Australian Research Council and National Science Foundation for their support of research related to the new material. We are also indebted to Springer-Verlag for their constant support and assistance in preparing the second edition. Fort Collins, Colorado P. J. BROCKWELL November, 1990 R. A. DAVIS * /TSM: An Interactive Time Series Modelling Package for the PC by P. J. Brockwell and R. A. Davis. ISBN: 0-387-97482-2; 1991.

Gretl is an econometrics package, including a shared library, a command-line client program and a graphical user interface. This book is a comprehensive user guide to Gretl.