Martin Czygan's Books

Python: Real-World Data Science

By: Dusty Phillips,Fabrizio Romano,Phuong Vo.T.H,Martin Czygan,Robert Layton,Sebastian Raschka

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Python: Real-World Data Science

By: Dusty Phillips,Fabrizio Romano,Phuong Vo.T.H,Martin Czygan,Robert Layton,Sebastian Raschka

Unleash the power of Python and its robust data science capabilities About This Book Unleash the power of Python 3 objects Learn to use powerful Python libraries for effective data processing and analysis Harness the power of Python to analyze data and create insightful predictive models Unlock deeper insights into machine learning with this vital guide to cutting-edge predictive analytics Who This Book Is For Entry-level analysts who want to enter in the data science world will find this course very useful to get themselves acquainted with Python's data science capabilities for doing real-world data analysis. What You Will Learn Install and setup Python Implement objects in Python by creating classes and defining methods Get acquainted with NumPy to use it with arrays and array-oriented computing in data analysis Create effective visualizations for presenting your data using Matplotlib Process and analyze data using the time series capabilities of pandas Interact with different kind of database systems, such as file, disk format, Mongo, and Redis Apply data mining concepts to real-world problems Compute on big data, including real-time data from the Internet Explore how to use different machine learning models to ask different questions of your data In Detail The Python: Real-World Data Science course will take you on a journey to become an efficient data science practitioner by thoroughly understanding the key concepts of Python. This learning path is divided into four modules and each module are a mini course in their own right, and as you complete each one, you'll have gained key skills and be ready for the material in the next module. The course begins with getting your Python fundamentals nailed down. After getting familiar with Python core concepts, it's time that you dive into the field of data science. In the second module, you'll learn how to perform data analysis using Python in a practical and example-driven way. The third module will teach you how to design and develop data mining applications using a variety of datasets, starting with basic classification and affinity analysis to more complex data types including text, images, and graphs. Machine learning and predictive analytics have become the most important approaches to uncover data gold mines. In the final module, we'll discuss the necessary details regarding machine learning concepts, offering intuitive yet informative explanations on how machine learning algorithms work, how to use them, and most importantly, how to avoid the common pitfalls. Style and approach This course includes all the resources that will help you jump into the data science field with Python and learn how to make sense of data. The aim is to create a smooth learning path that will teach you how to get started with powerful Python libraries and perform various data science techniques in depth.

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1255

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Packt Publishing Ltd

Published Date

ISBN 10

1786468417

ISBN 13

9781786468413

Python: End-to-end Data Analysis

By: Phuong Vothihong,Martin Czygan,Ivan Idris,Magnus Vilhelm Persson,Luiz Felipe Martins

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Python: End-to-end Data Analysis

By: Phuong Vothihong,Martin Czygan,Ivan Idris,Magnus Vilhelm Persson,Luiz Felipe Martins

Leverage the power of Python to clean, scrape, analyze, and visualize your data About This Book Clean, format, and explore your data using the popular Python libraries and get valuable insights from it Analyze big data sets; create attractive visualizations; manipulate and process various data types using NumPy, SciPy, and matplotlib; and more Packed with easy-to-follow examples to develop advanced computational skills for the analysis of complex data Who This Book Is For This course is for developers, analysts, and data scientists who want to learn data analysis from scratch. This course will provide you with a solid foundation from which to analyze data with varying complexity. A working knowledge of Python (and a strong interest in playing with your data) is recommended. What You Will Learn Understand the importance of data analysis and master its processing steps Get comfortable using Python and its associated data analysis libraries such as Pandas, NumPy, and SciPy Clean and transform your data and apply advanced statistical analysis to create attractive visualizations Analyze images and time series data Mine text and analyze social networks Perform web scraping and work with different databases, Hadoop, and Spark Use statistical models to discover patterns in data Detect similarities and differences in data with clustering Work with Jupyter Notebook to produce publication-ready figures to be included in reports In Detail Data analysis is the process of applying logical and analytical reasoning to study each component of data present in the system. Python is a multi-domain, high-level, programming language that offers a range of tools and libraries suitable for all purposes, it has slowly evolved as one of the primary languages for data science. Have you ever imagined becoming an expert at effectively approaching data analysis problems, solving them, and extracting all of the available information from your data? If yes, look no further, this is the course you need! In this course, we will get you started with Python data analysis by introducing the basics of data analysis and supported Python libraries such as matplotlib, NumPy, and pandas. Create visualizations by choosing color maps, different shapes, sizes, and palettes then delve into statistical data analysis using distribution algorithms and correlations. You'll then find your way around different data and numerical problems, get to grips with Spark and HDFS, and set up migration scripts for web mining. You'll be able to quickly and accurately perform hands-on sorting, reduction, and subsequent analysis, and fully appreciate how data analysis methods can support business decision-making. Finally, you will delve into advanced techniques such as performing regression, quantifying cause and effect using Bayesian methods, and discovering how to use Python's tools for supervised machine learning. The course provides you with highly practical content explaining data analysis with Python, from the following Packt books: Getting Started with Python Data Analysis. Python Data Analysis Cookbook. Mastering Python Data Analysis. By the end of this course, you will have all the knowledge you need to analyze your data with varying complexity levels, and turn it into actionable insights. Style and approach Learn Python data analysis using engaging examples and fun exercises, and with a gentle and friendly but comprehensive "learn-by-doing" approach. It offers you a useful way of analyzing the data that's specific to this course, but that can also be applied to any other data. This course is designed to be both a guide and a reference for moving beyond the basics of data analysis.

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911

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Packt Publishing Ltd

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ISBN 10

1788396545

ISBN 13

9781788396547

Inhibition of Tumor Induction and Development

By: Morris S. Zedeck,Martin Lipkin

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Inhibition of Tumor Induction and Development

By: Morris S. Zedeck,Martin Lipkin

The primary purpose of this book is to bring to the attention of members of the medical and scientific communities, as well as to other interested persons, a new and expanding area of investigation that features the use of chemicals for the prevention of tumor induction and development. This novel use of chemical compounds has succeeded in producing a remarkable series of discoveries in recent years. Some of these are beginning to be evaluated in the field of clinical oncology in a manner that has potentially enormous public health implications. It is anticipated, therefore, that increasing amounts of time, energy, and financial resources will be devoted to the further development and expansion of this work. The major contribution of this book at the present time is that it summarizes and brings up to date the pioneering efforts of the various scientists who originated this new and exciting field of scientific activity. The thoughts expressed by Louis Pasteur in 1884 may soon be applicable in the fight against cancer: "When meditating over a disease, I never think of finding a remedy for it, but instead a means of preventing it. " The emphasis on cancer prevention currently underway is the result, in part, of an increased awareness that the environment-geographical, cultural, and occupational-has a role in development of the disease.

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242

Publisher

Springer Science & Business Media

Published Date

ISBN 10

1461592186

ISBN 13

9781461592181

The Oxford Handbook of Evolutionary Medicine

By: Martin Brüne,Wulf Schiefenhövel

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The Oxford Handbook of Evolutionary Medicine

By: Martin Brüne,Wulf Schiefenhövel

Medicine is grounded in the natural sciences, where biology stands out with regard to our understanding of human physiology and the conditions that cause dysfunction. Ironically though, evolutionary biology is a relatively disregarded field. One reason for this omission is that evolution is deemed a slow process. Indeed, the macroanatomical features of our species have changed very little in the last 300,000 years. A more detailed look, however, reveals that novel ecological contingencies, partly in relation to cultural evolution, have brought about subtle changes pertaining to metabolism and immunology, including adaptations to dietary innovations, as well as adaptations to the exposure to novel pathogens. Rapid pathogen evolution and evolution of cancer cells cause major problems for the immune system. Moreover, many adaptations to past ecologies have actually turned into risk factors for somatic disease and psychological disorder in our modern worlds (i.e. mismatch), among which epidemics of autoimmune diseases, cardiovascular diseases, diabetes and obesity, as well as several forms of cancer stand out. One could add depression, anxiety, and other psychiatric conditions to the list. The Oxford Handbook of Evolutionary Medicine is a compilation of up-to-date insights into the evolutionary history of ourselves as a species, exploring how and why our evolved design may convey vulnerability to disease. Written in a classic textbook style emphasising physiology and pathophysiology of all major organ systems, the Oxford Handbook of Evolutionary Medicine is valuable reading for students as well as scholars in the fields of medicine, biology, anthropology and psychology.

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1225

Publisher

Oxford University Press

Published Date

ISBN 10

019250679X

ISBN 13

9780192506795

Python: Real-World Data Science

By: Dusty Phillips,Fabrizio Romano,Phuong Vo.T.H,Martin Czygan,Robert Layton,Sebastian Raschka

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Python: Real-World Data Science

By: Dusty Phillips,Fabrizio Romano,Phuong Vo.T.H,Martin Czygan,Robert Layton,Sebastian Raschka

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1255

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Packt Publishing Ltd

Published Date

ISBN 10

1786468417

ISBN 13

9781786468413

Python: End-to-end Data Analysis

By: Phuong Vothihong,Martin Czygan,Ivan Idris,Magnus Vilhelm Persson,Luiz Felipe Martins

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Python: End-to-end Data Analysis

By: Phuong Vothihong,Martin Czygan,Ivan Idris,Magnus Vilhelm Persson,Luiz Felipe Martins

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911

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Packt Publishing Ltd

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ISBN 10

1788396545

ISBN 13

9781788396547

Python: Data Analytics and Visualization

By: Phuong Vo.T.H,Martin Czygan,Ashish Kumar,Kirthi Raman

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Python: Data Analytics and Visualization

By: Phuong Vo.T.H,Martin Czygan,Ashish Kumar,Kirthi Raman

Understand, evaluate, and visualize data About This Book Learn basic steps of data analysis and how to use Python and its packages A step-by-step guide to predictive modeling including tips, tricks, and best practices Effectively visualize a broad set of analyzed data and generate effective results Who This Book Is For This book is for Python Developers who are keen to get into data analysis and wish to visualize their analyzed data in a more efficient and insightful manner. What You Will Learn Get acquainted with NumPy and use arrays and array-oriented computing in data analysis Process and analyze data using the time-series capabilities of Pandas Understand the statistical and mathematical concepts behind predictive analytics algorithms Data visualization with Matplotlib Interactive plotting with NumPy, Scipy, and MKL functions Build financial models using Monte-Carlo simulations Create directed graphs and multi-graphs Advanced visualization with D3 In Detail You will start the course with an introduction to the principles of data analysis and supported libraries, along with NumPy basics for statistics and data processing. Next, you will overview the Pandas package and use its powerful features to solve data-processing problems. Moving on, you will get a brief overview of the Matplotlib API .Next, you will learn to manipulate time and data structures, and load and store data in a file or database using Python packages. You will learn how to apply powerful packages in Python to process raw data into pure and helpful data using examples. You will also get a brief overview of machine learning algorithms, that is, applying data analysis results to make decisions or building helpful products such as recommendations and predictions using Scikit-learn. After this, you will move on to a data analytics specialization—predictive analytics. Social media and IOT have resulted in an avalanche of data. You will get started with predictive analytics using Python. You will see how to create predictive models from data. You will get balanced information on statistical and mathematical concepts, and implement them in Python using libraries such as Pandas, scikit-learn, and NumPy. You'll learn more about the best predictive modeling algorithms such as Linear Regression, Decision Tree, and Logistic Regression. Finally, you will master best practices in predictive modeling. After this, you will get all the practical guidance you need to help you on the journey to effective data visualization. Starting with a chapter on data frameworks, which explains the transformation of data into information and eventually knowledge, this path subsequently cover the complete visualization process using the most popular Python libraries with working examples This Learning Path combines some of the best that Packt has to offer in one complete, curated package. It includes content from the following Packt products: Getting Started with Python Data Analysis, Phuong Vo.T.H &Martin Czygan Learning Predictive Analytics with Python, Ashish Kumar Mastering Python Data Visualization, Kirthi Raman Style and approach The course acts as a step-by-step guide to get you familiar with data analysis and the libraries supported by Python with the help of real-world examples and datasets. It also helps you gain practical insights into predictive modeling by implementing predictive-analytics algorithms on public datasets with Python. The course offers a wealth of practical guidance to help you on this journey to data visualization

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866

Publisher

Packt Publishing Ltd

Published Date

ISBN 10

1788294858

ISBN 13

9781788294850

Book's by Martin Czygan

Python: Real-World Data Science

Python: Real-World Data Science

Python: End-to-end Data Analysis

Python: End-to-end Data Analysis

Inhibition of Tumor Induction and Development

Inhibition of Tumor Induction and Development

The Oxford Handbook of Evolutionary Medicine

The Oxford Handbook of Evolutionary Medicine

Python: Real-World Data Science

Python: Real-World Data Science

Python: End-to-end Data Analysis

Python: End-to-end Data Analysis

Python: Data Analytics and Visualization

Python: Data Analytics and Visualization

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