Dmitry Zinoviev – författare

Departing from traditional methodologies of teaching data analysis, this book presents a dual-track learning experience, with both Executive and Technical Tracks, designed to accommodate readers with various learning goals or skill levels. Through integrated content, readers can explore fundamental concepts in data analysis while gaining hands-on experience with Python programming, ensuring a holistic understanding of theory and practical application in Python.

Emphasizing the practical relevance of data analysis in today''s world, the book equips readers with essential skills for success in the field. By advocating for the use of Python, an open-source and versatile programming language, we break down financial barriers and empower a diverse range of learners to access the tools they need to excel.

Whether you''re a novice seeking to grasp the foundational concepts of data analysis or a seasoned professional looking to enhance your programming skills, this book offers a comprehensive and accessible guide to mastering the art and science of data analysis in social science research.

Key Features:

Departing from traditional methodologies of teaching data analysis, this book presents a dual-track learning experience, with both Executive and Technical Tracks, designed to accommodate readers with various learning goals or skill levels. Through integrated content, readers can explore fundamental concepts in data analysis while gaining hands-on experience with Python programming, ensuring a holistic understanding of theory and practical application in Python.

Emphasizing the practical relevance of data analysis in today''s world, the book equips readers with essential skills for success in the field. By advocating for the use of Python, an open-source and versatile programming language, we break down financial barriers and empower a diverse range of learners to access the tools they need to excel.

Whether you''re a novice seeking to grasp the foundational concepts of data analysis or a seasoned professional looking to enhance your programming skills, this book offers a comprehensive and accessible guide to mastering the art and science of data analysis in social science research.

Key Features:

Go from messy, unstructured artifacts stored in SQL and NoSQL databases to a neat, well-organized dataset with this quick reference for the busy data scientist. Understand text mining, machine learning, and network analysis; process numeric data with the NumPy and Pandas modules; describe and analyze data using statistical and network-theoretical methods; and see actual examples of data analysis at work. This one-stop solution covers the essential data science you need in Python.Data science is one of the fastest-growing disciplines in terms of academic research, student enrollment, and employment. Python, with its flexibility and scalability, is quickly overtaking the R language for data-scientific projects. Keep Python data-science concepts at your fingertips with this modular, quick reference to the tools used to acquire, clean, analyze, and store data.This one-stop solution covers essential Python, databases, network analysis, natural language processing, elements of machine learning, and visualization. Access structured and unstructured text and numeric data from local files, databases, and the Internet. Arrange, rearrange, and clean the data. Work with relational and non-relational databases, data visualization, and simple predictive analysis (regressions, clustering, and decision trees). See how typical data analysis problems are handled. And try your hand at your own solutions to a variety of medium-scale projects that are fun to work on and look good on your resume.Keep this handy quick guide at your side whether you''re a student, an entry-level data science professional converting from R to Python, or a seasoned Python developer who doesn''t want to memorize every function and option.What You Need:You need a decent distribution of Python 3.3 or above that includes at least NLTK, Pandas, NumPy, Matplotlib, Networkx, SciKit-Learn, and BeautifulSoup. A great distribution that meets the requirements is Anaconda, available for free from www.continuum.io. If you plan to set up your own database servers, you also need MySQL (www.mysql.com) and MongoDB (www.mongodb.com). Both packages are free and run on Windows, Linux, and Mac OS.

Go from messy, unstructured artifacts stored in SQL and NoSQL databases to a neat, well-organized dataset with this quick reference for the busy data scientist. Understand text mining, machine learning, and network analysis; process numeric data with the NumPy and Pandas modules; describe and analyze data using statistical and network-theoretical methods; and see actual examples of data analysis at work. This one-stop solution covers the essential data science you need in Python.Data science is one of the fastest-growing disciplines in terms of academic research, student enrollment, and employment. Python, with its flexibility and scalability, is quickly overtaking the R language for data-scientific projects. Keep Python data-science concepts at your fingertips with this modular, quick reference to the tools used to acquire, clean, analyze, and store data.This one-stop solution covers essential Python, databases, network analysis, natural language processing, elements of machine learning, and visualization. Access structured and unstructured text and numeric data from local files, databases, and the Internet. Arrange, rearrange, and clean the data. Work with relational and non-relational databases, data visualization, and simple predictive analysis (regressions, clustering, and decision trees). See how typical data analysis problems are handled. And try your hand at your own solutions to a variety of medium-scale projects that are fun to work on and look good on your resume.Keep this handy quick guide at your side whether you''re a student, an entry-level data science professional converting from R to Python, or a seasoned Python developer who doesn''t want to memorize every function and option.What You Need:You need a decent distribution of Python 3.3 or above that includes at least NLTK, Pandas, NumPy, Matplotlib, Networkx, SciKit-Learn, and BeautifulSoup. A great distribution that meets the requirements is Anaconda, available for free from www.continuum.io. If you plan to set up your own database servers, you also need MySQL (www.mysql.com) and MongoDB (www.mongodb.com). Both packages are free and run on Windows, Linux, and Mac OS.

Construct, analyze, and visualize networks with networkx, a Python language module. Network analysis is a powerful tool you can apply to a multitude of datasets and situations. Discover how to work with all kinds of networks, including social, product, temporal, spatial, and semantic networks. Convert almost any real-world data into a complex network--such as recommendations on co-using cosmetic products, muddy hedge fund connections, and online friendships. Analyze and visualize the network, and make business decisions based on your analysis. If you''re a curious Python programmer, a data scientist, or a CNA specialist interested in mechanizing mundane tasks, you''ll increase your productivity exponentially.

Complex network analysis used to be done by hand or with non-programmable network analysis tools, but not anymore! You can now automate and program these tasks in Python. Complex networks are collections of connected items, words, concepts, or people. By exploring their structure and individual elements, we can learn about their meaning, evolution, and resilience.

Starting with simple networks, convert real-life and synthetic network graphs into networkx data structures. Look at more sophisticated networks and learn more powerful machinery to handle centrality calculation, blockmodeling, and clique and community detection. Get familiar with presentation-quality network visualization tools, both programmable and interactive--such as Gephi, a CNA explorer. Adapt the patterns from the case studies to your problems. Explore big networks with NetworKit, a high-performance networkx substitute. Each part in the book gives you an overview of a class of networks, includes a practical study of networkx functions and techniques, and concludes with case studies from various fields, including social networking, anthropology, marketing, and sports analytics.

Combine your CNA and Python programming skills to become a better network analyst, a more accomplished data scientist, and a more versatile programmer.

What You Need:

You will need a Python 3.x installation with the following additional modules: Pandas (>=0.18), NumPy (>=1.10), matplotlib (>=1.5), networkx (>=1.11), python-louvain (>=0.5), NetworKit (>=3.6), and generalizesimilarity. We recommend using the Anaconda distribution that comes with all these modules, except for python-louvain, NetworKit, and generalizedsimilarity, and works on all major modern operating systems.

Construct, analyze, and visualize networks with networkx, a Python language module. Network analysis is a powerful tool you can apply to a multitude of datasets and situations. Discover how to work with all kinds of networks, including social, product, temporal, spatial, and semantic networks. Convert almost any real-world data into a complex network--such as recommendations on co-using cosmetic products, muddy hedge fund connections, and online friendships. Analyze and visualize the network, and make business decisions based on your analysis. If you''re a curious Python programmer, a data scientist, or a CNA specialist interested in mechanizing mundane tasks, you''ll increase your productivity exponentially.

Complex network analysis used to be done by hand or with non-programmable network analysis tools, but not anymore! You can now automate and program these tasks in Python. Complex networks are collections of connected items, words, concepts, or people. By exploring their structure and individual elements, we can learn about their meaning, evolution, and resilience.

Starting with simple networks, convert real-life and synthetic network graphs into networkx data structures. Look at more sophisticated networks and learn more powerful machinery to handle centrality calculation, blockmodeling, and clique and community detection. Get familiar with presentation-quality network visualization tools, both programmable and interactive--such as Gephi, a CNA explorer. Adapt the patterns from the case studies to your problems. Explore big networks with NetworKit, a high-performance networkx substitute. Each part in the book gives you an overview of a class of networks, includes a practical study of networkx functions and techniques, and concludes with case studies from various fields, including social networking, anthropology, marketing, and sports analytics.

Combine your CNA and Python programming skills to become a better network analyst, a more accomplished data scientist, and a more versatile programmer.

What You Need:

You will need a Python 3.x installation with the following additional modules: Pandas (>=0.18), NumPy (>=1.10), matplotlib (>=1.5), networkx (>=1.11), python-louvain (>=0.5), NetworKit (>=3.6), and generalizesimilarity. We recommend using the Anaconda distribution that comes with all these modules, except for python-louvain, NetworKit, and generalizedsimilarity, and works on all major modern operating systems.

Make your good Python code even better by following proven and effective pythonic programming tips. Avoid logical errors that usually go undetected by Python linters and code formatters, such as frequent data look-ups in long lists, improper use of local and global variables, and mishandled user input. Discover rare language features, like rational numbers, set comprehensions, counters, and pickling, that may boost your productivity. Discover how to apply general programming patterns, including caching, in your Python code. Become a better-than-average Python programmer, and develop self-documented, maintainable, easy-to-understand programs that are fast to run and hard to break.

Python is one of the most popular and rapidly growing modern programming languages. With more than 200 standard libraries and even more third-party libraries, it reaches into the software development areas as diverse as artificial intelligence, bioinformatics, natural language processing, and computer vision. Find out how to improve your understanding of the spirit of the language by using one hundred pythonic tips to make your code safer, faster, and better documented.

This programming style manual is a quick reference of helpful hints and a random source of inspiration. Choose the suitable data structures for searching and sorting jobs and become aware of how a wrong choice may cause your application to be completely ineffective. Understand global and local variables, class and instance attributes, and information-hiding techniques. Create functions with flexible interfaces. Manage intermediate computation results by caching them in files and memory to improve performance and reliability. Polish your documentation skills to make your code easy for other programmers to understand. As a bonus, discover Easter eggs cleverly planted in the standard library by its developers.

Polish, secure, and speed-up your Python applications, and make them easier to maintain by following pythonic programming tips.

What You Need:

You will need a Python interpreter (ideally, version 3.4 or above) and the standard Python library that usually comes with the interpreter.

Make your good Python code even better by following proven and effective pythonic programming tips. Avoid logical errors that usually go undetected by Python linters and code formatters, such as frequent data look-ups in long lists, improper use of local and global variables, and mishandled user input. Discover rare language features, like rational numbers, set comprehensions, counters, and pickling, that may boost your productivity. Discover how to apply general programming patterns, including caching, in your Python code. Become a better-than-average Python programmer, and develop self-documented, maintainable, easy-to-understand programs that are fast to run and hard to break.

Python is one of the most popular and rapidly growing modern programming languages. With more than 200 standard libraries and even more third-party libraries, it reaches into the software development areas as diverse as artificial intelligence, bioinformatics, natural language processing, and computer vision. Find out how to improve your understanding of the spirit of the language by using one hundred pythonic tips to make your code safer, faster, and better documented.

This programming style manual is a quick reference of helpful hints and a random source of inspiration. Choose the suitable data structures for searching and sorting jobs and become aware of how a wrong choice may cause your application to be completely ineffective. Understand global and local variables, class and instance attributes, and information-hiding techniques. Create functions with flexible interfaces. Manage intermediate computation results by caching them in files and memory to improve performance and reliability. Polish your documentation skills to make your code easy for other programmers to understand. As a bonus, discover Easter eggs cleverly planted in the standard library by its developers.

Polish, secure, and speed-up your Python applications, and make them easier to maintain by following pythonic programming tips.

What You Need:

You will need a Python interpreter (ideally, version 3.4 or above) and the standard Python library that usually comes with the interpreter.

Immerse yourself in the intricate world of forgotten programming languages with Seven Obscure Languages in Seven Weeks. This comprehensive guide serves as a bridge to understanding and revitalizing legacy code, offering invaluable insights into the evolution of programming. With hands-on tutorials spanning languages from Forth and Simula to SNOBOL and m4, readers are equipped to maintain older systems and gain a broader perspective on problem-solving techniques. Whether you are a seasoned developer, a software historian, or just curious about the roots of modern coding, this book illuminates the rich tapestry of programming''s past and sheds light on its present and future.

Venture into overlooked and long-forgotten programming languages that once stood at the forefront of technological innovation. From the stack-oriented design of Forth to the early object-oriented experiences in Simula, bridge the ever-widening chasm between contemporary code and legacy systems. If you find yourself ensnared by the challenges of updating or maintaining older systems, this book is the lifeline.

Unravel the fabric of seven programming languages by following practical tutorials and building small applications. Find out how Simula led to C++, what made APL so powerful, and why we still use m4 even to this day. Along the way, you''ll broaden your problem-solving horizons, and develop diverse approaches to computation that still ripple through today''s coding landscape. By the final chapter, you won''t merely possess historical knowledge, you''ll be equipped with production ready skills capable of tackling projects that interface with legacy code.

Trace the evolutionary lineage of programming to gain a predictive edge in anticipating future trends. After all, this isn''t just a nostalgic trip - it''s a roadmap to the past, present, and future of coding.

What You Need:

Various software tools and compilers are available for enthusiasts eager to explore the once-forgotten languages detailed in this book. Guidance is provided primarily for Linux users on accessing these older programming languages. This collection includes languages like m4, integral to the GNU Autoconf system, and other languages incorporated into the GNU ecosystem, such as APL, Forth, and Simula. For those with a penchant for nostalgia, there is the SNOBOL4.2, which can run using the DOSBox MS-DOS emulator. KRoC, an Occam compiler, works only with 32-bit architectures or in a docker. Suffolk University maintains Starset''s modern implementation. Readers can find links to repositories of these development tools, ensuring they can fully immerse themselves in this intriguing journey.

Immerse yourself in the intricate world of forgotten programming languages with Seven Obscure Languages in Seven Weeks. This comprehensive guide serves as a bridge to understanding and revitalizing legacy code, offering invaluable insights into the evolution of programming. With hands-on tutorials spanning languages from Forth and Simula to SNOBOL and m4, readers are equipped to maintain older systems and gain a broader perspective on problem-solving techniques. Whether you are a seasoned developer, a software historian, or just curious about the roots of modern coding, this book illuminates the rich tapestry of programming''s past and sheds light on its present and future.

Venture into overlooked and long-forgotten programming languages that once stood at the forefront of technological innovation. From the stack-oriented design of Forth to the early object-oriented experiences in Simula, bridge the ever-widening chasm between contemporary code and legacy systems. If you find yourself ensnared by the challenges of updating or maintaining older systems, this book is the lifeline.

Unravel the fabric of seven programming languages by following practical tutorials and building small applications. Find out how Simula led to C++, what made APL so powerful, and why we still use m4 even to this day. Along the way, you''ll broaden your problem-solving horizons, and develop diverse approaches to computation that still ripple through today''s coding landscape. By the final chapter, you won''t merely possess historical knowledge, you''ll be equipped with production ready skills capable of tackling projects that interface with legacy code.

Trace the evolutionary lineage of programming to gain a predictive edge in anticipating future trends. After all, this isn''t just a nostalgic trip - it''s a roadmap to the past, present, and future of coding.

What You Need:

Various software tools and compilers are available for enthusiasts eager to explore the once-forgotten languages detailed in this book. Guidance is provided primarily for Linux users on accessing these older programming languages. This collection includes languages like m4, integral to the GNU Autoconf system, and other languages incorporated into the GNU ecosystem, such as APL, Forth, and Simula. For those with a penchant for nostalgia, there is the SNOBOL4.2, which can run using the DOSBox MS-DOS emulator. KRoC, an Occam compiler, works only with 32-bit architectures or in a docker. Suffolk University maintains Starset''s modern implementation. Readers can find links to repositories of these development tools, ensuring they can fully immerse themselves in this intriguing journey.