Data's value has grown exponentially in the past decade, with 'Big Data' today being one of the biggest buzzwords in business and IT, and data scientist hailed as 'the sexiest job of the 21st century'. Practical Data Science Cookbook helps you see beyond the hype and get past the theory by providing you with a hands-on exploration of data science. With a comprehensive range of ...(展开全部)

Prabhanjan Tattar has 9 years of experience as a statistical analyst. His main thurst has been to explain statistical and machine learning techniques through elegant programming which will clear the nuances of the underlying mathematics. Survival analysis and statistical inference are his main areas of research/interest, and he has published several research papers in peer-revi...(展开全部)

Prabhanjan Tattar has 9 years of experience as a statistical analyst. His main thurst has been to explain statistical and machine learning techniques through elegant programming which will clear the nuances of the underlying mathematics. Survival analysis and statistical inference are his main areas of research/interest, and he has published several research papers in peer-revi...(展开全部)

This book is for Java developers who are familiar with the fundamentals of data science and want to improve their skills to become a pro.

This book is for Java developers who are familiar with the fundamentals of data science and want to improve their skills to become a pro.

A concise introduction to the emerging field of data science, explaining its evolution, relation to machine learning, current uses, data infrastructure issues, and ethical challenges.

Data Science Projects with Python is designed to give you practical guidance on industry-standard data analysis and machine learning tools in Python, with the help of realistic data. The book will help you understand how you can use pandas and Matplotlib to critically examine a dataset with summary statistics and graphs, and extract the insights you seek to derive. You will continue to build on your knowledge as you learn how to prepare data and feed it to machine learning algorithms, such as regularized logistic regression and random forest, using the scikit-learn package. You’ll discover how to tune the algorithms to provide the best predictions on new and, unseen data. As you delve into later chapters, you’ll be able to understand the working and output of these algorithms and gain insight into not only the predictive capabilities of the models but also their reasons for making these predictions. By the end of this book, you will have the skills you need to confidently use various machine learning algorithms to perform detailed data analysis and extract meaningful insights from unstructured data.

Data Science Projects with Python is designed to give you practical guidance on industry-standard data analysis and machine learning tools in Python, with the help of realistic data. The book will help you understand how you can use pandas and Matplotlib to critically examine a dataset with summary statistics and graphs, and extract the insights you seek to derive. You will continue to build on your knowledge as you learn how to prepare data and feed it to machine learning algorithms, such as regularized logistic regression and random forest, using the scikit-learn package. You’ll discover how to tune the algorithms to provide the best predictions on new and, unseen data. As you delve into later chapters, you’ll be able to understand the working and output of these algorithms and gain insight into not only the predictive capabilities of the models but also their reasons for making these predictions. By the end of this book, you will have the skills you need to confidently use various machine learning algorithms to perform detailed data analysis and extract meaningful insights from unstructured data.

This book is for those who are already familiar with the basic operation of R, but want to learn how to efficiently and effectively analyze real-world data problems using practical R packages.

Simply put, data science is the discipline of extracting meaning from data. More and more business analysts are called to work as data scientists and while it can involve deep knowledge of statistics, mathematics, machine learning, and computer science; for most non-academics, data science looks like applying analysis techniques to answer key business questions. Sophisticated s...(展开全部)