Essentials Of Data Science
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Python Data Science Essentials
Author: Alberto Boschetti
language: en
Publisher: Packt Publishing Ltd
Release Date: 2018-09-28
Gain useful insights from your data using popular data science tools Key FeaturesA one-stop guide to Python libraries such as pandas and NumPyComprehensive coverage of data science operations such as data cleaning and data manipulationChoose scalable learning algorithms for your data science tasksBook Description Fully expanded and upgraded, the latest edition of Python Data Science Essentials will help you succeed in data science operations using the most common Python libraries. This book offers up-to-date insight into the core of Python, including the latest versions of the Jupyter Notebook, NumPy, pandas, and scikit-learn. The book covers detailed examples and large hybrid datasets to help you grasp essential statistical techniques for data collection, data munging and analysis, visualization, and reporting activities. You will also gain an understanding of advanced data science topics such as machine learning algorithms, distributed computing, tuning predictive models, and natural language processing. Furthermore, You’ll also be introduced to deep learning and gradient boosting solutions such as XGBoost, LightGBM, and CatBoost. By the end of the book, you will have gained a complete overview of the principal machine learning algorithms, graph analysis techniques, and all the visualization and deployment instruments that make it easier to present your results to an audience of both data science experts and business users What you will learnSet up your data science toolbox on Windows, Mac, and LinuxUse the core machine learning methods offered by the scikit-learn libraryManipulate, fix, and explore data to solve data science problemsLearn advanced explorative and manipulative techniques to solve data operationsOptimize your machine learning models for optimized performanceExplore and cluster graphs, taking advantage of interconnections and links in your dataWho this book is for If you’re a data science entrant, data analyst, or data engineer, this book will help you get ready to tackle real-world data science problems without wasting any time. Basic knowledge of probability/statistics and Python coding experience will assist you in understanding the concepts covered in this book.
45 Essential Concepts in Data Science in 7 Minutes Each
### Book Description: '45 Essential Concepts in Data Science in 7 Minutes Each' Unlock the world of data science with *45 Essential Concepts in Data Science in 7 Minutes Each*, an engaging and insightful guide designed for professionals and enthusiasts alike. This book offers succinct overviews of key topics that shape the data science landscape, allowing readers to grasp fundamental concepts quickly and effectively. #### Why This Book? In today's fast-paced environment, understanding data science is essential, but time is often limited. Each chapter is crafted to be read in just seven minutes, making it the perfect resource for busy individuals seeking to enhance their knowledge without the overwhelm of lengthy texts. From foundational theories to cutting-edge techniques, this book covers a comprehensive range of topics that are pivotal for success in the data-driven world. #### What You'll Learn: - **Introduction to Data Science**: Discover the basics and the significance of data science in various industries. - **Data Types and Data Structures**: Understand how data is categorized and organized. - **Data Collection Methods**: Explore the different ways to gather data effectively. - **Data Cleaning and Preprocessing**: Learn the essential steps to prepare data for analysis. - **Exploratory Data Analysis (EDA)**: Uncover patterns and insights through initial investigations. - **Descriptive Statistics**: Get to know the tools for summarizing and describing data sets. - **Data Visualization Techniques**: Master the art of making data accessible and interpretable through visuals. - **Probability Basics & Statistical Inference**: Dive into the concepts that form the backbone of data analysis. - **Hypothesis Testing & Confidence Intervals**: Develop your skills in making data-driven decisions. - **Regression Analysis, Classification Techniques, and Clustering Methods**: Familiarize yourself with essential modeling techniques. - **Machine Learning Algorithms**: Gain insight into Decision Trees, Support Vector Machines, Neural Networks, and more. - **Natural Language Processing (NLP)**: Explore the intersection of language and data. - **Feature Engineering & Dimensionality Reduction**: Learn to enhance and streamline your datasets. - **Model Evaluation Metrics, Overfitting and Underfitting**: Understand how to measure the success of your models. - **Big Data Technologies & Distributed Computing**: Navigate the complexities of modern data environments. - **Data Ethics and Privacy**: Reflect on the moral responsibilities of data scientists in today's world. - **Deployment of Data Science Models & Data Pipelines**: Understand how to implement models in real-world settings. - **Cloud Computing in Data Science & Working with APIs**: Learn how the cloud empowers data science infrastructure. - **Data Governance & Interpretation**: Explore frameworks for maintaining data integrity and conveying results effectively. - **Industry Applications and Future Trends**: Gain insights into how data science is transforming various sectors. Each chapter concisely introduces you to the topic, breaking down complex concepts into digestible insights. Whether you are a student, a working professional, or just someone curious about the data science field, this book equips you with the essential knowledge to thrive in a data-centric world. ### Get ready to embark on your data journey and transform your understanding of data science in just seven minutes at a time!
Essentials of Data Science and Analytics
This book combines the key concepts of data science and analytics to help you gain a practical understanding of these fields.