Python Data Science Essentials - Learn the fundamentals of Data Science with Python

Key Features* Quickly get familiar with data science using Python * Save time - and effort - with all the essential tools explained * Create effective data science projects and avoid common pitfalls with the help of examples and hints dictated by experience Book DescriptionThe book starts by introducing you to setting up your essential data science toolbox. Then it will guide you across all the data munging and preprocessing phases. This will be done in a manner that explains all the core data science activities related to loading data, transforming and fixing it for analysis, as well as exploring and processing it. Finally, it will complete the overview by presenting you with the main machine learning algorithms, the graph analysis technicalities, and all the visualization instruments that can make your life easier in presenting your results. In this walkthrough, structured as a data science project, you will always be accompanied by clear code and simplified examples to help you understand the underlying mechanics and real-world datasets. What you will learn* Set up your data science toolbox using a Python scientific environment on Windows, Mac, and Linux * Get data ready for your data science project * Manipulate, fix, and explore data in order to solve data science problems * Set up an experimental pipeline to test your data science hypothesis * Choose the most effective and scalable learning algorithm for your data science tasks * Optimize your machine learning models to get the best performance * Explore and cluster graphs, taking advantage of interconnections and links in your data About the Authors**Alberto Boschetti** is a data scientist with expertise in signal processing and statistics. He holds a PhD in telecommunication engineering and currently lives and works in London. In his work projects, he faces challenges involving natural language processing (NLP), machine learning, and probabilistic graph models everyday. **Luca Massaron** is a data scientist and marketing research director who specializes in multivariate statistical analysis, machine learning, and customer insight, with over a decade of experience in solving real-world problems and generating value for stakeholders by applying reasoning, statistics, data mining, and algorithms. Table of Contents1. First Steps 2. Data Munging 3. The Data Science Pipeline 4. Machine Learning 5. Social Network Analysis 6. Visualization Key Features Quickly get familiar with data science using Python Save time - and effort - with all the essential tools explained Create effective data science projects and avoid common pitfalls with the help of examples and hints dictated by experience Book Description The book starts by introducing you to setting up your essential data science toolbox. Then it will guide you across all the data munging and preprocessing phases. This will be done in a manner that explains all the core data science activities related to loading data, transforming and fixing it for analysis, as well as exploring and processing it. Finally, it will complete the overview by presenting you with the main machine learning algorithms, the graph analysis technicalities, and all the visualization instruments that can make your life easier in presenting your results. In this walkthrough, structured as a data science project, you will always be accompanied by clear code and simplified examples to help you understand the underlying mechanics and real-world datasets. What you will learn Set up your data science toolbox using a Python scientific environment on Windows, Mac, and Linux Get data ready for your data science project Manipulate, fix, and explore data in order to solve data science problems Set up an experimental pipeline to test your data science hypothesis Choose the most effective and scalable learning algorithm for your data science tasks Optimize your machine learning models to get the best performance Explore and cluster graphs, taking advantage of interconnections and links in your data About the Authors Alberto Boschetti is a data scientist with expertise in signal processing and statistics. He holds a PhD in telecommunication engineering and currently lives and works in London. In his work projects, he faces challenges involving natural language processing (NLP), machine learning, and probabilistic graph models everyday. Luca Massaron is a data scientist and marketing research director who specializes in multivariate statistical analysis, machine learning, and customer insight, with over a decade of experience in solving real-world problems and generating value for stakeholders by applying reasoning, statistics, data mining, and algorithms. Table of Contents First Steps Data Munging The Data Science Pipeline Machine Learning Social Network Analysis Visualization Become an efficient data science practitioner by thoroughly understanding the key concepts of PythonKey FeaturesQuickly get familiar with data science using PythonSave tons of time through this reference book with all the essential tools illustrated and explainedCreate effective data science projects and avoid common pitfalls with the help of examples and hints dictated by experienceBook DescriptionThe book starts by introducing you to setting up your essential data science toolbox. Then it will guide you across all the data munging and preprocessing phases. This will be done in a manner that explains all the core data science activities related to loading data, transforming and fixing it for analysis, as well as exploring and processing it. Finally, it will complete the overview by presenting you with the main machine learning algorithms, the graph analysis technicalities, and all the visualization instruments that can make your life easier in presenting your results. In this walkthrough, structured as a data science project, you will always be accompanied by clear code and simplified examples to help you understand the underlying mechanics and real-world datasets.What you will learnSet up your data science toolbox using a Python scientific environment on Windows, Mac, and LinuxGet data ready for your data science projectManipulate, fix, and explore data in order to solve data science problemsSet up an experimental pipeline to test your data science hypothesisChoose the most effective and scalable learning algorithm for your data science tasksOptimize your machine learning models to get the best performanceExplore and cluster graphs, taking advantage of interconnections and links in your dataWho this book is forIf you are an aspiring data scientist and you have at least a working knowledge of data analysis and Python, this book will get you started in data science. Data analysts with experience of R or MATLAB will also find the book to be a comprehensive reference to enhance their data manipulation and machine learning skills.