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

Mastering Python Data Visualization provides thorough, hands-on guidance for creating impactful visual representations of data by leveraging Python's powerful libraries such as Matplotlib, Pandas, and Scikit-Learn. By following this book, you will gain proficiency in understanding data, performing analyses, and ultimately presenting your findings in a clear and engaging way. What this Book will help me do Effectively transform raw data into insightful visualizations using Python's rich ecosystem of libraries. Understand and apply best practices for selecting the most appropriate visualization techniques for different datasets and objectives. Master the use of Python for interactive plotting, regression analysis, clustering, and classification tasks. Develop a solid foundation in data visualization aesthetics and how to convey information clearly through visuals. Utilize Python for specialized fields such as finance, bioinformatics, and social network analysis, incorporating advanced computation techniques. Author(s) Kirthi Raman is an experienced data scientist and Python advocate with a strong background in technical computing and data visualization. He has hands-on experience in using Python's ecosystem to solve real-world data problems and a passion for sharing knowledge. Raman's writing focuses on blending practical insights with comprehensive explanations, ensuring readers not only learn the tools but also apply them effectively. Who is it for? This book is ideal for data analysts, data scientists, and researchers who want to deepen their knowledge of Python-based data visualization techniques. It requires readers to have a basic understanding of Python and data manipulation. If your goal is to create professional and informative visual narratives that are both visually appealing and data-driven, this book is for you.