Understand data analysis pipelines using machine learning algorithms and techniques with this practical guide Key Features Prepare and clean your data to use it for exploratory analysis, data manipulation, and data wrangling Discover supervised, unsupervised, probabilistic, and Bayesian machine learning methods Get to grips with graph processing and sentiment analysis Book DescriptionData analysis enables you to generate value from small and big data by discovering new patterns, and Python is one of the most popular tools for analyzing a wide variety of data. With this book, you'll get up and running using Python for data analysis by exploring the different phases used in data analysis and learning how to use modern libraries from the Python ecosystem to create efficient data pipelines. Starting with the essential statistical and data analysis fundamentals using Python, you'll perform complex data analysis and modeling, data manipulation, data cleaning, and data visualization using easy-to-follow examples. You'll then understand how to conduct time series analysis and signal processing using ARMA models. As you advance, you'll get to grips with smart processing and data analytics using machine learning algorithms such as regression, classification, Principal Component Analysis (PCA), and clustering. You'll also work on real-world examples to analyze textual and image data using natural language processing (NLP) and image analytics techniques, respectively. Finally, the book will demonstrate parallel computing using Dask. By the end of this data analysis book, you'll be equipped with the skills you need to prepare data for analysis and create meaningful data visualizations for forecasting values from data.What you will learn Prepare, clean, and transform your data for exploratory analysis, manipulation, and wrangling. Explore concepts in signal processing, time series analysis, and predictive analytics. Understand and apply key machine learning techniques, including supervised, unsupervised, probabilistic, and Bayesian methods. Work with graph data and perform sentiment analysis. Handle large-scale image and text analytics efficiently. Accelerate data manipulation using Dask, Modin, and Ray. Perform scalable big data analytics with PySpark. Who this book is forThis book is for data analysts, business analysts, statisticians, and data scientists looking to learn how to use Python for data analysis. Students and academic faculties will also find this book useful for learning and teaching Python data analysis using a hands-on approach. A basic understanding of math and working knowledge of the Python programming language will help you get started with this book.

About the Author
Avinash Navlani has over 8 years of experience working in data science and AI. Currently, he is working as a senior data scientist, improving products and services for customers by using advanced analytics, deploying big data analytical tools, creating and maintaining models, and onboarding compelling new datasets. Previously, he was a university lecturer, where he trained and educated people in data science subjects such as Python for analytics, data mining, machine learning, database management, and NoSQL. Avinash has been involved in research activities in data science and has been a keynote speaker at many conferences in India. Cornellius is a leader in data science and an entrepreneur with over six years of experience delivering AI solutions. As a startup's co-founder and data lead focused on an AI-powered data platform, he drives scalable AI strategies, transforming raw data into actionable insights. Previously, he was a Senior Data Scientist and Assistant Manager in the finance industry, leading end-to-end projects-from problem framing to model deployment-to address business challenges and deliver value through predictive analytics. In addition to his startup, Cornellius provides consulting services for early-stage companies, assisting them in creating customized machine-learning systems. He is a prolific technical writer who shares his knowledge through articles, LinkedIn posts, and a well-regarded newsletter on data trends.


Book Information
ISBN 9781806022878
Author Avinash Navlani
Format Paperback
Imprint Packt Publishing Limited
Publisher Packt Publishing Limited