Python Data Analysis

Perform data collection, data processing, wrangling, visualization, and model building using Python

; Armando Fandango ; Ivan Idris

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 trends, and Python is one of the most popular tools for analyzing a wide variety of data. Les mer
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419,-

(Paperback) Fri frakt!
Leveringstid: Sendes innen 21 dager

Paperback
Legg i
Paperback
Legg i
Vår pris: 419,-

(Paperback) Fri frakt!
Leveringstid: Sendes innen 21 dager

Om boka

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 trends, 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 and methodologies 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. In the concluding chapters, you'll 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

Explore data science and its various process models
Perform data manipulation using NumPy and pandas for aggregating, cleaning, and handling missing values
Create interactive visualizations using Matplotlib, Seaborn, and Bokeh
Retrieve, process, and store data in a wide range of formats
Understand data preprocessing and feature engineering using pandas and scikit-learn
Perform time series analysis and signal processing using sunspot cycle data
Analyze textual data and image data to perform advanced analysis
Get up to speed with parallel computing using Dask

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.

Fakta

Innholdsfortegnelse

Table of Contents

Getting Started with Python Libraries
NumPy and Pandas
Statistics
Linear Algebra
Data Visualization
Retrieving, Processing, and Storing Data
Cleaning Messy Data
Signal Processing and Time Series
Supervised Learning - Regression Analysis
Supervised Learning - Classification Techniques
Unsupervised Learning - PCA and Clustering
Analyzing Textual Data
Analyzing Image Data
Parallel Computing using Dask

Om forfatteren

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.

Armando Fandango creates AI-empowered products by leveraging his expertise in deep learning, machine learning, distributed computing, and computational methods and has provided thought leadership roles as the chief data scientist and director at start-ups and large enterprises. He has advised high-tech AI-based start-ups. Armando has authored books such as Python Data Analysis - Second Edition and Mastering TensorFlow, Packt Publishing. He has also published research in international journals and conferences.
Ivan Idris has an MSc in experimental physics. His graduation thesis had a strong emphasis on applied computer science. After graduating, he worked for several companies as a Java developer, data warehouse developer, and QA analyst. His main professional interests are business intelligence, big data, and cloud computing. Ivan Idris enjoys writing clean, testable code and interesting technical articles. Ivan Idris is the author of NumPy 1.5. Beginner's Guide and NumPy Cookbook by Packt Publishing.