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An end-to-end guide to getting well-versed with data analysis pipelines using machine learning algorithms and techniquesKey Features
- Prepare and clean your data, and use it for exploratory analysis, data manipulation, and wrangling
- Cover various machine learning methods such as supervised, unsupervised, probabilistic, and bayesian
- Get to grips with graph processing and sentiment analysis using this practical guide
Data analysis generates value from small and big data by finding new patterns and trends. Python is one of the most popular and easy tools to analyze a variety of data. This book is a handy guide to get well versed with all the phases and methodologies used in the data analysis domain. You will be using modern libraries from the Python ecosystem to create efficient data pipelines.
To begin with, you will focus on the essential statistical overview and data analysis fundamentals using Python. You will perform effective and complex data analysis and modeling, data manipulation, data cleaning, data visualization and more using easy-to-follow examples. The book focuses on the practical, relevant fundamentals, in-depth and advanced description of Python data analysis. You will perform time-series analysis and signal processing using ARMA models. Later you will learn to bring smart processing and deep dive into data analytics using machine learning algorithms like regression, classification, PCA, clustering, and more. You will pick real-world examples to analyze textual and image data using NLP and image analytics techniques respectively. Later you will focus on parallel computing using Dask.
By the end of this book, you will learn to prepare data for analysis, and create meaningful data visualizations to forecast values from data.What you will learn
- Learn about data science and its various process model
- Perform data manipulations such as aggregating, cleaning, and handling missing values using NumPy and Pandas
- Create interactive visualizations using matplotlib, seaborn and bokeh
- Retrieve, process and store data in various formats
- Learn data preprocessing and feature engineering using pandas and scikit-learn
- Perform time-series and signal processing examples using sunspot cycles data
- Analyze textual data and image data to perform advanced analysis
- Learn about parallel computing using Dask.
This book is for data analysts, business analysts, statisticians, and data scientists who would like to learn and perform end-to-end data analysis using Python. Also, students, academic trainers, and faculty can refer this book to learn and teach with a classic hands-on approach for Python data analysis. This will work as a comprehensive guide to get well versed with various data science methodologies. Some basics of math and working knowledge of Python programming language is expected.
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About the Author
Avinash Navlani has over 7 years of experience working in data science and AI. Currently, he is working as Sr. 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 Lecturer at university level, 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 Chief Data Scientist and Director at startups and large enterprises. He has been advising high-tech AI-based startups. Armando has authored books titled Python Data Analysis - Second Edition and Mastering TensorFlow. 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. You can find more information and a blog with a few NumPy examples at ivanidris.net.