More Information
Learn
  • Delve into data science and its various process models
  • Perform data manipulation 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 a wide range of formats
  • Understand data preprocessing and feature engineering using pandas and scikit-learn
  • Perform time series 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
About

Data analysis generates value from small and big data by finding new patterns and trends. Python is one of the most popular tools for analyzing a wide variety of data. This book will help you get up and running with the different phases and methodologies used in data analysis, and will show you how to use modern libraries from the Python ecosystem to create efficient data pipelines.

To begin with, you will focus on the essential statistical and data analysis fundamentals using Python. You’ll then perform effective and complex data analysis and modeling, data manipulation, data cleaning, and data visualization using easy-to-follow examples. Next, you’ll understand how to conduct time-series analysis and signal processing using ARMA models. Later, you’ll delve into smart processing and data analytics using machine learning algorithms such as regression, classification, Principal Component Analysis (PCA), and clustering. Toward the concluding chapters, you’ll pick real-world examples to analyze textual and image data using natural language processing (NLP) and image analytics techniques respectively. Finally, you will focus on 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.

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
Page Count 121
Course Length 3 hours 37 minutes
ISBN 9781789955248
Date Of Publication 21 Aug 2020

Authors

Avinash Navlani

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

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

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.