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Python: Data Analytics and Visualization

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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

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Good Habits for Great Coding: Improving Programming Skills with Examples in Python
Good Habits for Great Coding: Improving Programming Skills with Examples in Python

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Taking an informal, conversational tone,...

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Key Features

  • Your ultimate resource for getting up and running with Python numerical computations
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  • A hands-on guide to implementing mathematics with Python, with complete...
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Leverage the capabilities of SAS to process and analyze Big Data

About This Book

  • Combine SAS with platforms such as Hadoop, SAP HANA, and Cloud Foundry-based platforms for effecient Big Data analytics
  • Learn how to use the web browser-based SAS Studio and iPython Jupyter Notebook...

Python Web Scraping: Hands-on data scraping and crawling using PyQT, Selnium, HTML and Python, 2nd Edition
Python Web Scraping: Hands-on data scraping and crawling using PyQT, Selnium, HTML and Python, 2nd Edition

Successfully scrape data from any website with the power of Python 3.x

Key Features

  • A hands-on guide to web scraping using Python with solutions to real-world problems
  • Create a number of different web scrapers in Python to extract information
  • This book...
Graphing Data with R: An Introduction
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It’s much easier to grasp complex data relationships with a graph than by scanning numbers in a spreadsheet. This introductory guide shows you how to use the R language to create a variety of useful graphs for visualizing and analyzing complex data for science, business, media, and many other fields. You’ll learn methods...

Penetration Testing Essentials
Penetration Testing Essentials
Your pen testing career begins here, with a solid foundation in essential skills and concepts

Penetration Testing Essentials provides a starting place for professionals and beginners looking to learn more about penetration testing for cybersecurity. Certification eligibility requires work experience—but before...

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