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| P R.E S E N T S . |
| / . |
| Python: Data Analytics And Visualization |
| |
| |
| |
| DATE: 2018-07-24 SIZE: 47.91 MB DISKS: 6x 10MB PAGES: n/a |
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| PUBLISHER: n/a GENRE: |
| |
| AUTHOR: Phuong Vo.T.H, Martin Czygan, Ashish Kumar |
| |
| FORMAT: EPUB PROTECTION: DRM EDITION: n/a |
| |
| URL: https://is.gd/mXqPPr |
| |
| LANGUAGE: English ISBN: 9781788290098 |
: :
: 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 generat :
: 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 analyze :
: 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 financia :
: models using Monte-Carlo simulations - Create directed graphs and multi- :
: graphs - Advanced visualization with D3 In Detail You will start the cours :
: 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 t :
: solve data-processing problems. Moving on, you will get a brief overview o :
: 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 dat :
: 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, Decisio :
: 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 pat :
: 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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