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Python: Data Analytics and Visualization

More Information
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
About

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
Features
  • 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
Page Count 866
Course Length 25 hours 58 minutes
ISBN9781788290098
Date Of Publication 30 Mar 2017

Authors

Kirthi Raman

Kirthi Raman is currently working as a lead data engineer with Neustar Inc, based in Mclean, Virginia USA. Kirthi has worked on data visualization, with a focus on JavaScript, Python, R, and Java, and is a distinguished engineer. Previously, he worked as a principle architect, data analyst, and information retrieval specialist at Quotient, Inc. Kirthi has also worked as a technical lead and manager for a start-up.

He has taught discrete mathematics and computer science for several years. Kirthi has a graduate degree in mathematics and computer science from IIT Delhi and an MS in computer science from the University of Maryland. He has written several white papers on data analysis and big data.

Phuong Vo.T.H

Phuong Vo.T.H has a MSc degree in computer science, which is related to machine learning. After graduation, she continued to work in some companies as a data scientist. She has experience in analyzing users' behavior and building recommendation systems based on users' web histories. She loves to read machine learning and mathematics algorithm books, as well as data analysis articles.

Martin Czygan

Martin Czygan studied German literature and computer science in Leipzig, Germany. He has been working as a software engineer for more than 10 years. For the past eight years, he has been diving into Python, and is still enjoying it. In recent years, he has been helping clients to build data processing pipelines and search and analytics systems.

Ashish Kumar

Ashish Kumar is an IIM alumnus and an engineer at heart. He has extensive experience in data science, machine learning, and natural language processing having worked at organizations, such as McAfee-Intel, an ambitious data science startup Volt consulting), and presently associated to the software and research lab of a leading MNC. Apart from work, Ashish also participates in data science competitions at Kaggle in his spare time.