More Information
Learn
  • Install and setup Python
  • Implement objects in Python by creating classes and defining methods
  • Get acquainted with NumPy to use it with arrays and array-oriented computing in data analysis
  • Create effective visualizations for presenting your data using Matplotlib
  • Process and analyze data using the time series capabilities of pandas
  • Interact with different kind of database systems, such as file, disk format, Mongo, and Redis
  • Apply data mining concepts to real-world problems
  • Compute on big data, including real-time data from the Internet
  • Explore how to use different machine learning models to ask different questions of your data
About

The Python: Real-World Data Science course will take you on a journey to become an efficient data science practitioner by thoroughly understanding the key concepts of Python. This learning path is divided into four modules and each module are a mini course in their own right, and as you complete each one, you’ll have gained key skills and be ready for the material in the next module.

The course begins with getting your Python fundamentals nailed down. After getting familiar with Python core concepts, it’s time that you dive into the field of data science. In the second module, you'll learn how to perform data analysis using Python in a practical and example-driven way. The third module will teach you how to design and develop data mining applications using a variety of datasets, starting with basic classification and affinity analysis to more complex data types including text, images, and graphs. Machine learning and predictive analytics have become the most important approaches to uncover data gold mines. In the final module, we'll discuss the necessary details regarding machine learning concepts, offering intuitive yet informative explanations on how machine learning algorithms work, how to use them, and most importantly, how to avoid the common pitfalls.

Features
  • Unleash the power of Python 3 objects
  • Learn to use powerful Python libraries for effective data processing and analysis
  • Harness the power of Python to analyze data and create insightful predictive models
  • Unlock deeper insights into machine learning with this vital guide to cutting-edge predictive analytics
Page Count 1255
Course Length 37 hours 39 minutes
ISBN 9781786465160
Date Of Publication 9 Jun 2016

Authors

Dusty Phillips

Dusty Phillips is a Canadian software developer and an author currently living in New Brunswick. He has been active in the open-source community for 2 decades and has been programming in Python for nearly as long. He holds a master's degree in computer science and has worked for Facebook, the United Nations, and several startups.

Fabrizio Romano

Fabrizio Romano holds a master's degree in computer science engineering from the University of Padova. He is also a certified scrum master, Reiki master and teacher, and a member of CNHC. He moved to London in 2011 to work for companies, such as Glasses Direct and TBG/Sprinklr. He now works at Sohonet as a principal engineer/team lead. He has given talks on Teaching Python and TDD at two editions of EuroPython, and at Skillsmatter and ProgSCon in London.

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.

Robert Layton

Robert Layton is a data scientist investigating data-driven applications to businesses across a number of sectors. He received a PhD investigating cybercrime analytics from the Internet Commerce Security Laboratory at Federation University Australia, before moving into industry, starting his own data analytics company dataPipeline. Next, he created Eureaktive, which works with tech-based startups on developing their proof-of-concepts and early-stage prototypes. Robert also runs the LearningTensorFlow website, which is one of the world's premier tutorial websites for Google's TensorFlow library.

Robert is an active member of the Python community, having used Python for more than 8 years. He has presented at PyConAU for the last four years and works with Python Charmers to provide Python-based training for businesses and professionals from a wide range of organisations.

Robert can be best reached via Twitter @robertlayton

Sebastian Raschka

Sebastian Raschka is an Assistant Professor of Statistics at the University of Wisconsin-Madison focusing on machine learning and deep learning research. Some of his recent research methods have been applied to solving problems in the field of biometrics for imparting privacy to face images. Other research focus areas include the development of methods related to model evaluation in machine learning, deep learning for ordinal targets, and applications of machine learning to computational biology.