The Data Science Workshop
A New, Interactive Approach to Learning Data Science
Published on 29. January 2020
818 pages
978-1-83898-308-6 (ISBN)
Description
Cut through the noise and get real results with a step-by-step approach to data science
Key Features
Ideal for the data science beginner who is getting started for the first time
A data science tutorial with step-by-step exercises and activities that help build key skills
Structured to let you progress at your own pace, on your own terms
Use your physical print copy to redeem free access to the online interactive edition
Book DescriptionYou already know you want to learn data science, and a smarter way to learn data science is to learn by doing. The Data Science Workshop focuses on building up your practical skills so that you can understand how to develop simple machine learning models in Python or even build an advanced model for detecting potential bank frauds with effective modern data science. You'll learn from real examples that lead to real results. Throughout The Data Science Workshop, you'll take an engaging step-by-step approach to understanding data science. You won't have to sit through any unnecessary theory. If you're short on time you can jump into a single exercise each day or spend an entire weekend training a model using sci-kit learn. It's your choice. Learning on your terms, you'll build up and reinforce key skills in a way that feels rewarding. Every physical print copy of The Data Science Workshop unlocks access to the interactive edition. With videos detailing all exercises and activities, you'll always have a guided solution. You can also benchmark yourself against assessments, track progress, and receive content updates. You'll even earn a secure credential that you can share and verify online upon completion. It's a premium learning experience that's included with your printed copy. To redeem, follow the instructions located at the start of your data science book. Fast-paced and direct, The Data Science Workshop is the ideal companion for data science beginners. You'll learn about machine learning algorithms like a data scientist, learning along the way. This process means that you'll find that your new skills stick, embedded as best practice. A solid foundation for the years ahead.What you will learn
Find out the key differences between supervised and unsupervised learning
Manipulate and analyze data using scikit-learn and pandas libraries
Learn about different algorithms such as regression, classification, and clustering
Discover advanced techniques to improve model ensembling and accuracy
Speed up the process of creating new features with automated feature tool
Simplify machine learning using open source Python packages
Who this book is forOur goal at Packt is to help you be successful, in whatever it is you choose to do. The Data Science Workshop is an ideal data science tutorial for the data science beginner who is just getting started. Pick up a Workshop today and let Packt help you develop skills that stick with you for life.
Key Features
Ideal for the data science beginner who is getting started for the first time
A data science tutorial with step-by-step exercises and activities that help build key skills
Structured to let you progress at your own pace, on your own terms
Use your physical print copy to redeem free access to the online interactive edition
Book DescriptionYou already know you want to learn data science, and a smarter way to learn data science is to learn by doing. The Data Science Workshop focuses on building up your practical skills so that you can understand how to develop simple machine learning models in Python or even build an advanced model for detecting potential bank frauds with effective modern data science. You'll learn from real examples that lead to real results. Throughout The Data Science Workshop, you'll take an engaging step-by-step approach to understanding data science. You won't have to sit through any unnecessary theory. If you're short on time you can jump into a single exercise each day or spend an entire weekend training a model using sci-kit learn. It's your choice. Learning on your terms, you'll build up and reinforce key skills in a way that feels rewarding. Every physical print copy of The Data Science Workshop unlocks access to the interactive edition. With videos detailing all exercises and activities, you'll always have a guided solution. You can also benchmark yourself against assessments, track progress, and receive content updates. You'll even earn a secure credential that you can share and verify online upon completion. It's a premium learning experience that's included with your printed copy. To redeem, follow the instructions located at the start of your data science book. Fast-paced and direct, The Data Science Workshop is the ideal companion for data science beginners. You'll learn about machine learning algorithms like a data scientist, learning along the way. This process means that you'll find that your new skills stick, embedded as best practice. A solid foundation for the years ahead.What you will learn
Find out the key differences between supervised and unsupervised learning
Manipulate and analyze data using scikit-learn and pandas libraries
Learn about different algorithms such as regression, classification, and clustering
Discover advanced techniques to improve model ensembling and accuracy
Speed up the process of creating new features with automated feature tool
Simplify machine learning using open source Python packages
Who this book is forOur goal at Packt is to help you be successful, in whatever it is you choose to do. The Data Science Workshop is an ideal data science tutorial for the data science beginner who is just getting started. Pick up a Workshop today and let Packt help you develop skills that stick with you for life.
More details
Edition
Digital original
Language
English
Place of publication
Birmingham
United Kingdom
Target group
College/higher education
US School Grade: College Graduate Student
Edition type
Digital original
File size
46,38 MB
ISBN-13
978-1-83898-308-6 (9781838983086)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
New editions
Anthony So So | Thomas Joseph | Robert Thas John
The Data Science Workshop
Learn how you can build machine learning models and create your own real-world data science projects
E-Book
08/2020
2nd Edition
Packt Publishing
€25.49
Available for download
Additional editions
Anthony So So | Thomas Joseph | Robert Thas John
The Data Science Workshop
A New, Interactive Approach to Learning Data Science
Book
01/2020
Packt Publishing
€55.20
Shipment within 3-4 weeks
Persons
Anthony So is a renowned leader in data science. He has extensive experience in solving complex business problems using advanced analytics and AI in different industries including financial services, media, and telecommunications. He is currently the chief data officer of one of the most innovative fintech start-ups. He is also the author of several best-selling books on data science, machine learning, and deep learning. He has won multiple prizes at several hackathon competitions, such as Unearthed, GovHack, and Pepper Money. Anthony holds two master's degrees, one in computer science and the other in data science and innovation. Thomas V. Joseph is a data science practitioner, researcher, trainer, mentor, and writer with more than 19 years of experience. He has extensive experience in solving business problems using machine learning toolsets across multiple industry segments. Robert Thas John is a data engineer with a career that spans two decades. He manages a team of data engineers, analysts, and machine learning engineers - roles that he has held in the past. He leads a number of efforts aimed at increasing the adoption of machine learning on embedded devices through various programs from Google Developers and ARM Ltd, which licenses the chips found in Arduinos and other microcontrollers.
He started his career as a software engineer with work that has spanned various industries. His first experience with embedded systems was in programming payment terminals. Andrew David Worsley is an independent consultant and educator with expertise in the areas of machine learning, statistics, cloud computing, and artificial intelligence. He has practiced data science in several countries across a multitude of industries including retail, financial services, marketing, resources, and healthcare. Dr. Samuel Asare is a professional engineer with enthusiasm for Python programming, research, and writing. He is highly skilled in applying data science methods to the extraction of useful insights from large data sets. He possesses solid skills in project management processes. Samuel has previously held positions, in industry and academia, as a process engineer and a lecturer of materials science and engineering respectively. Presently, he is pursuing his passion for solving industry problems, using data science methods, and writing. https://www.linkedin.com/in/ivanliu1989/
Programming Languages: Python, R, Google Cloud Contacted by Melwyn Dsouza on 14/05/22018
Contacted for HTML5 and CSS3 on July 22, 2019 by Sneha Shinde
https://www.udemy.com/making-ionic-mobile-apps-with-ionic-creator/
Barbora Stetinova works in an Automotive industry earned experience in data science and machine learning, leading small team, leading strategical projects and in controlling topics for 13 years. Since Sept 2018 she is a member of IT department participating on the Data science implementation in an automotive company.
He started his career as a software engineer with work that has spanned various industries. His first experience with embedded systems was in programming payment terminals. Andrew David Worsley is an independent consultant and educator with expertise in the areas of machine learning, statistics, cloud computing, and artificial intelligence. He has practiced data science in several countries across a multitude of industries including retail, financial services, marketing, resources, and healthcare. Dr. Samuel Asare is a professional engineer with enthusiasm for Python programming, research, and writing. He is highly skilled in applying data science methods to the extraction of useful insights from large data sets. He possesses solid skills in project management processes. Samuel has previously held positions, in industry and academia, as a process engineer and a lecturer of materials science and engineering respectively. Presently, he is pursuing his passion for solving industry problems, using data science methods, and writing. https://www.linkedin.com/in/ivanliu1989/
Programming Languages: Python, R, Google Cloud Contacted by Melwyn Dsouza on 14/05/22018
Contacted for HTML5 and CSS3 on July 22, 2019 by Sneha Shinde
https://www.udemy.com/making-ionic-mobile-apps-with-ionic-creator/
Barbora Stetinova works in an Automotive industry earned experience in data science and machine learning, leading small team, leading strategical projects and in controlling topics for 13 years. Since Sept 2018 she is a member of IT department participating on the Data science implementation in an automotive company.
Content
Table of Contents
Introduction to Data Science in Python
Regression
Binary Classification
Multiclass Classification with RandomForest
Performing Your First Cluster Analysis
How to Assess Performance
The Generalization of Machine Learning Models
Hyperparameter Tuning
Interpreting a Machine Learning Model
Analyzing a Dataset
Data Preparation
Feature Engineering
Imbalanced Datasets
Dimensionality Reduction
Ensemble Learning
Machine Learning Pipelines
Automated Feature Engineering
Introduction to Data Science in Python
Regression
Binary Classification
Multiclass Classification with RandomForest
Performing Your First Cluster Analysis
How to Assess Performance
The Generalization of Machine Learning Models
Hyperparameter Tuning
Interpreting a Machine Learning Model
Analyzing a Dataset
Data Preparation
Feature Engineering
Imbalanced Datasets
Dimensionality Reduction
Ensemble Learning
Machine Learning Pipelines
Automated Feature Engineering
