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 Description
You 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 for
Our 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.
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
Anthony So is an outstanding leader with more than 13 years of experience. He is recognized for his analytical skills and data-driven approach for solving complex business problems and driving performance improvements. He is also a successful coach and mentor with capabilities in statistical analysis and expertise in machine learning with Python.
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 tool sets across multiple industry segments.
Robert Thas John is a Google developer expert in machine learning. His day job involves working as a data engineer on the Google Cloud Platform by building, training, and deploying large-scale machine learning models. He also makes decisions about how to store and process large amounts of data. He has more than 10 years of experience in building enterprise-grade solutions and working with data. He spends his free time learning or contributing to the developer community. He frequently travels to speak at technology events or to mentor developers. He also writes a blog on data science.
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.
