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The Data Science Workshop

The Data Science Workshop

By : Anthony So , Thomas Joseph, Robert Thas John, Andrew Worsley , Dr. Samuel Asare , Ivan Liu, Tiffany Ford, Barbora stetinova, Pritesh Tiwari
3.3 (4)
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The Data Science Workshop

The Data Science Workshop

3.3 (4)
By: Anthony So , Thomas Joseph, Robert Thas John, Andrew Worsley , Dr. Samuel Asare , Ivan Liu, Tiffany Ford, Barbora stetinova, Pritesh Tiwari

Overview of this book

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.
Table of Contents (18 chapters)
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1. Introduction to Data Science in Python

Overview

By the end of this chapter, you will be able to explain what data science is and distinguish between supervised and unsupervised learning. You will also be able to explain what machine learning is and distinguish between regression, classification, and clustering problems. You will be able to create and manipulate different types of Python variable, including core variables, lists, and dictionaries. You will build a for loop, print results using f-strings, and define functions. You will also import Python packages and load data in different formats using pandas. You will also get your first taste of training a model using scikit-learn.

This very first chapter will introduce you to the field of data science and walk you through an overview of Python's core concepts and their application in the world of data science.

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The Data Science Workshop
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