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Key benefits

Tackle data science problems by identifying the problem to be solved

Illustrate patterns in data using appropriate visualizations

Implement suitable machine learning algorithms to gain insights from data

Description

Data Science Projects with Python is designed to give you practical guidance on industry-standard data analysis and machine learning tools, by applying them to realistic data problems. You will learn how to use pandas and Matplotlib to critically examine datasets with summary statistics and graphs, and extract the insights you seek to derive. You will build your knowledge as you prepare data using the scikit-learn package and feed it to machine learning algorithms such as regularized logistic regression and random forest. You’ll discover how to tune algorithms to provide the most accurate predictions on new and unseen data. As you progress, you’ll gain insights into the working and output of these algorithms, building your understanding of both the predictive capabilities of the models and why they make these predictions. By then end of this book, you will have the necessary skills to confidently use machine learning algorithms to perform detailed data analysis and extract meaningful insights from unstructured data.

Who is this book for?

If you are a data analyst, data scientist, or business analyst who wants to get started using Python and machine learning techniques to analyze data and predict outcomes, this book is for you. Basic knowledge of Python and data analytics will help you get the most from this book. Familiarity with mathematical concepts such as algebra and basic statistics will also be useful.

What you will learn

Install the required packages to set up a data science coding environment

Load data into a Jupyter notebook running Python

Use Matplotlib to create data visualizations

Fit machine learning models using scikit-learn

Use lasso and ridge regression to regularize your models

Compare performance between models to find the best outcomes

Use k-fold cross-validation to select model hyperparameters

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Amazon verified reviews

Honest Reviewer Jul 08, 2020

The book is very well written and author did a good job explaining every line of codes and concepts. Worth every penny! Thank you!

Amazon Verified review

Monsoon Feb 03, 2020

I liked this book better because it broke through some other books' lectures and abstracts and dove into the kind of data and scenarios that I am more likely to actually encounter in my job, rather than just memorize them. Plus I didn't have to fix or workaround outdated or outversioned python code as I have had to do with some online teaching forums. This book will move your career or business forward.

Jonas Jun 24, 2019

This book teaches you the best practices of data science and machine learning based on real world case studies. I found this highly valuable because you are able to actually work on real data sets. This is also a quick way to learn industry recognized tools and mathematical concepts that are actually being used by data scientist. Another advantage of this book in my opinion is the author's approach for coding. Author writes and explains each code and outcome separately rather than giving you several paragraphs of code and explain them all at once. I strongly recommend this book if you want to learn data science and machine learning on a practical level applying code and assessing the outcome

Richard Aug 09, 2019

As someone who has managed multiple data science projects in academia and the business world, I found this book to be a much-needed introduction to practical data science in the real-world. Some books thoroughly cover the mathematical complexity of machine learning models while others focus on implementing the models through coding (e.g. Scikit-Learn, Tensor flow, etc.). However, it is rare to find a book that ties the math and coding together to provide a comprehensive take on the data science process, which includes much under-appreciated topics such as data munging, exploratory data analysis, model evaluation, etc. Nevertheless, the author also does not skip out on explanation of the mathematics of the machine learning models and treat them as “black-boxes,” which can be frustrating for many readers who need more depth.This book is ideal for individuals with some familiarity with Python and limited mathematical background. It does not include the latest, cutting-edge deep learning models. However, having a robust process of understanding the data and evaluating models is more critical to the success of a data science project than applying the latest, most sophisticated models coming out of academic research. In this regards, the author does an excellent job of walking through its reader step-by-step in building a robust pipeline process using real-world data science projects as examples.Chapter 6: Imputation of Missing Data, Financial Analysis, and Delivery to Client offers a good overview of the most important step in data science in the business world. You would be hard-pressed to find information in this chapter anywhere else.For experienced data scientists, this book may be too introductory, but it can serve as a textbook or a training manual for your team if you lead a team with entry-level data scientists/analysts who recently graduated from school and still need help applying what they learned from school in the real-world.My only suggestion to the author would have been to include more materials on the next steps and provide a brief survey of the latest models in data science and resources to learn about them.All in all, it is a great book for new entrants or those hoping to join the field. It also seems ideal as a textbook for short 6-8 week data science courses.

C. Bennett May 25, 2019

As a professor at DePaul University who teaches data science and machine learning, I can say that this is a great book for introducing the fundamental concepts that lie behind using Python for data science projects. Readers will learn useful coding skills in Python, and its various packages for data manipulation and visualization such as Pandas, Numpy, Matplotlib. Furthermore, they will learn how to use Scikit-Learn, one of the major data science toolkits in Python, to construct machine learning models based on the same data. The book is well laid out, with each section building on the last, and reflects what actual data scientist do in the field day-to-day.The book provides a great platform for anyone who is interested in learning practical "how-to" skills, and creates the foundation for those who want to move on to more advanced concepts.

Data Science Projects with Python: A case study approach to successful data science projects using Python, pandas, and scikit-learn

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