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Mastering Python for Data Science, by Samir Madhavan
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Explore the world of data science through Python and learn how to make sense of data
About This Book- Master data science methods using Python and its libraries
- Create data visualizations and mine for patterns
- Advanced techniques for the four fundamentals of Data Science with Python - data mining, data analysis, data visualization, and machine learning
If you are a Python developer who wants to master the world of data science then this book is for you. Some knowledge of data science is assumed.
What You Will Learn- Manage data and perform linear algebra in Python
- Derive inferences from the analysis by performing inferential statistics
- Solve data science problems in Python
- Create high-end visualizations using Python
- Evaluate and apply the linear regression technique to estimate the relationships among variables.
- Build recommendation engines with the various collaborative filtering algorithms
- Apply the ensemble methods to improve your predictions
- Work with big data technologies to handle data at scale
Data science is a relatively new knowledge domain which is used by various organizations to make data driven decisions. Data scientists have to wear various hats to work with data and to derive value from it. The Python programming language, beyond having conquered the scientific community in the last decade, is now an indispensable tool for the data science practitioner and a must-know tool for every aspiring data scientist. Using Python will offer you a fast, reliable, cross-platform, and mature environment for data analysis, machine learning, and algorithmic problem solving.
This comprehensive guide helps you move beyond the hype and transcend the theory by providing you with a hands-on, advanced study of data science.
Beginning with the essentials of Python in data science, you will learn to manage data and perform linear algebra in Python. You will move on to deriving inferences from the analysis by performing inferential statistics, and mining data to reveal hidden patterns and trends. You will use the matplot library to create high-end visualizations in Python and uncover the fundamentals of machine learning. Next, you will apply the linear regression technique and also learn to apply the logistic regression technique to your applications, before creating recommendation engines with various collaborative filtering algorithms and improving your predictions by applying the ensemble methods.
Finally, you will perform K-means clustering, along with an analysis of unstructured data with different text mining techniques and leveraging the power of Python in big data analytics.
Style and approachThis book is an easy-to-follow, comprehensive guide on data science using Python. The topics covered in the book can all be used in real world scenarios.
- Sales Rank: #1031470 in Books
- Published on: 2015-08-31
- Released on: 2015-08-31
- Original language: English
- Number of items: 1
- Dimensions: 9.25" h x .67" w x 7.50" l, 1.12 pounds
- Binding: Paperback
- 294 pages
About the Author
Samir Madhavan
Samir Madhavan has been working in the field of data science since 2010. He is an industry expert on machine learning and big data. He has also reviewed R Machine Learning Essentials by Packt Publishing. He was part of the ubiquitous Aadhar project of the Unique Identification Authority of India, which is in the process of helping every Indian get a unique number that is similar to a social security number in the United States. He was also the first employee of Flutura Decision Sciences and Analytics and is a part of the core team that has helped scale the number of employees in the company to 50. His company is now recognized as one of the most promising Internet of Things―Decision Sciences companies in the world.
Most helpful customer reviews
1 of 1 people found the following review helpful.
Useful primer and a good reference for Python data science
By Natester
Madhavan's book has proven useful for some of the projects I'm working on.
The first chapter includes a brief primer on Numpy and Pandas--useful for someone that is new to the Python ecosystem, but assuming you are already familiar with those packages, it should be okay to skip to the second chapter. The second chapter includes some Python statistical examples that I have not seen in other texts, but are important when looking at different types of distributions. These distribution examples and explanations are a must-have in my collection of Python recipes. There are also data visualization tweaks that I've not seen in other Data Science + Python texts.
The book also provides an intro to some of the canonical machine learning algorithms (Chapter 5). These examples are great for becoming familiarized with some of the ML algorithms out there without being overwhelmed by all the other algorithms out there.
If you are looking for a good primer on Data Science with Python, this is a good book. I'm using the book as a reference more than a primer and the book is also useful.
1 of 1 people found the following review helpful.
Really solid book for getting up to speed on using Python for Data Science
By Tim Crothers
This book covers a lot of ground quick, which I liked a lot. Like everything that has pro's and con's. It does a really solid job of hitting all of the significant topics. If you're an experienced Python programmer getting into data science then this is an excellent resource for you. If you are newer to Python then you might want to hold off. The downside of the fast and broad coverage is the depth could be enhanced some. You will also likely struggle a bit if you are new to Data Science. This book is less about how to do data science and more about how to do it with Python. It also could use another round of editing to fix typos and grammatical errors but they aren't bad enough to detract from the value of the material.
Bottom line, a really useful addition to my library for quick examples and coverage of using Python in Data Science applications.
1 of 1 people found the following review helpful.
Good introduction to Data Science Algorithms and their use in Python
By Jonathan Brett Crawley
The pace of the book is quite quick, so you will be up to speed in no time. The book gives a nice introduction to the algorithms used in data science, explained well and backed up with source code examples of how to implement them in the Python language. My only criticism would be that there are a number of grammatical errors in the text but they do not obstruct the reader from understanding the material. Overall a good beginners book for getting to know the world of data science
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