Summary

Introducing Data Science teaches you how to accomplish the fundamental tasks that occupy data scientists. Using the Python language and common Python libraries, you'll experience firsthand the challenges of dealing with data at scale and gain a solid foundation in data science.

About the Technology

Many companies need developers with data science skills to work on projects ranging from social media marketing to machine learning. Discovering what you need to learn to begin a career as a data scientist can seem bewildering. This book is designed to help you get started.

About the Book

Introducing Data ScienceIntroducing Data Science explains vital data science concepts and teaches you how to accomplish the fundamental tasks that occupy data scientists. You’ll explore data visualization, graph databases, the use of NoSQL, and the data science process. You’ll use the Python language and common Python libraries as you experience firsthand the challenges of dealing with data at scale. Discover how Python allows you to gain insights from data sets so big that they need to be stored on multiple machines, or from data moving so quickly that no single machine can handle it. This book gives you hands-on experience with the most popular Python data science libraries, Scikit-learn and StatsModels. After reading this book, you’ll have the solid foundation you need to start a career in data science. 

What’s Inside

• Handling large data
• Introduction to machine learning
• Using Python to work with data
• Writing data science algorithms

About the Reader

This book assumes you're comfortable reading code in Python or a similar language, such as C, Ruby, or JavaScript. No prior experience with data science is required.

About the Authors

Davy Cielen, Arno D. B. Meysman, and Mohamed Ali are the founders and managing partners of Optimately and Maiton, where they focus on developing data science projects and solutions in various sectors. 

Table of Contents

1. Data science in a big data world
2. The data science process
3. Machine learning
4. Handling large data on a single computer
5. First steps in big data
6. Join the NoSQL movement
7. The rise of graph databases
8. Text mining and text analytics
9. Data visualization to the end user

The book is available, here.

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Additional Reading

• What statisticians think about data scientists
• Data Science Compared to 16 Analytic Disciplines
• 10 types of data scientists
• 91 job interview questions for data scientists
• 50 Questions to Test True Data Science Knowledge
• 24 Uses of Statistical Modeling
• 21 data science systems used by Amazon to operate its business
• Top 20 Big Data Experts to Follow (Includes Scoring Algorithm)
• 5 Data Science Leaders Share their Predictions for 2016 and Beyond
• 50 Articles about Hadoop and Related Topics
• 10 Modern Statistical Concepts Discovered by Data Scientists
• Top data science keywords on DSC
• 4 easy steps to becoming a data scientist
• 22 tips for better data science
• How to detect spurious correlations, and how to find the real ones
• 17 short tutorials all data scientists should read (and practice)
• High versus low-level data science

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