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You're reading from Hands-On Data Science with Anaconda Utilize the right mix of tools to create high-performance data science applications

Product type Paperback

Published in May 2018

Publisher Packt

ISBN-13 9781788831192

Length 364 pages

Edition 1st Edition

Languages

Python

Tools

Anaconda

Concepts

Data Science

Authors (2):

James Yan

Yuxing Yan

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Table of Contents (15) Chapters

Preface

1. Ecosystem of Anaconda FREE CHAPTER

2. Anaconda Installation

3. Data Basics

4. Data Visualization

5. Statistical Modeling in Anaconda

6. Managing Packages

7. Optimization in Anaconda

8. Unsupervised Learning in Anaconda

9. Supervised Learning in Anaconda

10. Predictive Data Analytics – Modeling and Validation

11. Anaconda Cloud

12. Distributed Computing, Parallel Computing, and HPCC

13. References

14. Other Books You May Enjoy

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Review questions and exercises

Why is understanding various packages important?
What are package dependencies?
For R, Python, Julia, and Octave, find out how many packages are available for each of them, today.
How do we install a package in R, Python, and Julia?
How do we update a package in R, Python, and Julia?
What is the task view for R?
How do we install all R packages included in a task view?
After an R package is installed, how do you find its related directory? What is the usage to find its related directory? You could use the R package called healthcare as an example. Note that the package is about tools for healthcare machine learning.
Find out more details about the task views related to the subject of Econometrics. Then install all related R packages. How many are there?
How do we update one R package? How would we do it for Octave?
How do we find the manual for...

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Authors (2)

Yuxing Yan

Yuxing Yan graduated from McGill University with a PhD in finance. Over the years, he has been teaching various finance courses at eight universities: McGill University and Wilfrid Laurier University (in Canada), Nanyang Technological University (in Singapore), Loyola University of Maryland, UMUC, Hofstra University, University at Buffalo, and Canisius College (in the US). His research and teaching areas include: market microstructure, open-source finance and financial data analytics. He has 22 publications including papers published in the Journal of Accounting and Finance, Journal of Banking and Finance, Journal of Empirical Finance, Real Estate Review, Pacific Basin Finance Journal, Applied Financial Economics, and Annals of Operations Research. He is good at several computer languages, such as SAS, R, Python, Matlab, and C. His four books are related to applying two pieces of open-source software to finance: Python for Finance (2014), Python for Finance (2nd ed., expected 2017), Python for Finance (Chinese version, expected 2017), and Financial Modeling Using R (2016). In addition, he is an expert on data, especially on financial databases. From 2003 to 2010, he worked at Wharton School as a consultant, helping researchers with their programs and data issues. In 2007, he published a book titled Financial Databases (with S.W. Zhu). This book is written in Chinese. Currently, he is writing a new book called Financial Modeling Using Excel — in an R-Assisted Learning Environment. The phrase "R-Assisted" distinguishes it from other similar books related to Excel and financial modeling. New features include using a huge amount of public data related to economics, finance, and accounting; an efficient way to retrieve data: 3 seconds for each time series; a free financial calculator, showing 50 financial formulas instantly, 300 websites, 100 YouTube videos, 80 references, paperless for homework, midterms, and final exams; easy to extend for instructors; and especially, no need to learn R.

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Yan

James Yan is an undergraduate student at the University of Toronto (UofT), currently double-majoring in computer science and statistics. He has hands-on knowledge of Python, R, Java, MATLAB, and SQL. During his study at UofT, he has taken many related courses, such as Methods of Data Analysis I and II, Methods of Applied Statistics, Introduction to Databases, Introduction to Artificial Intelligence, and Numerical Methods, including a capstone course on AI in clinical medicine.

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