|
Showing 1 - 3 of
3 matches in All Departments
Develop, deploy, and streamline your data science projects with the
most popular end-to-end platform, Anaconda Key Features -Use
Anaconda to find solutions for clustering, classification, and
linear regression -Analyze your data efficiently with the most
powerful data science stack -Use the Anaconda cloud to store,
share, and discover projects and libraries Book DescriptionAnaconda
is an open source platform that brings together the best tools for
data science professionals with more than 100 popular packages
supporting Python, Scala, and R languages. Hands-On Data Science
with Anaconda gets you started with Anaconda and demonstrates how
you can use it to perform data science operations in the real
world. The book begins with setting up the environment for Anaconda
platform in order to make it accessible for tools and frameworks
such as Jupyter, pandas, matplotlib, Python, R, Julia, and more.
You'll walk through package manager Conda, through which you can
automatically manage all packages including cross-language
dependencies, and work across Linux, macOS, and Windows. You'll
explore all the essentials of data science and linear algebra to
perform data science tasks using packages such as SciPy,
contrastive, scikit-learn, Rattle, and Rmixmod. Once you're
accustomed to all this, you'll start with operations in data
science such as cleaning, sorting, and data classification. You'll
move on to learning how to perform tasks such as clustering,
regression, prediction, and building machine learning models and
optimizing them. In addition to this, you'll learn how to visualize
data using the packages available for Julia, Python, and R. What
you will learn Perform cleaning, sorting, classification,
clustering, regression, and dataset modeling using Anaconda Use the
package manager conda and discover, install, and use functionally
efficient and scalable packages Get comfortable with heterogeneous
data exploration using multiple languages within a project Perform
distributed computing and use Anaconda Accelerate to optimize
computational powers Discover and share packages, notebooks, and
environments, and use shared project drives on Anaconda Cloud
Tackle advanced data prediction problems Who this book is
forHands-On Data Science with Anaconda is for you if you are a
developer who is looking for the best tools in the market to
perform data science. It's also ideal for data analysts and data
science professionals who want to improve the efficiency of their
data science applications by using the best libraries in multiple
languages. Basic programming knowledge with R or Python and
introductory knowledge of linear algebra is expected.
Learn and implement various Quantitative Finance concepts using the
popular Python libraries About This Book * Understand the
fundamentals of Python data structures and work with time-series
data * Implement key concepts in quantitative finance using popular
Python libraries such as NumPy, SciPy, and matplotlib * A
step-by-step tutorial packed with many Python programs that will
help you learn how to apply Python to finance Who This Book Is For
This book assumes that the readers have some basic knowledge
related to Python. However, he/she has no knowledge of quantitative
finance. In addition, he/she has no knowledge about financial data.
What You Will Learn * Become acquainted with Python in the first
two chapters * Run CAPM, Fama-French 3-factor, and
Fama-French-Carhart 4-factor models * Learn how to price a call,
put, and several exotic options * Understand Monte Carlo
simulation, how to write a Python program to replicate the
Black-Scholes-Merton options model, and how to price a few exotic
options * Understand the concept of volatility and how to test the
hypothesis that volatility changes over the years * Understand the
ARCH and GARCH processes and how to write related Python programs
In Detail This book uses Python as its computational tool. Since
Python is free, any school or organization can download and use it.
This book is organized according to various finance subjects. In
other words, the first edition focuses more on Python, while the
second edition is truly trying to apply Python to finance. The book
starts by explaining topics exclusively related to Python. Then we
deal with critical parts of Python, explaining concepts such as
time value of money stock and bond evaluations, capital asset
pricing model, multi-factor models, time series analysis, portfolio
theory, options and futures. This book will help us to learn or
review the basics of quantitative finance and apply Python to solve
various problems, such as estimating IBM's market risk, running a
Fama-French 3-factor, 5-factor, or Fama-French-Carhart 4 factor
model, estimating the VaR of a 5-stock portfolio, estimating the
optimal portfolio, and constructing the efficient frontier for a
20-stock portfolio with real-world stock, and with Monte Carlo
Simulation. Later, we will also learn how to replicate the famous
Black-Scholes-Merton option model and how to price exotic options
such as the average price call option. Style and approach This book
takes a step-by-step approach in explaining the libraries and
modules in Python, and how they can be used to implement various
aspects of quantitative finance. Each concept is explained in depth
and supplemented with code examples for better understanding.
A handson guide with easy to follow examples to help you learn
about option theory, quantitative finance, financial modeling, and
time series using Python. Python for Finance is perfect for
graduate students, practitioners, and application developers who
wish to learn how to utilize Python to handle their financial
needs. Basic knowledge of Python will be helpful but knowledge of
programming is necessary.
|
|