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Learn how to make the right decisions for your business with the
help of Python recipes and the expertise of data leaders Key
Features Learn and practice various clustering techniques to gather
market insights Explore real-life use cases from the business world
to contextualize your learning Work your way through practical
recipes that will reinforce what you have learned Book
DescriptionOne of the most valuable contributions of data science
is toward helping businesses make the right decisions.
Understanding this complicated confluence of two disparate worlds,
as well as a fiercely competitive market, calls for all the
guidance you can get. The Art of Data-Driven Business is your
invaluable guide to gaining a business-driven perspective, as well
as leveraging the power of machine learning (ML) to guide
decision-making in your business. This book provides a common
ground of discussion for several profiles within a company. You'll
begin by looking at how to use Python and its many libraries for
machine learning. Experienced data scientists may want to skip this
short introduction, but you'll soon get to the meat of the book and
explore the many and varied ways ML with Python can be applied to
the domain of business decisions through real-world business
problems that you can tackle by yourself. As you advance, you'll
gain practical insights into the value that ML can provide to your
business, as well as the technical ability to apply a wide variety
of tried-and-tested ML methods. By the end of this Python book,
you'll have learned the value of basing your business decisions on
data-driven methodologies and have developed the Python skills
needed to apply what you've learned in the real world. What you
will learn Create effective dashboards with the seaborn library
Predict whether a customer will cancel their subscription to a
service Analyze key pricing metrics with pandas Recommend the right
products to your customers Determine the costs and benefits of
promotions Segment your customers using clustering algorithms Who
this book is forThis book is for data scientists, machine learning
engineers and developers, data engineers, and business decision
makers who want to apply data science for business process
optimization and develop the skills needed to implement data
science projects in marketing, sales, pricing, customer success, ad
tech, and more from a business perspective. Other professionals
looking to explore how data science can be used to improve business
operations, as well as individuals with technical skills who want
to back their technical proposal with a strong business case will
also find this book useful.
Quickly build and deploy massive data pipelines and improve
productivity using Azure Databricks Key Features Get to grips with
the distributed training and deployment of machine learning and
deep learning models Learn how ETLs are integrated with Azure Data
Factory and Delta Lake Explore deep learning and machine learning
models in a distributed computing infrastructure Book
DescriptionMicrosoft Azure Databricks helps you to harness the
power of distributed computing and apply it to create robust data
pipelines, along with training and deploying machine learning and
deep learning models. Databricks' advanced features enable
developers to process, transform, and explore data. Distributed
Data Systems with Azure Databricks will help you to put your
knowledge of Databricks to work to create big data pipelines. The
book provides a hands-on approach to implementing Azure Databricks
and its associated methodologies that will make you productive in
no time. Complete with detailed explanations of essential concepts,
practical examples, and self-assessment questions, you'll begin
with a quick introduction to Databricks core functionalities,
before performing distributed model training and inference using
TensorFlow and Spark MLlib. As you advance, you'll explore MLflow
Model Serving on Azure Databricks and implement distributed
training pipelines using HorovodRunner in Databricks. Finally,
you'll discover how to transform, use, and obtain insights from
massive amounts of data to train predictive models and create
entire fully working data pipelines. By the end of this MS Azure
book, you'll have gained a solid understanding of how to work with
Databricks to create and manage an entire big data pipeline. What
you will learn Create ETLs for big data in Azure Databricks Train,
manage, and deploy machine learning and deep learning models
Integrate Databricks with Azure Data Factory for extract,
transform, load (ETL) pipeline creation Discover how to use Horovod
for distributed deep learning Find out how to use Delta Engine to
query and process data from Delta Lake Understand how to use Data
Factory in combination with Databricks Use Structured Streaming in
a production-like environment Who this book is forThis book is for
software engineers, machine learning engineers, data scientists,
and data engineers who are new to Azure Databricks and want to
build high-quality data pipelines without worrying about
infrastructure. Knowledge of Azure Databricks basics is required to
learn the concepts covered in this book more effectively. A basic
understanding of machine learning concepts and beginner-level
Python programming knowledge is also recommended.
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