Data Science for Business

Data Mining, Data Warehousing, Data Analytics, Data Visualization, Data Modelling, Regression Analysis, Big Data and Machine Learning

By: Travis Goleman

Narrated by: Austin R Stoler

Length: 2 hrs and 41 mins

Publisher's Summary

This guide will discuss everything that you need to know about data science for business because it is impossible not to use it in your job. As data science and big data gets more popular, it is being used in more businesses than ever before because of how efficient it is. Throughout the audiobook, you are going to find out how data can mitigate risks in business and how to handle and manage big data in business.

Data can improve the efficacy of your business in several ways. Here is a flavor of how data can play a significant role in upping your business game:

• Big Data for Small Businesses
• How Data Can Help Mitigate Risks in Business
• How to Handle and Manage Big Data in Business
• Data Visualization
• Machine Learning for Data Science
• Data Mining Functionalities
• Basics of Big Data Analytics
• Data Science and Big Data Analytics
• The Process of Data Analysis
• Real World Examples of Data Science Benefitting Businesses
• Areas of Concern in Using Big Data for Business
• Tools, Technologies, and Trends in Business Data Analytics
• The Significance of Data Analysis for Your Business
• Overview of Data Warehouse Development Goals
• Data Mining Text
• Data Modeling
• And Much More

There is a huge demand for data scientists marketing is like a horizontal functionality across all industries, therefore, this very critical skill would be required currently as well as in the future.

©2019 Travis Goleman (P)2019 Travis Goleman

Highly recommended!

There is also a chapter on evaluating and critiquing data mining proposals, which nicely ties together the algorithmic, business, and practical concepts discussed earlier in the book. Some case studies are revisited in several chapters at increasing levels of sophistication, making the book feel like a cohesive whole rather than a mere compilation of chapters. If you’re coming from a technical background, you will learn a great deal about the business and practical/implementation aspects of analytics. Highly recommended!

Great starting reference for data science.

This is a great book that gives a top level view of machine learning and data science. It is particularly useful for non-technical readers who need to have a need to know the lingo for being able to follow conversations around the topic. It is also a great introduction for technical people who have never worked in data science before.

An excellent tour of Data Science for business

This book gives a really solid grounding in both the business (strategic) and data (analytic, technical) aspects of modern data analytics. The authors clearly show that data is the next wave of change and that it will require a mindset change across all business functions--a mindset they call data-analytic thinking. If you need to master/improve this thinking skill set--here is a great place to start no matter what your job title.

Start Here

While it stops short of providing detailed instruction on how to use these techniques, it provides the reader a solid foundation for taking this next hands-on step. And for those who are not working directly with data, but are otherwise stakeholders in the use of analytics to drive better organizational outcomes, this book will greatly enable you to understand and add value to the analytical process.

Good Data Science 101 book.

The best book I've found so far at explaining the theory of Data Science. If you are looking to get into data science and don't want to just start plugging away with Python or R then this is a great starter book that will teach you the lingo and the concepts that surround data science.

The style is very pleasant

The style is very pleasant since authors have made efforts to put the reader in specific situations to better understand a problem. To be noted the very interesting discussion of data mining leaks as well as data mining automation. The book is divided by concepts and provides a focus on them (instead of techniques). Although no exercice is present, the book could easily be used as a resource for a course.

The understanding of Data Science.

The book requires a background in a number of supportive academics for full understanding . The discipline has defined its own language much like most of the technological disciplines and is best appreciated by those familiar with the vocabulary. It is a book that warrants study not just as a quick read for introduction. For a person studying or practicing in this area I highly recommend this book for both its interest and as a reference book. Travis Goleman have made a valuable contribution to the understanding of Data Science.

Well-organized Audio

Chapters are easy enough to read but don't over-simplify important concepts. Inclusion of Glossary, Bibliography, and index, as well as a detailed table of contents, makes it easy to navigate. The only exception our instructor took with the text during my course was their insistence that only the best data scientists should be considered. Removing this bias, the information provided was clear, concise, and helpful for anyone working with big data or in data analytics.

This book does a great job

The author provides strategies and guiding principles coupled with case studies and actionable recommendations. Listening this book will arm any leader with a toolkit to navigate and thrive in the world of data science and machine learning -- I think that this is a must-read for anyone who is interested in understanding how to employ these new fields to their business.

An Awesome book with practical industrial experien

This book explains radically what we should adapt to a digital world and how executives should perceive and strategically execute it. As an advocate to emerging data technologies, I highly recommend this book to everyone who is considering taking a share of the Fourth Industrial Revolution!

A great listen

This is by far the most accessible data science book out there. If you have any interaction with data scientists/statisticians as part of work, this book is a way to understand the burgeoning field better.
It does not go in depth into mathematics or computer science concepts, rather it grounds the material into a digestible format that anyone can understand.

Great book for providing a high level summary

Great book for providing a high level summary of data science techniques to people that may not care about the mathematical details. I tend to recommend this to people more worried about building up a working knowledge of classic problems and approaches in data science rather than for those wanting to look at page after page of detailed math related to probability distributions.

Excellent book for everyone

I read the book in parallel with the coursera course of applied data science with Python of Michigan University.
This book is an excellent complement in order to understand the real power and limitations of this kind of analysis.
I really enjoyed the math explanations that are scarce but important in order to understand what is really happening under the hood without being too technical.

Data Science for Business

This book will definitely open your mind to the possibilities and capabilities that business data provides.

If you have a close relationship to your marketing manager or executives, this book will make you shine when speaking at meetings or when you are setting up business plans.

It gives you the ability to look at data differently and make better decisions on what might have otherwise been difficult hurdles in the way of progress and innovation.

Excellent Overview of Concepts

Excellent overview of many data science concepts! I recommend making a list of key words (concepts, terms, algorithms, etc.) that are mentioned. These can be very useful for making flashcards and identifying topics for further research. After listening the book you of course won't fully understand all of these concepts and terms, but there is significant utility in being aware of these concepts and ready to connect them while working on problems.

One of the best I've listen on this topic

This book is fantastic. it's a perfect mix of high-level explanation and technical details. There doesn't seem to be much to help one actually execute the methods described, but that does not appear to be the author's intent (which is why there is no negative impact on my rating).

Big Data and Machine Learning

Strengths – Organization, having technical details in a side by side section for those who want it, covering details from definition, through use and application, as well as doing a good job explaining similarities and differences on key topics.
Weaknesses – there are a few small places I wanted more. Meaning if they could have somehow had more examples for the different models, situations, etc., especially as I got into more of the predictive models.

Great Book. Well done!

This is a great book for any in the data science field or wanting to just understand “Big Data” or a manager/professional just trying to “get current. “ I have a masters degree in software engineering with a data science background and three years experience in a prior job in Data warehousing. It was a long read, especially with the holidays, but well worth it, and more enjoyable than almost every technical book I have every listen.

If you are an analyst professionally or academical

If you are an analyst professionally or academically, this book profoundly and practically relates statistics to relevant business processes and data. If you are a professional data analyst this book will help you reorder and consolidate your work experience and knowledge while reinforcing and focusing your analytic abilities. If you are a student of data analytics, this book will accelerate your understanding of the required methods to become a practicing data analyst...and your "customers" will see this competence in your work and your presentation.

How Data Science Applies to Our Emerging Big Data

A discussion of holdout model tests, prediction & validation. Particular emphasis is placed on how to frame questions to apply to the business case so suitable conclusions can guide business decisions and strategy. You will get the sense that the authors are battle tested veterans of the data mining business and have applied their creativity to a broad range of business, data and technical challenges.