Powerful ideas from top nonfiction
Try Blinkist to get the key ideas from 7,500+ bestselling nonfiction titles and podcasts. Listen or read in just 15 minutes.
Get started
Blink 3 of 8 - The 5 AM Club
by Robin Sharma
Data Mining for Business Analytics summary
Galit Shmueli
Brief summary
Data Mining for Business Analytics by Galit Shmueli provides a comprehensive introduction to data mining and its applications in business. It covers techniques for analyzing large datasets to uncover valuable insights for decision-making.
Topics
Table of Contents
Data Mining for Business Analytics
Summary of key ideas
The Art and Science of Data Mining
In Data Mining for Business Analytics by Galit Shmueli, the author introduces us to the world of data mining. The book starts with an introduction to the art and science of data mining, emphasizing its growing significance in the business world. It highlights the role of data mining in making informed, data-driven decisions and how it can help businesses gain a competitive edge.
Shmueli then delves into the various aspects of data mining, beginning with data exploration and preparation. The book explains the importance of understanding the data and preparing it for analysis. It introduces us to the concept of data quality and the techniques to handle missing values, outliers, and other data anomalies.
Data Mining Techniques
As we progress through the book, Shmueli introduces us to different data mining techniques. She discusses predictive modeling, a crucial aspect of data mining that involves building models to predict future outcomes. She explains various predictive modeling techniques such as regression, decision trees, and neural networks, and their applications in business analytics.
Next, the book moves on to unsupervised learning, a type of machine learning where the model learns from unlabeled data. Shmueli explains clustering and association analysis as two key unsupervised learning techniques and illustrates their applications in segmenting customers and identifying product associations, among others.
Model Evaluation and Deployment
Shmueli then focuses on model evaluation and deployment, emphasizing the importance of assessing the model's performance before deploying it in a real-world business scenario. She introduces us to various performance metrics and validation techniques used to evaluate the model's accuracy and generalizability.
Furthermore, the book discusses deployment strategies and the challenges associated with implementing data mining models in business settings. It emphasizes the need for continuous monitoring and updating of the models to ensure their relevance and effectiveness.
Advanced Topics in Data Mining
In the latter part of Data Mining for Business Analytics, Shmueli explores advanced topics in data mining. She discusses text and web mining, explaining how businesses can extract valuable insights from unstructured data sources such as text documents and web pages. She also touches upon social network analysis, a technique used to understand and analyze social relationships among entities.
Finally, the book concludes with a discussion on the ethical and societal implications of data mining. Shmueli emphasizes the need for responsible use of data mining techniques and the importance of privacy and data security in the era of big data.
In Conclusion
In conclusion, Data Mining for Business Analytics by Galit Shmueli provides a comprehensive and practical guide to data mining techniques and their applications in business analytics. It serves as an invaluable resource for business professionals, data analysts, and students looking to harness the power of data mining for informed decision-making and business success.
How do we create content on this page?
More knowledge in less time
Read or listen
Get the key ideas from nonfiction bestsellers in minutes, not hours.
Find your next read
Get book lists curated by experts and personalized recommendations.
Shortcasts New
We’ve teamed up with podcast creators to bring you key insights from podcasts.
What is Data Mining for Business Analytics about?
Data Mining for Business Analytics by Galit Shmueli provides a comprehensive introduction to data mining techniques and their applications in business. It covers topics such as data preprocessing, classification, clustering, association analysis, and more, with a focus on practical implementation using software tools. The book is a valuable resource for business professionals and students looking to leverage data mining for improved decision-making and business insights.
Data Mining for Business Analytics Review
Data Mining for Business Analytics by Galit Shmueli (2018) provides essential insights into leveraging data analysis for strategic decision-making in business. Here's why this book stands out:
- Explains complex concepts clearly, allowing readers to grasp and apply data mining techniques effectively in real-world scenarios.
- Offers a comprehensive overview of various data mining methods and their applications across diverse industries, making it a valuable resource for professionals.
- Presents practical case studies and examples that demonstrate the impact of data mining on business outcomes, keeping the content engaging and relevant.
Who should read Data Mining for Business Analytics?
Business professionals looking to leverage data for decision-making and strategy
Data analysts and data scientists seeking practical techniques for extracting insights from large datasets
Students and academics studying business analytics, data mining, or related fields
Categories with Data Mining for Business Analytics
Book summaries like Data Mining for Business Analytics
People ❤️ Blinkist
Sven O.
It's highly addictive to get core insights on personally relevant topics without repetition or triviality. Added to that the apps ability to suggest kindred interests opens up a foundation of knowledge.
Thi Viet Quynh N.
Great app. Good selection of book summaries you can read or listen to while commuting. Instead of scrolling through your social media news feed, this is a much better way to spend your spare time in my opinion.
Jonathan A.
Life changing. The concept of being able to grasp a book's main point in such a short time truly opens multiple opportunities to grow every area of your life at a faster rate.
Renee D.
Great app. Addicting. Perfect for wait times, morning coffee, evening before bed. Extremely well written, thorough, easy to use.
People also liked these summaries
4.7 Stars
Average ratings on iOS and Google Play
36 Million
Downloads on all platforms
10+ years
Experience igniting personal growth
Data Mining for Business Analytics FAQs
What is the main message of Data Mining for Business Analytics?
The main message of Data Mining for Business Analytics highlights the importance of data-driven decision-making in business.
How long does it take to read Data Mining for Business Analytics?
Reading time for Data Mining for Business Analytics varies, but expect a few hours. The Blinkist summary can be read in a short time.
Is Data Mining for Business Analytics a good book? Is it worth reading?
Data Mining for Business Analytics is a valuable read for those interested in leveraging data for business insights. It's practical and insightful.
Who is the author of Data Mining for Business Analytics?
The author of Data Mining for Business Analytics is Galit Shmueli.
What to read after Data Mining for Business Analytics?
If you're wondering what to read next after Data Mining for Business Analytics, here are some recommendations we suggest:
- Basic Economics by Thomas Sowell
- The Ascent of Money by Niall Ferguson
- Think and Grow Rich by Napoleon Hill
- The 4-Hour Workweek by Tim Ferriss
- Rich Dad, Poor Dad by Robert T. Kiyosaki
- Secrets of the Millionaire Mind by T. Harv Eker
- The Richest Man in Babylon by George S. Clason
- Business Adventures by John Brooks
- The Most Important Thing by Howard Marks
- More Money Than God by Sebastian Mallaby
Featured Titles
- 1776
- American Kingpin
- An Attempt To Repair America's Broken Credit System
- Bulls, Bears and Other Beasts
- Buy Then Build
- Corporate Financial Distress, Restructuring, and Bankruptcy
- Corporate Value of Enterprise Risk Management
- Democracy at Work
- Encyclopedia of Chart Patterns
- Essential Mathematics for Market Risk Management