Data Mining For Business Analytics & Data Analysis In Python

By Book Option

Do you want to become an expert in data mining? Do you want to use data science, analytics, and explainable AI to uncover business insights that can be put into practice? You've arrived at the ideal location.

I'll walk you through the most effective Python data mining methods I've seen used to analyse data and extract insightful conclusion

• Language: English
• Publisher: Book Option
• Release date: Nov 3, 2023
• ISBN: 9798868970115

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Data Mining For Business Analytics & Data Analysis In Python

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Contents

Copyright

Contents

What You'll Discover

About

Who Should Take This Book

Text mining

Applications of Text Mining

Method of Text Mining

Matrix of Terms

Exploiting TDM

Text Mining And Data Mining Contrasted

Optimal Techniques for Text Mining

Review Questions

Naïve Bayes Analysis

Chance

Naïve-Bayes Model

An Illustration Of Basic Classification

Example of Text Classification

Bayes's Benefits and Drawbacks

Review Questions

Support Vector Machines

SVM framework

The Kernel Approach

Benefits and drawbacks

Review Questions

Web Mining

Content Mining On The Web

Mining Web Structures

Internet Usage Analysis

Algorithms for Web Structure Mining

Review Questions

Social Network Analysis

SNA Applications

Topologies Of Networks

Algorithms And Techniques

Locating Sub-Networks

Calculating Nodes' Importance

PageRank

Useful Information

Evaluating Data Analytics Against SNA

Review Questions

Big Data

Getting to Know Big Data

Managing Large-Scale Data

Taking Advantage of Big Data

Big Data Management

Recap of the Questions

Data Modeling and SQL

Data Management Systems' Evolution

Relational Data Model

Relational Data Model Implementation

Systems For Managing Databases (DBMS)

Structured Query Language (SQL)

Recap of the Questions

Statistics Tutorial

Description of Statistics

Inferential Statistics

Predictive Statistics

Review Questions

An Introduction to Artificial Intelligence

AI, Machine Learning, and Deep Learning

The Industrial Revolution

The Revolution in Information

The Revolution in Cognitive (or AI)

AI and Job Losses

AI as an Existential Danger

Review Questions

Data Ownership and Privacy

Ownership of Data

Security of Data

The Dilemma Of Data Sharing

Recap of the Questions

Careers in Data Science

Data Scientist

Aptitude For Data Science

Well-liked Abilities

Applications and Frameworks

The Chain Of Data Processing

Methods and Procedures for Data Mining

Big Data Mining

Data Privacy And Artificial Intelligence

Dimensionality Reduction and Data Wrangling

Handling Data

Reduction of Dimensionality

Conclusion

What You'll Discover

Recognize the benefits of data mining for fast data analysis and interpretation.

Utilise Python programming to apply data mining algorithms for business analytics.

Describe the fundamentals of different data mining algorithms, such as explainable AI and supervised and unsupervised machine learning.

Use the explainable artificial intelligence models to explain the outcomes of data mining models.

Apply data mining techniques in real-world scenarios and through practical tasks.

Use Python to carry out association rule learning, dimension reduction, and cluster analysis.

Create a portfolio of data mining initiatives for intelligence and business data analytics.

Make informed company decisions and strategies by utilising data mining tools.

About

Do you want to become an expert in data mining? Do you want to use data science, analytics, and explainable AI to uncover business insights that can be put into practice? You've arrived at the ideal location.

I'll walk you through the most effective Python data mining methods I've seen used to analyse data and extract insightful conclusions throughout my professional career.

With so much data available these days thanks to numerous spreadsheets, it's easy to feel overwhelmed. Herein lies the use of data mining techniques. to quickly evaluate, look for trends, and provide you with a result. The value that data mining adds, in my opinion, is the ability to put an end to number crunching and pivot table construction, freeing up time to develop practical plans based on the insights.

First, you won't have to focus too much on the arithmetic to acquire the intuition behind the models. Understanding the assumptions that underlie a model and why it makes sense is essential. I will use words, graphs, and metaphors to describe each model to you; I won't go into much detail on maths or the Greek letter.

The comprehensive book design of the most effective Data Mining methods for Data Science and Business Analytics is the second justification. There is a challenge at the conclusion of each section. The idea is for you to put what you've learnt into practice right away. I provide you with a dataset and a set of steps to tackle the problem. It is, in my opinion, the finest method to truly instil all of the techniques in you. As a result, each technique will have two case studies. I hope I piqued your curiosity, and I can't wait to see you inside!

Who Should Take This Book

Experts who want to understand Data Mining methods

Data analysts are beginning to study data mining methods.

Business analysts that are interested in learning algorithms for obtaining business insights

Any Python coder interested in learning about tools for data mining.

Text mining

The process of extracting information, patterns, and insights from a systematically arranged set of textual datasets is known as text mining. In order to evaluate and interpret the text data, machine learning and natural language processing (NLP) approaches are used. The frequency analysis of key terms and the semantic linkages between them can be aided by text mining. High accuracy and scalability relevant information should be produced via good text mining. One significant component of the world's expanding data set is text.

With the use of social media tools, individuals can now create their own text, photographs, and other types of content. Large-scale social media data can be mined using text mining techniques to determine preferences and gauge emotional states. Additionally, it can be used at the individual, corporate, and social levels. Research in text mining is a field that is fast developing. With the increasing volume of social media and other text data, it is necessary to efficiently extract and classify important information from the text.

Text mining has seen a full change thanks to artificial intelligence tools. Voice-activated chatbots, like Apple's Siri and Amazon's Alexa, have become remarkably accurate at understanding text, identifying it, and responding to it. Caselet: Private Security and WhatsApp Do you believe that your social media posts stay private? Rethink it. A recently created dashboard illustrates the volume of personal data that is available and the methods by which businesses might utilise it for their own gain. This is a dashboard that shows the 45-day exchange of conversations between Nicole and Jennifer. Nicole and Jennifer discuss a wide range of topics, including technology, politics, laundry, and desserts. Jennifer responds to Nicole far more than she does, and she views Nicole as the influencer in their relationship. Jennifer's personal beliefs and tone are strongly positive. Jennifer's awake hours are depicted in the data visualisation, which indicates that she goes to bed at midnight and is most active around 8:00 p.m. 15% of her time is about sweets, and 53% of it is about food. Perhaps she is a shrewd individual who can promote restaurant or diet advertisements.

The conversation between Nicole and Jennifer reveals the most personal detail: they talk about conservative politics, extreme parties, and right-wing populism. It serves as an example of how much private information may be dangerously and infinitely retrieved from your WhatsApp discussions. WhatsApp boasts over 450 million users, making it the largest messaging service globally. This three-year-old business was recently acquired by Facebook for an astounding $19 billion. On WhatsApp, users divulge a great deal of private and sensitive information that they might not even tell their relatives. Sources: Adi Azaria, What Facebook Knows About You From One WhatsApp Conv, Linked In, April 10, 2014. 1. What societal and business ramifications does this type of analysis have? 2: Do you feel anxious? Do you need to worry?

Text mining is applicable to almost any type of text, including Word documents, PDF files, XML files, text messages, and more, from both corporate and non-commercial areas. Here are a few illustrative instances: Text sources in the legal field would be laws, decisions made by courts, orders from courts, etc. It would include published research articles, interview transcripts, and other materials from academic studies. It will comprise CFO statements, internal reports, statutory reports, and more in the finance industry. It would comprise patient histories, discharge summaries, medical journals, etc. in the medical field. It would contain commercials, client feedback, etc. in marketing. It would include patent applications, all of the data on the World Wide Web, and more in the context of technology and search.

Applications of Text Mining

When used by knowledge officers, text mining can be an effective technique for extracting pertinent information for an organisation. Numerous application domains and industrial sectors, such as decision assistance, sentiment analysis, fraud detection, survey analysis, and many more, can benefit from the use of text