Data Mining and Data Visualization

Introduction

Data Mining and Data Visualization book gives you a comprehensive understanding of the modern approaches used for data analysis. You will gain knowledge in extracting useful information from large datasets, as well as learn about various visualization techniques to present complex data. The book starts with an introduction to data mining principles, algorithms, and statistical methods used for analysis. It then progresses to advanced topics such as deep learning, text mining, artificial intelligence (AI), and natural language processing (NLP). You will also discover different tools used for visualizing data such as Tableau, Power BI, and others. By the end of this training course, you will have a strong foundation needed to make sense of meaningful insights from raw datasets and effectively communicate these results in an understandable way.

Objectives

1. To develop an understanding of basic Data Mining and Data Visualization concepts, tools & techniques.
2. To identify the data sources, gather and store data to support analysis workflows.
3. To design and build scalable, efficient analytical models that can be used in real-world applications.
4. To present complex datasets using interactive visualizations for clearer communication and easier decision making.
5. To utilize advanced analytics techniques such as neural networks, machine learning, clustering, and text mining for deeper insights into large datasets.
6. Develop strategies for transforming large amounts of raw data into meaningful reports or presentations for stakeholders or management teams.

Table of Contents

Module 1. Introduction to Data Mining and Data Visualization

-Overview of data mining and its applications.

-Differences between data mining and data visualization.

-Data Mining and Data Visualization concepts, tools & techniques.

-Benefits of using data mining and data visualization techniques.

-The data sources, gather and store data to support analysis workflows

Module 2. Processes for Data Mining and Data Visualization

-Understanding the different stages of the data mining process.

-Steps involved in the creation of a visual representation of data

-Data preparation techniques including cleaning, filtering, sampling, and normalizing.

-Methods for analysing large datasets.

-Identifying patterns in data using machine learning algorithms such as decision trees, clustering algorithms, neural networks, Naive Bayes etc.

Module 3. Tools for Data Mining and Data Visualization: 

-Learning about effective tools for data mining such as Apache Spark, Hadoop, Weka etc.

– Advanced analytics techniques such as neural networks, machine learning, clustering and text mining for deeper insights into large datasets.

-Exploring popular open-source libraries for creating visual representations such as D3.js, matplotlib etc.

Module 4. Implementing a Project involving Data Mining and Data Visualization              

-Developing an understanding on how to design a project that uses both data mining and data visualization techniques;

-Application demonstration by completing a real-world project involving complex datasets.

-Presenting insights gained from the analysis visually through charts, graphs or other forms of interactive graphics.

Module 5: Exams