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Last updated: May 25, 2022

Data Warehousing and Data Mining

Hey guys, doing something with data and you are confused about where to store and how to manage it. When dealing with data, we come across these techniques of managing data. So before going into these topics let's get to know what is data warehousing? and what is data mining? Data warehousing is a storehouse of data that we use for reporting and analyzing data. When dealing with business intelligence, we usually work with data warehouses as while dealing with businesses we have various types of data in bulk. Thus for storing such data we manage using data warehousing. Data mining is the method of processing the data for some patterns and characters that data depicts. In this article, we will be looking in-depth about what is data warehousing and mining. Don't worry we won't stop there until we teach you how to use data warehousing tools and data mining tools. We will go there slowly because slow and steady wins the race. Thus taking baby steps, we will first teach the architecture of data warehousing and mining. Then we will step further into what are its techniques and then we will illustrate various tools and also give you hands-on practice regarding these tools by giving from scratch information on one of the tools. I know it's a lot to take in at present but don't worry, we will be by your side with every step and ready for your questions, so ask them a lot.
Data Mining vs KDD
This article will cover all the related points covering data mining vs KDD. We will discuss data mining and KDD and then move to the differences.

Data Warehousing

Just as the word 'warehouse' means a place to store bulk produce or goods in large quantities. Adding the word, "data" means storing a large amount of data. Data Warehousing is the method used for analytical purposes and business reporting. It mainly involves collecting and managing the data from various sources and then using it for business purposes. This data is also used to extract business insights. There are various types of Data warehousing architecture such as single-tier, two-tier, and three-tier architecture.
Data Warehousing
In this article, we will learn about data warehouse, their architecture, design implementation, security, advantages, and disadvantages, and so on.
Characteristics of Data Warehouse MEDIUM
Data Warehouse Characteristics 1. Subject-Oriented 2. Integrated 3. Time-Variant 4. Non-Volatile 5. Designed for Decision Support
How does Data Warehousing Work?
In this article, we will understand the meaning and working of Data Warehousing. At the same time, we saw the features and uses of Data Warehousing.
Components of Data Warehouse EASY
In this article, we will discuss the components of a data warehouse.
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Data Lakes vs Data Warehouses EASY
In this article, we will be covering the difference between a Data lake and vs Data warehouse.
Databases vs Data Warehouses EASY
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Data Warehouse Architecture EASY
Data Warehouse Architecture is complex as it's an information system that contains historical and commutative data from multiple sources.
Introduction to Meta Data for Data Warehousing MEDIUM
Metadata is data that describes other data. In data warehousing, Metadata refers to information representing the characteristics and structure of the data present in the warehouse.
How are Data Warehouses Built?
This article discusses How are Data Warehouses Built?
Applications of data warehouse EASY
This article discusses Applications Of Data Warehousing.
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Data Mining

Data mining is the practice of analyzing large databases to generate new information from them. It is also uncovering the patterns or hidden data from the given databases and analyzing them. It combines statistical and artificial intelligence to analyze these large data sets which we obtain using various techniques and then discover useful information from these. Using data warehousing and the growth of big data, and other such techniques of data mining helped in transforming the raw data into useful knowledge. It involves the discussion of various data mining techniques and architectures.
What Is Data Mining? MEDIUM
This article will take you through Data Mining, its importance, and the different tasks, advantages, disadvantages, limitations, and applications of data mining.
Data Mining vs AI EASY
In this article, we will first discuss data mining and Artificial Intelligence with their types, pros, and cons. Then the difference between data mining and AI.
Data Mining vs Text Mining
This article will cover all the related points covering data mining vs text mining. We will discuss data mining and text mining and then move to the differences.
Data Mining vs Machine Learning EASY
In this article, we will cover the differences between data mining and machine learning.
Data Mining vs Big Data
In this article, we will cover the difference between data mining and big data. We will start our discussion by understanding data mining and big data.
Scope of Data Mining EASY
This blog introduces data mining, its scope, and its benefits.
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This article will take you through the concepts, techniques, practical applications, and a code example of outlier analysis in data mining.
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In this blog,  we will study the working of FP algorithms in detail, along with their advantages and disadvantages.
The Data Mining Process EASY
This article on Data Mining Process Covers Data Mining Models, Steps and Challenges Involved in the Data Extraction Process.
Data Mining Architecture EASY
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Discretization in Data Mining EASY
In this blog, we will learn about Discretization in Data Mining. We will understand its core concepts, its usage, advantages, and much more for better understanding.
Data Mining vs Web Mining EASY
In this blog, we will be discussing the features and applications of Data Mining and Web Mining followed by the differences between them.
Data Mining vs Data Snooping
In this article, we will first discuss data mining and data snooping with their types, pros, and cons. Then the difference between data mining and data snooping.
Data Mining vs OLAP EASY
In this blog, we will be discussing the features and applications of Data Mining and Olap followed by the differences between them.
Data Mining vs Deep Learning
In this article, we will first discuss data mining and deep learning with their types, pros, and cons. Then the difference between data mining and deep learning.
Data Mining vs Data Profiling
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Types Of Data Mining Architecture EASY
In this article, we will learn about Data Mining Architecture, its types, advantages, and disadvantages.
​​Bayesian Classification in Data Mining EASY
This article explains the Bayesian Classification in data mining. It involves predicting a class label for a given input by utilizing probability theory.
Data Cube in Data Mining EASY
This guide aims to simplify and explain data cubes, their significance, and how they're used in data mining.
KDD in Data Mining EASY
KDD is a method of extracting relevant, unknown, and useful data from massive databases(also known as big data).
Outlier Detection in Data Mining EASY
In this blog, we will learn about the different methods of Outlier Detection in Data Mining, along with their application and challenges.
Detecting Phishing in Data Mining MEDIUM
In this article, we will discuss what Phishing is, the method of detecting phishing in Data Mining, along with the Random Forest Algorithm.
Data Mining Vs Data Analytics EASY
This blog explains the detailed difference between Data Mining and Data Analytics.
Fake News Detection Project in Data Mining MEDIUM
This article will discuss the problem of fake news circulation in social media and its possible solutions using fake news detection project in data mining.
What is Regression in Data Mining? EASY
This article will discuss Regression in data mining and types of regressions in detail, along with their equations and graphs and some applications of Regression.
Data Mining Vs Data Warehousing EASY
This blog contains an introduction to data mining and data warehouse and their differences.
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Applications of Data Mining
This article on Applications of Data Minings Covers Data Mining and its applications in various fields.
Credit Card Fraud Detection Project in Data Mining MEDIUM
This article covers the concept of credit card fraud detection project in data mining with its implementation.
Clustering in Data Mining MEDIUM
Clustering is a technique in data mining that groups similar data points together based on their features and characteristics. This article will teach you all you need to know about clustering in data mining

Data Mining Algorithms

A very important station has arrived. You better sit tight and take your pens out. You might be well versed with the word "Algorithms". Now we will introduce to you what is the data mining algorithm. There are many but here we will discuss a few important ones. Let's first take a look at the part about choosing the right algorithm. What and how can one choose the right algorithm? Later we will be doing one-to-one discussions on these algorithms. We will briefly explain what it is and then dive into the mathematical details.
Top 10 Common Data Mining Algorithms
Choosing The Right Algorithm in Data Mining EASY
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C4.5 Algorithm in Data Mining EASY
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K-mean Algorithm EASY
In this blog, We will see how implementation and theory of the K-mean Algorithm.
Support Vector Machines EASY
This article will try to improve your knowledge about the concept of Support Vector Machines. How do they work, what is the math behind them, etc.?
Apriori Algorithm EASY
In this blog, We will see how implementation and theory of the Apriori Algorithm.
Expectation-Maximization Algorithm EASY
This article will mainly give an overview of Expectation-Maximization, how it works, and why it is going to be used in different applications. We will also discuss the math included in it.
PageRank Algorithm
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AdaBoost Algorithm
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K-Nearest Neighbour Algorithm
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Naive Bayes Algorithm EASY
This blog will discuss the Naive Bayes Algorithm and its Working, implementation. We will also discuss the Pros and cons, Applications, and Types of the Naive Bayes Algorithm.
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This blog describes the Classification and Regression Tree(CART) Algorithm. We will discuss the Decision Tree, the Category of the CART algorithm, the Working of the CART algorithm, and its advantages.