Data Mining

Learn Data Mining from basics in this free online training. This free Data Mining course is taught hands-on by experts. Learn about Data Description, Data Manipulation, Skewness & a lot more. Best for Beginners. Start now!

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intermediate
Intermediate

What you learn in Data Mining ?

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Visualising
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Regression Analysis

About this Free Certificate Course

This Data Mining course will introduce you to prominent Data Mining concepts. The course begins by introducing you to data description concepts. You will understand the basics of data, data manipulation, and skewness using histograms in the first half of the course. You will then learn to visualize outliers using boxplots, correlation using scatter plots, and understand what machine learning is. You will also understand regression analysis, multiple linear regression, and logistic regression, with demonstrated examples in the latter part of this course. There is an assessment to evaluate your knowledge at the end of the course. Complete the course for free and avail your certificate. You can also study the attached materials for reference.

After this free, self-paced, intermediate-level guide to Data Mining, you can enroll in theData Science courseand embark on your career with the professional Post Graduate certificate. Learn various concepts in depth with millions of aspirants across the globe!

Course Outline

Data Description

You will learn mathematics concepts for data mining tasks such as statistics, its types, population, parameter, sample, mean, median, mode, normal distribution, interquartile range of IQR, and its upper and lower limits. This section comprehends a demonstration of the outlier concept at the end of the course for your better understanding.

Basic Data Understanding

你應當明白data and learn to infer insights from the datasets using the diabetes dataset in this section.

Data Manipulation

This section explains how to work with or manipulate the data with different methods in a given set to extract a particular range of values. You will also understand how a dataset not showing accurate data can be recognized and be replaced with the median since it does not get affected by outliers.

Skewness using Histogram/ Density Curves

你應當明白the outlier concept in-depth in this section. You will learn to detect and impute outliers and understand their working later in this section. You will also learn to infer/express data using the histogram.

Visualising outliers using boxplots

You will learn to express missing data and express data in box plots for simple representation and also understand outlier analysis concepts in this section.

Visualising correlation using Scatter Plots/ Heat map

You will learn to represent correlation with different methods and scatter plots or heat maps using automobile dataset to perform exploratory data analysis in this section.

What is Machine Learning?

This module begins by defining machine learning. It then discusses how a machine understands the tasks with examples and explains supervised and unsupervised learning concepts in machine learning.

Introduction to Regression Analysis

This section shall define regression, brief different types of regression, and then explain what regression analysis is in machine learning. You will learn to work with regression analysis to understand the data better.

Linear Regression Demo

This section shall explain simple linear regression. You will learn to import classes and packages and work with Google Colaboratory to understand linear regression better.

Multiple Linear Regression Demo

You will understand the concept of multiple data points, to begin with in this section and then learn to work with multiple linear regression with NumPy.

Salary Prediction Demo

You will learn to work with the dataset by understanding a project on Salary Prediction. You will also learn to work with NumPy, Pandas, and Matplotlib library for the project.

Introduction to Logistic Regression

You will learn a supervised learning technique to classify the data based on the classifying points and logistic regression in this section.

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Data Mining

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Frequently Asked Questions

What are the prerequisites required to learn the Data Mining course?

數據挖掘是一個中級課程。之前e you begin with this course, you will have to do a little homework on data science if you do not have a thorough understanding of it.

How long does it take to complete this free Data Mining certificate course?

This free certificate course is 2.5-hours long. You can learn Data Mining concepts and work at your convenience to understand the subject since the course is self-paced.

Will I have lifetime access to this free course?

Yes, once you enroll in this Data Mining course, you will have lifetime access to this Great Learning Academy's free course. You can log in and learn at your leisure.

What are my next learning options after I complete Data Mining?

Once you complete this free course, you can follow up with the data mining process and data mining applications, or you can also opt for aMaster's in Data Sciencethat will aid in advancing your career growth in this leading field

Is it worth learning Data Mining?

Yes, it is beneficial to learn Data Mining. Different techniques are used to understand data to work on projects and building models. These techniques contribute to analyzing data through identifying patterns and relationships to solve business problems.

What are Data Mining tools used for?

Data mining tools help data miners discover patterns, trends, and groupings within a huge dataset and transform the data into more structured information. Popular data mining tools include MonkeyLearn, RapidMiner, Oracle Data Mining, and IBM SPSS Modeler.

Why is Data Mining so popular?

Data mining is a simple technique that opens data science, artificial intelligence, and machine learning professionals to business opportunities since it can be leveraged for predictive and descriptive abilities. The predictive and descriptive capabilities of data mining can predict the future trend and also heighten profits.

Will I get a certificate after completing this free Data Mining course?

Yes, you will get a certificate of completion for the Data Mining course after completing all the modules and cracking the assessment/quiz. All the assessment tests your knowledge and badges your skills.

What knowledge and skills will I gain upon completing this course?

You will understand data manipulation, skewness, visualization, machine learning, and regression analysis concepts. You will learn to work with different representation techniques for visualization purposes and be able to work on projects efficiently after you have completed this data mining course.

How much does this Data Mining course cost?

It is an entirely free course from Great Learning Academy. Anyone interested in learning data mining techniques for data science, big data, artificial intelligence, and machine learning concepts can get started with this course. You can also refer to the attached materials for additional knowledge.

Is there any limit on how many times I can take this free course?

Once you enroll in the Data Mining course, you have lifetime access to it. So, you can log in anytime and learn it for free online at your convenience.

Can I sign up for multiple courses from Great Learning Academy at the same time?

Yes, you can enroll in as many courses as you want from Great Learning Academy. There is no stricture to the number of courses you can enroll in at once, but since the courses offered by Great Learning Academy are free, we suggest you learn one by one to get the best out of the subject.

Why choose Great Learning Academy for this Data Mining course?

Great Learning is a global educational technology platform committed to developing skilled professionals. Great Learning Academy is a Great Learning project that provides free online courses to assist people in succeeding in their careers. Great Learning Academy's free online courses have helped over 4 million students from 140 countries. It's a one-stop destination for all of a student's needs.

This course is free and self-paced, but it also includes solved problems, demonstrated codes, sample projects, and presented examples to help you comprehend the numerous areas that fall under the subject and awards you a certificate to showcase your skills. The course is conducted by topic experts and is carefully tailored to cater to both beginners and professionals.

Who is eligible to take this course?

Anybody interested in learning the Data Mining concepts and techniques for Data Science and AIML can take up the course. So, enroll in our Data Mining course today and learn it for free online.

What are the steps to enroll in this Data Mining course?

Enrolling in Great Learning Academy's Data Mining course is a simple and straightforward approach. You will have to sign-up with your E-Mail ID, enter your user details, and then you can start learning at your own pace.

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Data Mining Course

Data Mining, alternatively known as Knowledge Discovery in Database(KDD), is considered the most beneficial technique that helps researchers, entrepreneurs, and individuals to extract valuable insights from the collected dataset. It includes processes like Data cleaning, Data cleaning, integration, selection, transformation, mining, pattern evaluation, and knowledge presentation. This technique, where we extract the information to identify the trends, patterns, and helpful information that allows businesses to make data-driven decisions through Data Mining, is proven to be advantageous for their growth.

Data Mining can also be considered as a type of investigation where we try to find the hidden patterns and information from various categories of data collected. These data are stored in particular areas like data warehouses. Its efficient analysis and data mining algorithms help in decision-making and other helpful information, resulting in cost-cutting and generating revenue. Data Mining has advanced techniques to find the patterns and trends from the storage of large amounts of data. It utilizes complex mathematical algorithms to evaluate large data for future trend predictions. Many Businesses use Data Mining techniques to extract specific data from vast data storage.

Data Mining is similar to Data Science. The Data Mining process includes different types of services like text mining, video mining, web mining, social media mining, and pictorial data mining. Softwares are used to achieve Data Mining more efficiently. There are many high-end software through which you can locate data that is hard to find manually. Through Data Mining, you can predict your Business goals, identify data, prepare data, model and evaluate data, and present the data. You can perform Data Mining on various types of data like Relational Database. It is a collection of multiple data sets organized by tables, columns, and records. You can extract the required data and access the information you are looking for from the databases through these databases.

當你從databas搜索所需的數據es, tables convey and share the information that facilitates the data searching, reporting, and organization. Data Warehouse is also a type of data that helps in Data Mining. A data warehouse in a business is a technology that collects all the relevant data and provides valuable insights. Marketing and financial domains offer a lot of data from multiple places that can be stored in Data Warehouses. These extracted data are analyzed and are utilized for decision-making for businesses. Data Warehouses are designed for data analysis purposes. Many organizations are making use of Data Warehouses for data storage and analysis.

There are many advantages of Data Mining. Its techniques enable businesses to get knowledge-based information, and it also helps them make lucrative changes in the operation and production processes. Data Mining is more cost-efficient than other statistical applications. It also allows businesses to make crucial decisions regarding their growth. It helps them to uncover various patterns, trends, and behaviors. Data Mining can be done and implemented on the new system or the existing ones. Data Mining is quicker than other methods and helps you analyze enormous data sets in a shorter period. Data Mining is considered an excellent technique to analyze and manipulate data.

You can find many Data Mining applications in health care, education,fraud detection, CRM, manufacturing engineering, financial banking, lie detection, market basket analysis, and more. Data Mining is very useful for businesses with intense consumer demands such as retail, financial, communication, marketing agencies, sales, etc. It also helps the companies predict what products the customers need and their preferences. Data has the power to determine future events and to uncover them Data Mining plays an important role. It helps many organizations realize the method to develop and promote their data in favor of customers, which in return brings them revenue.

Data Mining also faces some challenges during its implementation. These challenges can be related to the techniques, data, methods, performance, etc. Data Mining is effective if you tackle all these challenges that arise during its execution appropriately. It must be correct and complete to get useful insights from the collected data. Sometimes these incomplete and noisy data create havoc while Data Mining as you will be dealing with a large amount of data. These problems may also occur because of human mistakes or the data measuring instruments. Sometimes these large data can be inaccurate and unreliable. It is difficult to collect data from customers who are unwilling to provide their information, making data incomplete.

To enter Data Mining in-depth, you must first get a brief introduction to Data Mining. You should go through various Data Mining examples to understand its mechanism. It will also help you face the challenges that come during its execution. These challenges may be due to incomplete and noisy data, data distribution, complex data, performance, data privacy and security, data visualization, and more. Data Mining involves refined data analysis tools, and it helps you find previously unknown patterns and relations in the vast data sets. The tools get help from statistical models, mathematical algorithms, and Machine Learning techniques like neural networks or decision trees to analyze these data. Hence, Data Mining involves analysis and prediction.

借助先進的統計,數學,and Machine Learning techniques, Data Mining has become more effective and efficient. It involves Machine Learning, database management, and statistics, and professionals aim to understand these techniques and how to process them in their favor. Many have made Data Mining their career, and it is also in demand as we live in a data-driven world. From the recentData Mining projects, developers have come across various Data Mining techniques like Classification, Clustering, Regression, Outer, Sequential Patterns, Prediction, and Association Rules. All these Data Mining techniques make Data Mining more efficient and effective, and it helps professionals improve Data Mining performances.

To learn all the types and techniques of Data Mining, enroll in a free Data Mining course offered by Great Learning. Register in this course and get in-depth knowledge of Data Mining and its mechanism. Complete the course to get free Data Mining certificate and grab more job opportunities.

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