Data Mining Fundamentals For Beginners

Data Mining Fundamentals For Beginners, available at $44.99, has an average rating of 4.15, with 45 lectures, based on 46 reviews, and has 301 subscribers.

You will learn about Learn the fundamentals of data mining Learn professional techniques for data engineering Learn tools such as R Studio, Rapid Miner and Jupyter Notebooks This course is ideal for individuals who are Anyone who wants to learn data mining and data engineering will find this course very useful It is particularly useful for Anyone who wants to learn data mining and data engineering will find this course very useful.

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Summary

Title: Data Mining Fundamentals For Beginners

Price: $44.99

Average Rating: 4.15

Number of Lectures: 45

Number of Published Lectures: 45

Number of Curriculum Items: 45

Number of Published Curriculum Objects: 45

Original Price: $39.99

Quality Status: approved

Status: Live

What You Will Learn

Learn the fundamentals of data mining

Learn professional techniques for data engineering

Learn tools such as R Studio, Rapid Miner and Jupyter Notebooks

Who Should Attend

Anyone who wants to learn data mining and data engineering will find this course very useful

Target Audiences

Anyone who wants to learn data mining and data engineering will find this course very useful

Become a complete Data Engineer from scratch!!

Data mining is one of the key elements of data science that focuses on real-time implementation of data collection & analysis. It is important for designing & building pipelines that help in transforming & transporting data into a usable format.

This may sound simple, but it requires a lot more skills, time & hard work. And still, for many, the idea of data engineering remains fuzzy that has significantly contributed to the huge skill gaps.

In order to make the concept of data engineering clear & to help individuals become an expert data engineer, we have curated this course. This Online Data Engineering Course will help you to master all the underlying concepts, tools & technologies of data engineering.

Why you should learn Data Mining?

Focuses more on the implementation & harvesting of data.

Designing and building pipelines that can transform data into a usable form.

Helps in maintain data uniformity.

You will be able to design, manage & optimize the data flow with databases.

Database oriented job.

Why you should take this course?

Data engineering is one of the most misunderstood parts of data science. Moreover, data scientists are often confused with data engineers. However, both have separate roles & responsibilities. Data engineers are more oriented towards database & data harvesting, contrary to advanced data analysis or experimental designs.

In order to help you become a data engineer, we have curated this exclusive course that will be entirely dedicated to all the concepts involved in data engineering. This course also includes projects that will help you with a comprehensive understanding.

What You Will Learn?

Data mining & its tools

Using various software involved in Data Engineering

Data reduction with R, Python & RapidMiner

Classification with R, Python & RapidMiner

Clustering with R, Python & RapidMiner

Anomaly detection with R, Python & RapidMiner

Association analysis with R, Python & RapidMiner

Regression analysis with R, Python & RapidMiner

Text mining with R, Python & RapidMiner

Sequence mining with R, Python & RapidMiner

Data reduction with R, Python & RapidMiner

Projects for real-time implementation

Begin with this online course to understand all the underlying concepts of data engineering from scratch!!

Course Curriculum

Chapter 1: Introduction and Setup

Lecture 1: What is Data Mining

Lecture 2: Overview of Softwares Involved

Lecture 3: Installing R and R-Studio

Lecture 4: Installing Rapid Miner

Lecture 5: Installing Python and Jupyter Notebooks

Chapter 2: Data Mining Standard Processes & Models

Lecture 1: KDD – Knowledge Discovery in Databases

Lecture 2: SEMMA

Lecture 3: CRISP-DM

Lecture 4: A Review of Processes

Lecture 5: TDSP – Team Data Science Process

Chapter 3: How to Use the Software

Lecture 1: Overview of R Studio

Lecture 2: Overview of Jupyter Notebooks

Lecture 3: Rapid Miner Overview

Chapter 4: Data Reduction

Lecture 1: What is Data Reduction

Lecture 2: Data Reduction in R

Lecture 3: Data Reduction in Python

Lecture 4: 4.4 – Data Reduction in RapidMiner

Chapter 5: Clustering

Lecture 1: What is Clustering

Lecture 2: Clustering in R

Lecture 3: Clustering in Python

Lecture 4: Clustering in RapidMiner

Chapter 6: Classification

Lecture 1: What is Classification

Lecture 2: Classification In R

Lecture 3: Classification in Python

Lecture 4: Classification in RapidMiner

Chapter 7: Anomaly Detection

Lecture 1: What is Anomaly Detection

Lecture 2: Anomaly Detection in R

Lecture 3: Anomaly Detection in Python

Lecture 4: Anomaly Detection in RapidMiner

Chapter 8: Association analysis

Lecture 1: Association Analysis

Lecture 2: Association Analysis in R

Lecture 3: Association Analysis in Python

Lecture 4: Association Analysis in RapidMiner

Chapter 9: Regression analysis

Lecture 1: What is Regression analysis

Lecture 2: Regression analysis In R

Lecture 3: Regression analysis in Python

Lecture 4: Regression analysis in RapidMiner

Chapter 10: Sequence Mining

Lecture 1: What is Sequence mining

Lecture 2: Sequence mining In R

Lecture 3: Sequence mining in Python

Lecture 4: Sequence mining in RapidMiner

Chapter 11: Text mining

Lecture 1: What is Text mining

Lecture 2: Text mining In R

Lecture 3: Text mining in Python

Lecture 4: Text mining in RapidMiner

Instructors

Eduonix Learning Solutions
1+ Million Students Worldwide | 200+ Courses

Eduonix-Tech .

Rating Distribution

1 stars: 1 votes

2 stars: 1 votes

3 stars: 5 votes

4 stars: 15 votes

5 stars: 24 votes

Frequently Asked Questions

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Can I take my courses with me wherever I go?

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