What are some common tools used for data mining?

Some of the common tools that are used for data mining are: Data visualization tools: These tools help users to understand the information that they have gathered through data mining. They use visuals to represent complex datasets and make them easy to digest. Data analytics tools: These tools help organizations to gather insights from their data by using predictive modeling, machine learning, and artificial intelligence. Data preparation software: This software are used for cleaning raw data before it can be analyzed by other tools in the toolkit.

Course Content

Understanding of Data Science

7m 33s

How to use LearnVern

3m 20s

Introduction to Data Measurement in Data Science

10m 6s

Types of Variables in Data Science

2m 26s

Levels of Data Measurement in Data Science

10m 25s

Data Measurement Quiz

Charts and Graphs in Data Science

4m 2s

Frequency Distribution in Data Science

5m 46s

Bar Graphs and Pie Charts in Data Science

9m 1s

Summarizing Quantitative Data in Data Science

7m 25s

Cummulative Distribution in Data science

8m 4s

Histogram and Scatter Plot in Data Science

7m 42s

Cross Tabulation in Data Science

6m 13s

Charts and Graphs Quiz

Measures of Central Tendency and Variability in Data Science

6m 51s

Mean and Percentiles in Data Science

5m 32s

Quartiles in Data Science

2m 38s

Measures of Variability in Data Science

4m 56s

Variance and Standard Deviation in Data Science

7m 30s

Application of Standard Deviation in Data Science

5m 40s

Z-score and Co-efficient of Variation in Data Science

5m 23s

Measures of Central Tendency Quiz

Measures of Central Tendency for Grouped Data in Data Science

3m 59s

Standard Deviation and Variance for Grouped Data in Data Science

11m 21s

Skewness in Data Science

5m 25s

Kurtosis in Data Science

3m 48s

Box and Whisker Plots in Data Science

3m 45s

Measures of Shapes Quiz

Hypothesis Testing in Data Science

6m 33s

Statistical Hypothesis in Data Science

6m 30s

Errors in Statistical Hypothesis in Data Science

5m 45s

Statistical Inference for Single Population in Data Science

7m 11s

Testing Example in Data Science

5m 27s

Statistical Inference with Two Population in Data Science

8m 33s

R Programming in Data Science

5m 5s

Data Types in R in Data Science

3m 51s

Understanding R Console and R Studio in Data Science

7m 14s

Analysis of Variance and Annova in Data Science

3m 55s

Assumption and Variation in One-Way ANOVA in Data Science

7m 50s

Two way ANOVA in Data Science

7m 48s

Summary of ANOVA in Data Science

3m 11s

ANOVA Quiz

ANOVA example in Data Science

10m 50s

One way ANOVA Example in Data Science

4m 7s

Two way ANOVA Example in Data Science

9m 24s

Understanding Datamining in Data Science

7m 33s

Introduction to Simple Linear Regression and Correlation in Data Science

7m 28s

Least Square and Residual Analysis in Data Science

7m 15s

Test assumption of Regression Model in Data Science

7m 3s

Hypothesis and Co-Efficient of Determination in Data Science

4m 9s

Regression Example - Creating Model in Data Science

12m 1s

Residual Analaysis of Model example in Data Science

9m 7s

Residual Analysis of Model Part 2 in Data Science

8m 10s

Residual Analysis of Model Part 3 in Data Science

6m 38s

Regression Quiz

Understanding Clustering in Data Science

5m 58s

Hierarchical Clustering in Data Science

8m 33s

Example of Hierarchial Clustering in Data Science

13m 39s

K Means Clustering in Data Science

8m 6s

Example of K Means Clustering in Data Science

9m 41s

Clustering Quiz

Theoretical Concepts of Dashboard

5m 41s

Creating Pivots and Preparing Charts

5m 36s

Slicers in Dashboard

3m 27s

Preparing Final Dashboard

8m 20s

Presenting Dashboard

3m 2s

Understanding Datamining in Data Science

Data Science Course Introduction to Datamining

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Discussion

Course Content

Understanding of Data Science

7m 33s

How to use LearnVern

3m 20s

Introduction to Data Measurement in Data Science

10m 6s

Types of Variables in Data Science

2m 26s

Levels of Data Measurement in Data Science

10m 25s

Charts and Graphs in Data Science

4m 2s

Frequency Distribution in Data Science

5m 46s

Bar Graphs and Pie Charts in Data Science

9m 1s

Summarizing Quantitative Data in Data Science

7m 25s

Cummulative Distribution in Data science

8m 4s

Histogram and Scatter Plot in Data Science

7m 42s

Cross Tabulation in Data Science

6m 13s

Measures of Central Tendency and Variability in Data Science

6m 51s

Mean and Percentiles in Data Science

5m 32s

Quartiles in Data Science

2m 38s

Measures of Variability in Data Science

4m 56s

Variance and Standard Deviation in Data Science

7m 30s

Application of Standard Deviation in Data Science

5m 40s

Z-score and Co-efficient of Variation in Data Science

5m 23s

Measures of Central Tendency for Grouped Data in Data Science

3m 59s

Standard Deviation and Variance for Grouped Data in Data Science

11m 21s

Skewness in Data Science

5m 25s

Kurtosis in Data Science

3m 48s

Box and Whisker Plots in Data Science

3m 45s

Hypothesis Testing in Data Science

6m 33s

Statistical Hypothesis in Data Science

6m 30s

Errors in Statistical Hypothesis in Data Science

5m 45s

Statistical Inference for Single Population in Data Science

7m 11s

Testing Example in Data Science

5m 27s

Statistical Inference with Two Population in Data Science

8m 33s

R Programming in Data Science

5m 5s

Data Types in R in Data Science

3m 51s

Understanding R Console and R Studio in Data Science

7m 14s

Analysis of Variance and Annova in Data Science

3m 55s

Assumption and Variation in One-Way ANOVA in Data Science

7m 50s

Two way ANOVA in Data Science

7m 48s

Summary of ANOVA in Data Science

3m 11s

ANOVA example in Data Science

10m 50s

One way ANOVA Example in Data Science

4m 7s

Two way ANOVA Example in Data Science

9m 24s

Understanding Datamining in Data Science

7m 33s

Introduction to Simple Linear Regression and Correlation in Data Science

7m 28s

Least Square and Residual Analysis in Data Science

7m 15s

Test assumption of Regression Model in Data Science

7m 3s

Hypothesis and Co-Efficient of Determination in Data Science

4m 9s

Regression Example - Creating Model in Data Science

12m 1s

Residual Analaysis of Model example in Data Science

9m 7s

Residual Analysis of Model Part 2 in Data Science

8m 10s

Residual Analysis of Model Part 3 in Data Science

6m 38s

Understanding Clustering in Data Science

5m 58s

Hierarchical Clustering in Data Science

8m 33s

Example of Hierarchial Clustering in Data Science

13m 39s

K Means Clustering in Data Science

8m 6s

Example of K Means Clustering in Data Science

9m 41s

Theoretical Concepts of Dashboard

5m 41s

Creating Pivots and Preparing Charts

5m 36s

Slicers in Dashboard

3m 27s

Preparing Final Dashboard

8m 20s

Presenting Dashboard

3m 2s

FAQs

Exploring and analysing enormous chunks of data to find relevant patterns and trends is what data mining is all about. It can be used for a variety of purposes, including database marketing, credit risk management, fraud detection, spam email screening, and even determining user attitude.

Data science is a multidisciplinary strategy that combines analytical methodologies, subject expertise, and technology to uncover, extract, and surface patterns in data. Data mining, forecasting, machine learning, predictive analytics, statistics, and text analytics are all examples of this method.

Data Analytics is aimed to reveal the particular of extracted insights, whereas Data Science focuses on uncovering significant correlations between vast datasets. To put it another way, Data Analytics is a subset of Data Science that focuses on more detailed solutions to the issues that Data Science raises.

Some of the common tools that are used for data mining are:

Data visualization tools: These tools help users to understand the information that they have gathered through data mining. They use visuals to represent complex datasets and make them easy to digest.
Data analytics tools: These tools help organizations to gather insights from their data by using predictive modeling, machine learning, and artificial intelligence.
Data preparation software: This software are used for cleaning raw data before it can be analyzed by other tools in the toolkit.

Text mining, also known as information data mining, is the act of converting unstructured text into a structured format in order to uncover new insights and patterns.