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Course Content

Understanding of Data Science

How to use LearnVern

Introduction to Data Measurement in Data Science

Types of Variables in Data Science

Levels of Data Measurement in Data Science

Data Measurement Quiz

Charts and Graphs in Data Science

Frequency Distribution in Data Science

Bar Graphs and Pie Charts in Data Science

Summarizing Quantitative Data in Data Science

Cummulative Distribution in Data science

Histogram and Scatter Plot in Data Science

Cross Tabulation in Data Science

Charts and Graphs Quiz

Measures of Central Tendency and Variability in Data Science

Mean and Percentiles in Data Science

Quartiles in Data Science

Measures of Variability in Data Science

Variance and Standard Deviation in Data Science

Application of Standard Deviation in Data Science

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

Measures of Central Tendency Quiz

Measures of Central Tendency for Grouped Data in Data Science

Standard Deviation and Variance for Grouped Data in Data Science

Skewness in Data Science

Kurtosis in Data Science

Box and Whisker Plots in Data Science

Measures of Shapes Quiz

Hypothesis Testing in Data Science

Statistical Hypothesis in Data Science

Errors in Statistical Hypothesis in Data Science

Statistical Inference for Single Population in Data Science

Testing Example in Data Science

Statistical Inference with Two Population in Data Science

R Programming in Data Science

Data Types in R in Data Science

Understanding R Console and R Studio in Data Science

Analysis of Variance and Annova in Data Science

Assumption and Variation in One-Way ANOVA in Data Science

Two way ANOVA in Data Science

Summary of ANOVA in Data Science

ANOVA Quiz

ANOVA example in Data Science

One way ANOVA Example in Data Science

Two way ANOVA Example in Data Science

Understanding Datamining in Data Science

Introduction to Simple Linear Regression and Correlation in Data Science

Least Square and Residual Analysis in Data Science

Test assumption of Regression Model in Data Science

Hypothesis and Co-Efficient of Determination in Data Science

Regression Example - Creating Model in Data Science

Residual Analaysis of Model example in Data Science

Residual Analysis of Model Part 2 in Data Science

Residual Analysis of Model Part 3 in Data Science

Regression Quiz

Understanding Clustering in Data Science

Hierarchical Clustering in Data Science

Example of Hierarchial Clustering in Data Science

K Means Clustering in Data Science

Example of K Means Clustering in Data Science

Clustering Quiz

Theoretical Concepts of Dashboard

Creating Pivots and Preparing Charts

Slicers in Dashboard

Preparing Final Dashboard

Presenting Dashboard

Understanding Clustering in Data Science

Data Science Course Clustering

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Discussion

Course Content

Understanding of Data Science

How to use LearnVern

Introduction to Data Measurement in Data Science

Types of Variables in Data Science

Levels of Data Measurement in Data Science

Charts and Graphs in Data Science

Frequency Distribution in Data Science

Bar Graphs and Pie Charts in Data Science

Summarizing Quantitative Data in Data Science

Cummulative Distribution in Data science

Histogram and Scatter Plot in Data Science

Cross Tabulation in Data Science

Measures of Central Tendency and Variability in Data Science

Mean and Percentiles in Data Science

Quartiles in Data Science

Measures of Variability in Data Science

Variance and Standard Deviation in Data Science

Application of Standard Deviation in Data Science

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

Measures of Central Tendency for Grouped Data in Data Science

Standard Deviation and Variance for Grouped Data in Data Science

Skewness in Data Science

Kurtosis in Data Science

Box and Whisker Plots in Data Science

Hypothesis Testing in Data Science

Statistical Hypothesis in Data Science

Errors in Statistical Hypothesis in Data Science

Statistical Inference for Single Population in Data Science

Testing Example in Data Science

Statistical Inference with Two Population in Data Science

R Programming in Data Science

Data Types in R in Data Science

Understanding R Console and R Studio in Data Science

Analysis of Variance and Annova in Data Science

Assumption and Variation in One-Way ANOVA in Data Science

Two way ANOVA in Data Science

Summary of ANOVA in Data Science

ANOVA example in Data Science

One way ANOVA Example in Data Science

Two way ANOVA Example in Data Science

Understanding Datamining in Data Science

Introduction to Simple Linear Regression and Correlation in Data Science

Least Square and Residual Analysis in Data Science

Test assumption of Regression Model in Data Science

Hypothesis and Co-Efficient of Determination in Data Science

Regression Example - Creating Model in Data Science

Residual Analaysis of Model example in Data Science

Residual Analysis of Model Part 2 in Data Science

Residual Analysis of Model Part 3 in Data Science

Understanding Clustering in Data Science

Hierarchical Clustering in Data Science

Example of Hierarchial Clustering in Data Science

K Means Clustering in Data Science

Example of K Means Clustering in Data Science

Theoretical Concepts of Dashboard

Creating Pivots and Preparing Charts

Slicers in Dashboard

Preparing Final Dashboard

Presenting Dashboard

FAQs

The higher the similarity level, the more similar each cluster's observations are. The closer the observations in each cluster are, the lower the distance level. The clusters should, in theory, have a high level of similarity and a low level of distance.

Cluster analysis, often known as clustering, is the problem of arranging a set of items so that objects in the same group (called a cluster) are more comparable (in some sense) to those in other groups (clusters).

Clustering is the process of identifying unique groupings or "clusters" within a data set. The programme constructs groups using a machine language algorithm, and items in a comparable group will have similar features in general.

Data Science is a field that deals with the collection, processing, and analysis of data. There are many different clustering methods used in Data Science. The three most common types of clustering methods: hierarchical clustering, k-means clustering, and divisive hierarchical clustering.

Clustering allows us to find groups of similar items or people who are more likely to share certain attributes or behaviors. It is also helpful for finding outliers among other groups as well as identifying trends that may not have been noticed before.

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