Data Science Course > Project

Presenting Dashboard

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A dashboard is a graphical user interface that presents data in the form of charts, graphs, and tables. The main purpose of this type of dashboard is to provide a quick overview of the data and trends.

Dashboards are often used by managers to monitor their business and employees. They can also be utilized by sales managers to track their performance, or by HR managers for employee performance.

Dashboards are a way of displaying data that is easy to interpret. They are typically used in business analytics, and they can be used to help make sense of data.

Data science dashboards provide a way for companies to get insight into their data. They can use the insights from these dashboards to make better decisions, improve performance, and drive growth.

The following are some of the uses cases of dashboards:

  • Data scientists use them to monitor and analyze their progress.
  • Marketing teams can use them to visualize marketing insights and customer behavior trends.
  • IT professionals can use them to monitor their network operations and identify network issues.

Learner's Ratings

4.4

Overall Rating

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Reviews

S
SHAIKH TOHEED
5

good but practical knowledge not perfect

P
PUSHPAK DILLIP MALI
4

The content and teaching style is excellent. But there is slight issue is that video quality is kind of poor the dashboard is blur on 720mp on PC.

S
Sayali Jadhav
4

Very useful all data

S
Syed Mohammad Qaiser Rizvi
5

Way of teaching is very good. I want to get PPT which he is using to teach us ?

A
Anuja Bagadi
5

It is a very interactive and useful course..

K
Kashinath Myakale
5

All courses is very useful for which are looking free courses and gaining knowledge

A
ANKiT KUMAR BAMNiYA
4

SC

D
DOGALA UDAYKUMAR
5

OK

A
Anuj Jehta
5

nice

N
Nishant Saxena
5

great content

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Course Content

  1. Understanding of Data Science
    7m 33s
  2. How to use LearnVern
    3m 20s

  1. Introduction to Data Measurement in Data Science
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  2. Types of Variables in Data Science
    2m 26s
  3. Levels of Data Measurement in Data Science
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  1. Charts and Graphs in Data Science
    4m 2s
  2. Frequency Distribution in Data Science
    5m 46s
  3. Bar Graphs and Pie Charts in Data Science
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  4. Summarizing Quantitative Data in Data Science
    7m 25s
  5. Cummulative Distribution in Data science
    8m 4s
  6. Histogram and Scatter Plot in Data Science
    7m 42s
  7. Cross Tabulation in Data Science
    6m 13s

  1. Measures of Central Tendency and Variability in Data Science
    6m 51s
  2. Mean and Percentiles in Data Science
    5m 32s
  3. Quartiles in Data Science
    2m 38s
  4. Measures of Variability in Data Science
    4m 56s
  5. Variance and Standard Deviation in Data Science
    7m 30s
  6. Application of Standard Deviation in Data Science
    5m 40s
  7. Z-score and Co-efficient of Variation in Data Science
    5m 23s
  8. Measures of Central Tendency for Grouped Data in Data Science
    3m 59s
  9. Standard Deviation and Variance for Grouped Data in Data Science
    11m 21s

  1. Skewness in Data Science
    5m 25s
  2. Kurtosis in Data Science
    3m 48s
  3. Box and Whisker Plots in Data Science
    3m 45s

  1. Hypothesis Testing in Data Science
    6m 33s
  2. Statistical Hypothesis in Data Science
    6m 30s
  3. Errors in Statistical Hypothesis in Data Science
    5m 45s
  4. Statistical Inference for Single Population in Data Science
    7m 11s
  5. Testing Example in Data Science
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  6. Statistical Inference with Two Population in Data Science
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  1. R Programming in Data Science
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  2. Data Types in R in Data Science
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  3. Understanding R Console and R Studio in Data Science
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  1. Analysis of Variance and Annova in Data Science
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  2. Assumption and Variation in One-Way ANOVA in Data Science
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  3. Two way ANOVA in Data Science
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  4. Summary of ANOVA in Data Science
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  5. ANOVA example in Data Science
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  6. One way ANOVA Example in Data Science
    4m 7s
  7. Two way ANOVA Example in Data Science
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  1. Understanding Datamining in Data Science
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  1. Introduction to Simple Linear Regression and Correlation in Data Science
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  2. Least Square and Residual Analysis in Data Science
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  3. Test assumption of Regression Model in Data Science
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  4. Hypothesis and Co-Efficient of Determination in Data Science
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  5. Regression Example - Creating Model in Data Science
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  6. Residual Analaysis of Model example in Data Science
    9m 7s
  7. Residual Analysis of Model Part 2 in Data Science
    8m 10s
  8. Residual Analysis of Model Part 3 in Data Science
    6m 38s

  1. Understanding Clustering in Data Science
    5m 58s
  2. Hierarchical Clustering in Data Science
    8m 33s
  3. Example of Hierarchial Clustering in Data Science
    13m 39s
  4. K Means Clustering in Data Science
    8m 6s
  5. Example of K Means Clustering in Data Science
    9m 41s

  1. Theoretical Concepts of Dashboard
    5m 41s
  2. Creating Pivots and Preparing Charts
    5m 36s
  3. Slicers in Dashboard
    3m 27s
  4. Preparing Final Dashboard
    8m 20s
  5. Presenting Dashboard
    3m 2s
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