Data Science Course > Project

Preparing Final Dashboard

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The final dashboard is the final document that is created by the process of data visualization. The purpose of a final dashboard is to present a clear and concise visual representation of the data in a way that can be easily understood.

The first step is to create a final dashboard. This is the layout that you want your dashboard to be in. It should be easy to read and understand. This will help you decide what tools, charts, and graphs you need for your final dashboard. You will also know where these tools are located on your computer so that you can easily access them when the time comes for publishing the final dashboard.

Dashboards are essential in data science. They are a visual representation of the data collected and help in making decisions. Data scientists use dashboards to help them make sense of their data and understand what they need to do next. Dashboards are also used to show the progress of their projects and identify trends in data.

Dashboards are a great way to keep track of your progress in different projects. They can be used to monitor progress, provide insight into the project’s status, and make it easier for people involved to make decisions.

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V
vaibhav
5

best

A
Anjali
5

Excellent explanation

A
ashif mahiboob yadwad
5

Very good

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

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ANKiT KUMAR BAMNiYA
4

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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
    5m 27s
  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
    7m 48s
  4. Summary of ANOVA in Data Science
    3m 11s
  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
    9m 24s

  1. Understanding Datamining in Data Science
    7m 33s

  1. Introduction to Simple Linear Regression and Correlation in Data Science
    7m 28s
  2. Least Square and Residual Analysis in Data Science
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  3. Test assumption of Regression Model in Data Science
    7m 3s
  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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