Data Science Course > Project

Creating Pivots and Preparing Charts

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Pivot charts are helpful in understanding the data better and making decisions based on it. They can be used to identify patterns, trends, and outliers. They also provide visual representation of the changes over time or across different variables.

There are four types of pivots:

  • Strategic Pivot: This is when you change your business model and focus on a different industry.
  • Operational Pivot: This is when you change your company's operations so that it can better handle the changing market.
  • Marketing Pivot: This is when you change your marketing strategy to better target customers and increase sales.
  • Financial Pivot: This is when you change how you manage your finances, like changing from traditional bank loans to debt financing or going public with the company's stock.

The following are the steps that you should take before you prepare a chart for a pivot:

  • Set up your data collection and analysis process.
  • Identify the current situation in your business and prepare a forecast for what will happen in the future.
  • Determine what changes need to be made to stay competitive.
  • Create an action plan that outlines how you will implement those changes into your business model, organization, and data science process.
  • Prepare charts that show how these changes will affect different aspects of your business model and organization over time.
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Examples of pivots:

  • Linear Regression vs Exponential Regression: The linear regression model was used for many years until it was discovered that the exponential model could better predict the outcome of a given dataset.
  • The decision tree method is a way of predicting customer behavior based on given rules. The classifications and decisions that are made by the model can be used to take any given action, such as sending out an email or pushing a marketing campaign.

Pivots are a crucial part of data science. In order to create a pivot, you have to have an understanding of the data, your goal and the audience. There are many resources available online that can help you with creating pivots and preparing charts in data science.

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  2. How to use LearnVern
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  1. Introduction to Data Measurement in Data Science
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  2. Types of Variables in Data Science
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  3. Levels of Data Measurement in Data Science
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  1. Charts and Graphs in Data Science
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  2. Frequency Distribution in Data Science
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  3. Bar Graphs and Pie Charts in Data Science
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  4. Summarizing Quantitative Data in Data Science
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  5. Cummulative Distribution in Data science
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  6. Histogram and Scatter Plot in Data Science
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  7. Cross Tabulation in Data Science
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  1. Measures of Central Tendency and Variability in Data Science
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  2. Mean and Percentiles in Data Science
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  3. Quartiles in Data Science
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  4. Measures of Variability in Data Science
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  5. Variance and Standard Deviation in Data Science
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  6. Application of Standard Deviation in Data Science
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  7. Z-score and Co-efficient of Variation in Data Science
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  8. Measures of Central Tendency for Grouped Data in Data Science
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  9. Standard Deviation and Variance for Grouped Data in Data Science
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  1. Skewness in Data Science
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  2. Kurtosis in Data Science
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  3. Box and Whisker Plots in Data Science
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  1. Hypothesis Testing in Data Science
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  2. Statistical Hypothesis in Data Science
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  3. Errors in Statistical Hypothesis in Data Science
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  4. Statistical Inference for Single Population in Data Science
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  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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  6. One way ANOVA Example in Data Science
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  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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  6. Residual Analaysis of Model example in Data Science
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  7. Residual Analysis of Model Part 2 in Data Science
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  8. Residual Analysis of Model Part 3 in Data Science
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  1. Understanding Clustering in Data Science
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  2. Hierarchical Clustering in Data Science
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  3. Example of Hierarchial Clustering in Data Science
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  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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