Data Analytics Communication

Data Analytics Communication

Data Analytics Communication is the last step where the analyzed data is formally presented to stakeholders. Use of several data visualization techniques makes communication effective. 

Data analysis can be presented in various forms: –

  • Visual Graphs
  • Plotting maps
  • Reports
  • Whitepapers reports
  • PowerPoint presentations

Data Visualization

Data visualization techniques are used for graphical representation of data and are powerful tools for effective communication of data.

Benefits of data visualization:

  • Simplifies complex quantitative information through visuals.
  • Identifies the relationship between data points and variables.
  • Identifies patterns in data
  • Establishes trends present in the data and guides the analysis process.

Examples of data visualization:

  • If you want to present information about new and existing customers on the website and their behavior when they access the website.
  • If you want to see web traffic patterns for the website, for example, more activity on the website in the morning than in the evening.


Plotting is a data visualization technique used to represent underlying data through graphics. This is used often in data analytics. It can be understood as a coordinate system, like X-axis and Y-axis that represent variables, and you can mark your data based on the dataset and a mathematical equation.

Data Analytics Communication

Features of plotting:

  • Plotting is like telling a story about data using different colors, shapes, and sizes.
  • Plotting shows the relationship between variables.
  • Example:
    • Change in value of Y results in change in value of X.
    • X is independent of y.

Data Types for Plotting

There are various data types used for plotting.

Numerical data

There are two types of numerical data: –

  • Discrete Data – Distinct or counted values
    Examples: Number of employees in a company or number of students in a class.
  • Continuous Data – Values within a range that can be measured.
    Examples: Height can be measured in feet or inches and weight can be measured in pounds or kilograms.

Categorical data

There are two types of categorical data: –

  • Cluster or group – Grouped values
    Examples: Students can be divided into different groups based on height – Tall, Medium, and Short
  • Ordinal Data – Grouped values according to ranks
    Examples: A ranking system; a five-point scale with ranks like “Agree,” “Strongly agree,” and “Disagree”

Time Series

The third type of data is time series, Data measured in time blocks such as date, month, year, and time (hours, minutes, and seconds).

Types of Plots

There are several types of plotting techniques that can be used to visualize data. The choice of plot depends on the kind of data you are dealing with. Sometimes more than one is used to develop a better understanding of the data. When there is a new data feed or you have not worked with such data before, you can use more than one plot. Following are the few high-level plot selection techniques: –

If the data is continuous: –

  • Histograms
  • Line chart
  • Regression plot

If the data is categorical in nature or you need to identify patterns in the data: –

  • Heat map
  • Cluster map
  • Scatter plot

Data Analytics – An Iterative Process

Data Analytics is an iterative process involving tracing back the steps, often to ensure that you are on the right track. During every step of the process, you have to check the original question or intent to ensure that the analysis is on the right track. 

At the end of the process, you should be able to answer the question or solve the problem through data analysis by retracting the steps scientifically.

Data Analytics – Skills and Tools

These are the Skills and tools required for each step of the Data Analytics process.

  • Question or Business Problem
    • Ability to ask appropriate questions and know the business.
    • Domain Knowledge
    • Passion for data
    • Analytical Approach
  • Data Acquisition
    • Beautiful Soup for web scraping.
    • CSV or another file knowledge
    • NumPy
    • Pandas
    • Database
  • Data Wrangling
    • CSV or another file knowledge
    • NumPy
    • Pandas
    • Database
    • SciPy
  • Data Exploration
    • NumPy
    • SciPy
    • Pandas
    • Matplotlib
  • Conclusion or Predictions
    • Scikit-learn – the main machine learning library
    • CSV or another file knowledge
    • NumPy
    • Pandas
    • Database
    • SciPy
  • Communication or Data Visualization
    • Pandas
    • Database
    • Matplotlib
    • PPT
    • CSV or another file knowledge

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