Data Visualisation: The why & how?

Faculty: Mr. Amlesh Kanekar

Amlesh Kanekar is a consulting data scientist at Quantic Technovations and a visiting faculty member at the Meghnad Desai Academy of Economics. He has 30+ years of global experience in information technology in the Banking and Finance Domain. Prior to Quantic, he worked as Senior Director, Digital Banking Consulting at Oracle financial services software. You can read more about his background here – https://www.linkedin.com/in/amlesh-kanekar-6377a413/.

Learning Outcomes:

1. Understand how statistics and visualization complement each other.
2. Learn to appreciate the correlation between numerical dynamics of data and the principles of visualization.
3. Get an in-depth understanding of the visualizations for a single categorical variable and a single numeric variable.
4. Get an in-depth understanding of bivariate visualizations for combinations of categorical and numeric variables.
5. Understand multivariate visualization using the scatterplot.
6. Get a glimpse into how graphs need to be dressed up for publication on the world wide web

Duration: 10 Hours

Eligibility: Open to students, faculty, research scholars and working professionals from diverse backgrounds

Pre-requisites for the Data Visualization Course:

The course is designed such that anyone having attained or is in the process of attaining an undergraduate degree can attend and draw learnings.

The pre-requisites are:

• A desire to excel in data analysis
• Basic exposure to statistics such as types of variables and preliminary concepts of descriptive statistics such as mean, median and percentiles

An exposure to Microsoft Excel is a soft pre-requisite.  However, it is believed that all college students, regardless of their stream have the amount of exposure to Excel that would enable them to appreciate this course.

The use of Python for visualization will be covered, but a deep understanding of Python is not required. The course will however provide enough insights into Python usage to enable students to decide whether they would like to undertake formal training in Python.

Fees: INR 973.5 + GST

Use code MDAEPAST50 to get the recorded version of the course at 50% discounted price of the live version (Discounted price for the recorded version: Rs 973.5)

Course Structure:

Topic 1 (3 hours)
The philosophy of Data Visualization

• Deconstructing and understanding the expression data visualization
• Understanding how data analysis and visualization complement each other
• A drill-down into what constitutes data, with examples

Data

• An analysis of a “data table”
• The “Row View”
• The “Columnar View”
• What might interest a data analyst about rows i.e. instances or samples
• What might interest a data analyst about columns i.e. attributes or variables
• Types of variables – Categorical and Numeric

Where does Data Visualization enter the frame of Data Analysis?

• The motive force behind visualization – A PICTURE SPEAKS LOUDER THAN A THOUSAND WORDS
• The interplay of variables that leads to visualization
• Univariate analysis and visualization
• Bivariate analysis and visualization
• Multivariate analysis and visualization

The WHY, WHAT and HOW of Visualization

• Why visualize (what questions do we want visualization to answer)
• What is being visualized (the data)?
• How should we visualize (the various types of plots and graphs)

Topic 2 (2 hours)
Univariate Visualization

• The Boxplot
  • The numerical dynamics that drive a histogram
  • Constructing and interpreting a histogram (Excel and Python)
• The Piechart
  • Constructing and interpreting a piechart (Excel and Python)
• The Barchart
  • Constructing and interpreting a bar chart (Excel and Python)

Topic 3 (2 hours)
Going beyond Univariate Visualization

• Bivariate Visualization i.e. visualizing the relationship of two variables
  • The cause-effect relationship
  • Concept of X and Y variables
  • Concept of independent and dependent variables
  • Choice of pair of variables, which is X and which is Y, what questions will the combination answer
  • The matrix of 4 combinations
    • Categorical (X) vs Categorical (Y)
    • Numeric (X) vs Numeric (Y)
    • Categorical (X) vs Numeric (Y)
    • Numeric (X) vs Categorical (Y)
  • The Grouped Barchart
  • The Stacked Barchart
  • The Heatmap
  • Subplots

Topic 4 (1 hour)
Going beyond Bivariate Visualization

• The Scatterplot
  • Using the scatterplot for bivariate visualization (two numeric variables)
  • Extending the scatterplot to include up to 5 variables

Topic 5 (2 hours)
More visualizations

• Time series visualizations using the line chart
• Violin plot
• Pair plots

Extending “visualization” to “infographics”

• Constructing a chart from bbc.co.uk in Python

Registrations for this certificate program are now closed. Thank you for your interest. By registering in the form below, you will jump the queue for our second intake for this program which will begin shortly. We will also be posting a few lecture take-aways, reading materials and other interesting content from this course. Follow us on our social media platforms (Facebook & Instagram) and our website to know more.