Certificate Course on

Data Visualization Using Python Programming

Take Your First Step to Make a Career in Data Science

20 Hours | Self Learning Program

Rs 499 (Inclusive of GST)

Course Overview:

This course will introduce students to the philosophy, design, best practices and tools of Data Visualization. The visualizations shall be taught using Microsoft Excel, Python’s Matplotlib, Power BI and Tableau.

  • Design visualizations for univariate, bivariate, trivariate, multi-variate data
  • Construct visualizations using the above four tools

Any individual who is:

  • Aspiring to be a data analyst or data scientist . 
  • Professionals who want to enhance their job profile with data visualization.  

  • The course does not require prior programming experience.
  • A desire to think logically will be an asset.
  • An exposure to Microsoft Excel as a computational tool would be an advantage.
  • Basic understanding of logic and flowcharts would be an advantage.
  • Students are encouraged to join the online course from their computer (rather than a mobile phone). It is advised that Python 3.x be installed on the student’s computer, so that the student can experiment with Python code after course hours.

                                      Key Features

Live Coaching

20 hours self learning program

Continuous Assessment

Evaluation assignments

Collaborative Building of Code Snippet

Constant Learner Support

Digital office hours + teaching assistant + lifetime recording access on LMS.

Alumni Status

Industry event invitations + student network access

Career Enhancement

1 Resume building and Interview Tips Session

MDAE's Online Courses Loved By Learners Across 
 100 + Leading Companies

Meet Prof. Amlesh Kanekar

Your Course Faculty

Electrical Engineering - Veermata Jijabai Technological Institute (VJTI)
Worked as Senior Director, Digital Banking Consulting, Oracle Financial Services Software

Amlesh Kanekar has 34 years of experience in Information Technology in the Banking & Finance domain. His first 8 years were in Tata Unisys Limited (now merged into Tata Consultancy Services), followed by the next 12 years as an international independent consultant on Unisys products and finally the last 14 years with Oracle Financial Services Software Limited (OFSS). His Leadership roles include Global Consulting Head for Digital Banking, Product development head for Digital Banking, Product QA head for Core Banking and Digital Banking, Delivery head for the virtual bank in Japan for Core Banking & Digital Banking.

Session-Wise Course Curriculum

  • 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)
Learning Outcomes

At the end of this topic students will have a comprehensive understanding of how statistics and visualization complement each other. They will learn to appreciate the correlation between numerical dynamics of data and the principles of visualization.

  • 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 barchart (Excel and Python)
Learning Outcomes

At the end of this topic students will have an in-depth understanding of the visualizations for a single categorical variable and a single numeric variable.

  • 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
Learning Outcomes

At the end of this topic students will have an in-depth understanding of bivariate visualizations for combinations of categorical and numeric variables.

  • Going beyond Bivariate Visualization
    • The Scatterplot
      • Using the scatterplot for bivariate visualization (two numeric variables)
      • Extending the scatterplot to include up to 5 variables
Learning Outcomes

At the end of this topic students will be well-exposed to multivariate visualization using the scatterplot.

  • The Python list type
    • Time series visualizations using the line chart
    • Violin plot
    • Pairplots
  • Extending "visualization" to "infographics"
    • Constructing a chart from bbc.co.uk in Python
Learning Outcomes

At the end of this topic students will have learnt three additional visualizations. They will also have obtained a glimpse into how graphs need to be dressed up for publication on the worldwide web.

  • Visualizations using Python’s Matplotlib
    • The basic elements of a graph with Matplotlib
    • Bar chart
    • Histogram
    • Grouped bar chart
    • Subplots
    • Kernel density plot
Learning Outcomes

At the end of this topic students will have learnt how to create effective visualizations using Python’s Matplotlib package.

  • Visualizations using Power BI Desktop
    • The basic elements of creating visualizations using Power BI
    • A basic case-study of charting for the Mutual Funds industry
    • Publishing charts in Power BI as a dashboard
Learning Outcomes

At the end of this topic students will have learnt how to create effective visualizations using Power BI.

  • Visualizations using Tableau Public
    • The basic elements of creating visualizations using Tableau
    • Tri-variate bar chart using colour coding
    • Butterfly chart
    • Animated rate chary
Learning Outcomes

At the end of this topic students will have learnt how to create effective visualizations using Tableau Public.

Alumni Track Record!

MDAE alumni working in diverse roles across leading companies.

Arushi Mishra 

Data Consultant

Vallari Naik

 Trainee Decision Scientist

Pooja Joshi

  Senior Research Analyst

Nishitha Mehta

Risk Analyst

Swati Shrimali

 Business Analyst

Ujas Shah

  Research Analyst

What Our Students Say About Our Faculty  

"Prof. Amlesh was an exceptional guiding force. Everything I have learned about Python through his course not only helped me for the exam, but I believe will help me a long way. He was patient with every student and every doubt. He made sure that everyone was comfortable with the pace he was going at. Apart from that, his course was also very well structured, Learning coding this way, in a step by step manner has really cleared my basics, which makes it easier for me not just to get comfortable with python, but every other programming language. The handouts provided were of great help as well, because during the lecture or even while practicing, there was a guide available to us on every step of the way. Overall, I believe that Prof. Amlesh's course was outstanding in every way and I look forward to learning more from him if the academy gives me the opportunity."

Sana Parikh 

"Prof Amlesh is the most patient and eloquent professor ever. I have no background in data and programming, and he made everything seem so easy and understandable, that it never felt like I haven't done this before. He was always approachable, and always very open to helping students actually learn."

Neelesha Dhawan



Including GST

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Data Visualization Using Python Programming

20 Hours Online Certification

Data Visualization Using Python Programming

MDAE's online courses are loved by learners across 100 + companies and 200 + top colleges.

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