What will you learn in Understanding and Visualizing Data with Python Course
Identify and understand different types of data (categorical, quantitative) and how they are collected.
Create data visualizations (histograms, bar charts, box plots, scatter plots) using Python.
Analyze multivariate relationships and apply numerical summaries for insight.
Explore sampling methods (probability vs non-probability) and learn how sample statistics infer population trends.
Program Overview
Module 1: Introduction to Data & Statistical Thinking
⏳ 1 week
• Topics: Data types, study design, introduction to Jupyter notebook environment
• Hands‑on: Work in labs on variable identification, Python basics, and notebook navigation
Module 2: Univariate Visualizations & Summaries
⏳ 1 week
• Topics: Bar charts, histograms, box plots, and basic numerical summaries like mean, median, IQR, standard score
• Hands‑on: Analyze and visualize univariate datasets using Python libraries such as Pandas and Matplotlib
Module 3: Multivariate Relationships & Association
⏳ 1 week
• Topics: Exploring relationships between quantitative and categorical variables, scatter plots, and correlation structures
• Hands‑on: Build multivariate visualizations and interpret patterns in real-world datasets
Module 4: Sampling, Inference & Interpretation
⏳ 1 week
• Topics: Probability vs non-probability sampling, sampling variability, interpreting statistical claims
• Hands‑on: Evaluate sample design examples and apply reasoning on how to generalize findings
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Job Outlook
Core statistics skills and Python visualization are widely required in roles like Data Analyst, Research Associate, or BI Analyst.
Proficiency in tools like Pandas, Matplotlib, and Seaborn is valued in industries such as healthcare, finance, marketing, and academia.
Typical salary ranges: ₹6–12 LPA (India), $65K–$100K+ (global) for entry-level roles.
Builds a strong foundation for ML, data science, and decision-support roles.
Specification: Understanding and Visualizing Data with Python
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