What you will learn

• Data visualization
• Analytics
• Charts
• Dashboards

Course content (32 lessons)

1. 1.1 Unit 1 Overview — Unit 1: Overview of Data Visualisation and Analytics This unit introduces the fundamentals of data visualization, its importance, and various techniques to represent data…
2. 1.2 Analytics Fundamentals — Analytics: Basic Nomenclature 1. What is Analytics? Analytics is the systematic, computational process of collecting, cleaning, analyzing, and interpreting data to discover useful…
3. 1.3 Analytics Process Model — Analytics Process Model & Professional Roles 1. The Analytics Process Model The analytics process is a structured, iterative approach to solving problems using data. Study Deep:…
4. 1.4 Analytical Models — Analytical Models: Requirements & Types 1. What is an Analytical Model? An analytical model is a mathematical or statistical representation of a real-world process. Study Deep:…
5. 1.5 Data Collection & Sampling — Data Collection, Sampling & Distributions 1. Data Collection Sources Data collection is the foundation. The quality and type of data collected determines everything downstream.…
6. 1.6 Data Quality & Outliers — Data Quality: Missing Values & Outliers 1. Missing Values Handling missing data is critical because most statistical methods and machine learning algorithms cannot process…
7. 1.7 Standardization — Standardization (Feature Scaling) 1. Why Scale Data? Variables often have different units and magnitudes. Scaling puts all features on a level playing field. Study Deep: When…
8. 1.8 Categorization & Segmentation — Categorization vs. Segmentation 1. Categorization Categorization is the process of assigning data points into predefined, manually specified groups based on explicit rules. Study…
9. 2.1 Unit 2 Overview — Unit 2: Statistical Methods & Hypothesis Testing This unit covers the fundamental statistical techniques required for data analysis, tailored for BCA and computer science…
10. 2.2 Probability Distributions in Depth — Probability Distributions: The Theory of Data Patterns 1. Mathematical Foundation A probability distribution is a mathematical function that describes the likelihood of obtaining…
11. 2.3 Advanced Sampling Theory — Sampling Theory: Bridging Sample and Population 1. The Core Objective In data analytics, we rarely have access to the entire Population (N). Instead, we take a Sample (n) to…
12. 2.4 Rigorous Hypothesis Testing — Hypothesis Testing: The Decision Framework 1. The Philosophical Framework Hypothesis testing operates like a criminal trial: "Innocent until proven guilty." Study Deep:…
13. 2.5 Parametric Tests: Deep Dive — Parametric Tests: Z and T Distributions 1. What makes a test "Parametric"? Parametric tests assume that the underlying population follows a specific probability distribution…
14. 2.6 The Mathematics of p-Values — p-Values: Evidence and Interpretation 1. Mathematical Definition of a p-Value The p-value is the exact probability of obtaining a test statistic at least as extreme as the one…
15. 2.7 Confidence Intervals & Precision — Confidence Intervals: Quantifying Uncertainty 1. Point Estimates vs. Interval Estimates - Point Estimate: A single number calculated from a sample (e.g., sample mean x̄ = 45 ). It…
16. 2.8 Non-Parametric: Chi-Square Test — Chi-Square (χ²): Analyzing Categorical Data 1. Parametric vs. Non-Parametric When data violates normal distribution assumptions, or when dealing with nominal/ordinal categorical…
17. 2.9 Correlation & Linear Regression — Correlation & Regression: Modeling Relationships 1. Pearson Correlation Coefficient (r) Quantifies the linear relationship between two continuous variables. Study Deep: Adjusted…
18. 2.10 Analysis of Variance (ANOVA) — ANOVA: Comparing Multiple Groups Rigorously 1. The Problem with Multiple T-Tests If testing 3 algorithms (A, B, C), running 3 T-tests results in Family-wise Error Rate Inflation.…
19. 2.11 Statistical Paradoxes in Analytics — Statistical Paradoxes: When Math Defies Logic 1. Simpson’s Paradox A phenomenon where a trend appears in isolated subgroups of data, but disappears or reverses when the groups are…
20. 3.1 Unit 3 Overview — Unit 3: Data Visualization with Python This unit focuses on the practical application of visualization libraries. You will learn to create static, animated, and interactive plots…
21. 3.2 Matplotlib Basics — Data Visualization with Matplotlib: The Foundation 1. Introduction to Architecture Matplotlib is the foundational library for visualization in Python. Study Deep: Tufte's Data-Ink…
22. 3.3 Advanced Matplotlib — Advanced Matplotlib: Styling, Subplots & Layouts 1. Subplots: One Figure, Multiple Graphs Often you want to compare different views side-by-side. We use plt.subplots() . Parameter…
23. 3.4 Seaborn: Interface & Distributions — Study Deep: Kernel Density Estimation (KDE) A Histogram is sensitive to "bin size"—change the bin width, and the shape changes. The Solution: KDE . It smooths the data using a…
24. 3.5 Seaborn: Categorical & Styling — Seaborn: Categorical Data & Aesthetics 1. Visualizing Categorical Data ( catplot ) When one variable is a category (e.g., "Day of Week") and the other is numerical (e.g., "Total…
25. 4.1 Unit 4 Overview — Unit 4: GUI Programming & Database Access This final unit bridges the gap between analysis and application. You will learn to build user-friendly interfaces using Tkinter and…
26. 4.2 GUI Programming with Tkinter — GUI Programming: Creating User Interfaces with Tkinter 1. Introduction to GUI (Graphical User Interface) A GUI allows users to interact with a program using visual elements like…
27. 4.3 Advanced GUI Widgets — Advanced Tkinter: Selection, Menus, and Dialogs 1. Tkinter Variable Types Tkinter uses special variable classes to track widget state. They automatically update the GUI when…
28. 4.4 Database Connectivity & SQL — Database Access in Python: The DB-API 1. Introduction to DB-API Python provides a standard interface called DB-API 2.0 (PEP 249) for interacting with databases. This means the…
29. 4.5 CRUD Operations — Implementing CRUD in Python 1. Creating a Table Column Constraints: Constraint Meaning Example :--- :--- :--- PRIMARY KEY Unique identifier for each row id INTEGER PRIMARY KEY…
30. MID Term Important Questions — MID Term Important Questions Section A – Short Answer Questions 1. Define Data Analytics. 2. What do you mean by Basic Nomenclature in Analytics? 3. Explain the Analytics Process…
31. PYQ: End Term June 2024
32. PYQ: End Term May/June 2025

1.1 Unit 1 Overview

Unit 1: Overview of Data Visualisation and Analytics

This unit introduces the fundamentals of data visualization, its importance, and various techniques to represent data effectively. We will explore how raw data is transformed into meaningful insights through a systematic process. By the end of this unit, you will understand the full analytics pipeline — from data collection to decision-making — and the mathematical tools used at each stage.

Topics Covered in This Unit

Visual Overview

The visual overview below summarizes the key concepts covered in this unit, including the analytics process, types of data, and the role of visualization in decision-making.

(Refer to the image below for a structural breakdown)

Continue reading: 1.1 Unit 1 Overview →

Frequently asked questions

Is the Data Visualisation and Analytics course really free?

Yes. The entire Data Visualisation and Analytics course on Siksha Sarovar is free to read with no account required. You can optionally sign in with Google to save your progress.

Do I get a certificate for Data Visualisation and Analytics?

Yes — finish the lessons and pass the quiz to earn a free, verifiable certificate you can share on LinkedIn or with recruiters.

Can I run code while learning?

Yes. The built-in online compiler runs C, C++, Python, Java, PHP, JavaScript, C# and SQL directly in your browser — no installation needed.