Siksha Sarovar

Siksha Sarovar (sikshasarovar.com) is a free educational web application that helps students in India learn programming and prepare for academic and competitive exams. The platform offers structured coding courses (C, C++, Python, Java, HTML, CSS, PHP, Power BI, AI, Machine Learning, Data Science), complete university curriculum notes for BCA/MCA students with previous year question papers, Class 10 and Class 12 CBSE/HBSE school notes, and dedicated preparation material for SSC, UPSC, Banking, Railway and other government exams. Browsing the site is completely free and requires no account. Users may optionally sign in with Google solely to save their learning progress, quiz scores and personal preferences across devices.

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Siksha Sarovar is a free e-learning platform for coding courses, BCA university notes and competitive exam preparation. Optional Google sign-in saves your learning progress across devices.

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1.1 Unit 1 Overview

Lesson 1 of 32 in the free Data Visualisation and Analytics notes on Siksha Sarovar, written by Rohit Jangra.

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

#TopicDescriptionKey Concepts
1.2Analytics FundamentalsCore terminology, types of data, and the four analytics typesStructured vs. Unstructured Data, Descriptive to Prescriptive Analytics
1.3Analytics Process ModelStep-by-step data analysis pipeline and professional rolesCRISP-DM, Data Engineer vs. Data Scientist
1.4Analytical ModelsMathematical models for prediction and classificationClassification, Regression, Clustering, Time-Series
1.5Data Collection & SamplingHow to gather representative dataProbability vs. Non-Probability Sampling, Central Limit Theorem
1.6Data Quality & OutliersHandling imperfect dataMCAR/MNAR Missingness, Z-Score & IQR Outlier Detection
1.7StandardizationScaling features for fair comparisonMin-Max Normalization, Z-Score Standardization, Robust Scaling
1.8Categorization & SegmentationGrouping data — rule-based vs. data-drivenK-Means Clustering, Silhouette Score

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)