Easy Notes of Data Analytics unit- 1 @Computer Diploma

Description

1.1 Data Analytics Overview

• Definition: The process of examining raw data to find patterns, draw conclusions, and support decision-making.

• Importance: * Informed Decisions: Moves from “gut feeling” to data-driven choices.

  • Efficiency: Identifies bottlenecks in business processes.

  • Customer Insight: Understands behavior and preferences.

1.2 Types of Data Analytics

These are often viewed as a maturity model (from simple to complex).

1.3 Life Cycle & Quality

• Data Analytics Life Cycle:

  1. Discovery: Business objectives.

  2. Preparation: Cleaning and transforming data.

  3. Model Planning: Choosing algorithms.

  4. Model Building: Execution.

  5. Communicate Results: Visualization.

  6. Operationalize: Deployment.

• Quality vs. Quantity: More data is not always better. Quality (accuracy, completeness, consistency) beats Quantity (volume) if the volume is “noisy” or biased.

• Measurement: Assigning numbers to observations (Scales: Nominal, Ordinal, Interval, Ratio).

1.4 Data Types & Statistics

• Data Types:

  • Qualitative (Categorical): Nominal (Labels, e.g., Color) and Ordinal (Ordered, e.g., Ratings).

  • Quantitative (Numerical): Discrete (Counts, e.g., 5 people) and Continuous (Measurements, e.g., 5.5 kg).

• Measures of Central Tendency: * Mean: Average.

  • Median: Middle value.

  • Mode: Most frequent value.

• Measures of Dispersion: * Range: Max – Min.

  • Variance: Average of squared differences from the Mean.

  • Standard Deviation: Square root of variance; indicates how spread out data is.

1.5 Sampling & Probability Concepts

• Sampling Funnel: The process of narrowing down a Population (the whole group) to a Sample (the subgroup we actually study).

• Central Limit Theorem (CLT): States that if you take enough large samples from any population, the means of those samples will follow a Normal Distribution (Bell Curve).

• Confidence Interval (CI): A range of values (e.g., 95%) within which we are reasonably sure the true population parameter lies.

• Sampling Variation: The natural difference between results from different samples taken from the same population.

Quick Recall Keywords for Exams:

• GIGO: “Garbage In, Garbage Out” (referring to Data Quality).

• Bell Curve: Visual representation of Normal Distribution (CLT).

• Root Cause: The goal of Diagnostic Analytics.

• Outlier: Data points that fall far outside the normal range (affects the Mean heavily).