Statistics for Data Science
- 1
- New Delhi: Pinakin Publishing, 2026.
- 278p.
Contents Preface 1. Introduction to Statistics for Data Science Understanding Statistics Difference Between Descriptive and Inferential Statistics Difference Between Structured Data and Unstructured Data The Benefits of Data- Driven Decision Making 2. Data Collection and Sampling Techniques Data Collection Difference Between Primary and Secondary Data Population vs Sample in Statistics Sampling 3. Understanding Data Types and Measurement Scales Nominal, Ordinal, Interval and Ration Scales Cross Sectional vs Longitudinal Studies Difference Between Discrete and Continuous Variable Categorical Variable Dependent and Independent variable 4. Organizing and Summarizing Data Frequency Distribution Bar Charts and Histograms Line and Bar Graphs Application Contigency Table Central Tendency 5. Exploratory Data Analysis (EDA) Exploratory Data Analysis Introduction to Statistical Data Distributions Missing Data Detecting Outliers 6. Probability Concepts in Analytics Introduction to Probability Exploring the Different Types of Probabilities Probabilistics Risk Assessment Common Misconceptions in Probability 7.Understanding Distributions Probability Distribution The Normal Distribution Skewness and Kurtosis Negative Binomial Distribution Fitting Fat- Tailed Distributions in Finance 8. Statistical Inference Basics Statistical Inference Concepts of Estimation Difference Between Point and Interval Estimate Intervel Estimates Sampling Distribution Concept 9. Correlation and Association Understanding Correlation Positive and Negative Correlations Correlation and Causation Scatter Plot Correlation in Time Series 10. Introduction to Regression Analysis Understanding Regression Analysis Simple Linear Regression Regression Coefficients Residual Concepts Bibliography