The Statistics Essentials for Analytics course provides a comprehensive foundation in statistical concepts crucial for data-driven decision-making. Ideal for aspiring data analysts, business intelligence professionals, and data scientists, this training equips you with the tools to understand, interpret, and apply statistical techniques across various business and research contexts.

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Instructor led live online Classes

Statistics Essentials for Analytics

Instructor led live online Training (Weekday/Weekend)

Why Enroll in the Statistics Essentials for Analytics Course?

Core Knowledge: Gain proficiency in key statistical concepts for data analysis.
Career Advancement: Enhance your analytical skills for roles in data science, analytics, and research.
Hands-On Practice: Learn through practical problems and real-world datasets.
Foundation for Advanced Learning: Essential groundwork for advanced analytics, machine learning, or AI courses.
Industry Relevance: Understand statistical applications used in business intelligence and data interpretation.

Course Description

This course builds your understanding of descriptive and inferential statistics, probability theory, hypothesis testing, and data distributions used in analytics.

1. Aspiring Data Analysts and Scientists 2. Business Intelligence Professionals 3. Researchers and Academic Professionals 4. Anyone preparing for data-focused certifications or roles

1. Strengthens analytical reasoning through concepts, formulas, and case studies. 2. Prepares you for data science, machine learning, and analytics programs. 3. Includes quizzes, assignments, and instructor support.

What you'll learn

Descriptive Statistics: Measures of central tendency and variability.

Probability Theory: Basic probability, permutations, combinations, and probability distributions.

Inferential Statistics: Confidence intervals, sampling, and hypothesis testing.

Regression Analysis: Simple linear regression and correlation.

Statistical Applications: Applying statistics in business and data analytics.

Requirements

Basic Mathematics Knowledge
No Prior Experience in Statistics Required

Curriculum Designed by Experts

Introduction to Data Types
Numerical parameters to represent data
a. Mean

b. Mode

c. Median

d. Sensitivity

e. Information Gain

f. Entropy
Statistical parameters to represent data

Uses of probability
Need of probability
Bayesian Inference
Density Concepts
Normal Distribution Curve

Point Estimation
Confidence Margin
Hypothesis Testing
Levels of Hypothesis Testing

Association and Dependence
Causation and Correlation
Covariance
Simpson’s Paradox
Clustering Techniques

Parametric Test
Parametric Test Types
Non- Parametric Test
Experimental Designing
A/B testing

Logistic and Regression Techniques
Problem of Collinearity
WOE and IV
Residual Analysis
Heteroscedasticity
Homoscedasticity

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Statistics Essentials for Analytics

Read Review

Instructor led live online Classes

Statistics Essentials for Analytics