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Analyze Business Data Using Statistics Foundations

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Analyze Business Data Using Statistics Foundations

Instructor: EDUCBA

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Gain insight into a topic and learn the fundamentals.
1 week to complete
at 10 hours a week
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Gain insight into a topic and learn the fundamentals.
1 week to complete
at 10 hours a week
Flexible schedule
Learn at your own pace

What you'll learn

  • Analyze business data using probability, statistics, and mathematical methods.

  • Apply hypothesis testing and statistical distributions to real-world business problems.

  • Evaluate relationships between variables to support data-driven decisions.

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Recently updated!

January 2026

Assessments

20 assignments

Taught in English

There are 5 modules in this course

By completing this course, learners will be able to analyze business data using statistical methods, apply probability models to real-world scenarios, perform hypothesis testing, interpret statistical distributions, and evaluate relationships between variables to support data-driven decision-making.

This course provides a comprehensive foundation in Business Mathematics and Statistics, designed to bridge theory with practical business and analytics applications. Learners progress from understanding data types and sampling methods to mastering probability, distributions, matrix mathematics, and statistical inference. The course emphasizes real-world relevance by connecting statistical concepts with machine learning fundamentals and business decision contexts. Learners benefit by developing the ability to interpret data accurately, manage uncertainty, and justify decisions using statistical evidence—skills highly valued in analytics, finance, operations, and management roles. Step-by-step explanations, structured modules, and applied examples ensure clarity even for learners with minimal mathematical background. What makes this course unique is its integrated approach, combining statistics, probability, mathematical foundations, and applied hypothesis testing within a single learning pathway. The curriculum is industry-aligned, assessment-driven, and optimized for both academic learning and professional upskilling, making it ideal for business students, aspiring analysts, and working professionals.

This module introduces learners to the fundamentals of data collection, sampling techniques, data types, and the role of machine learning in statistical analysis, forming the base for all further statistical reasoning.

What's included

9 videos4 assignments

9 videosTotal 76 minutes
  • Sampling Method in Machine Learning8 minutes
  • Technical Terminology11 minutes
  • Error of Observation and Non Observation7 minutes
  • Systematic Sampling8 minutes
  • Cluster Sampling11 minutes
  • Statistics Data Types5 minutes
  • Qualitative Data and Visualization8 minutes
  • Machine Learning8 minutes
  • Relative Frequency Probability9 minutes
4 assignmentsTotal 60 minutes
  • Graded-Foundations of Data & Sampling30 minutes
  • Introduction to Data & Learning Systems10 minutes
  • Sampling Techniques in Practice10 minutes
  • Understanding and Visualizing Data10 minutes

This module builds a strong foundation in probability theory, enabling learners to quantify uncertainty and analyze events using probability rules and distributions commonly applied in business decision-making.

What's included

9 videos4 assignments

9 videosTotal 83 minutes
  • Joint Probability10 minutes
  • Conditional Probability9 minutes
  • Concept of Independence7 minutes
  • Total Probability10 minutes
  • Random Variable9 minutes
  • Probability Distribution11 minutes
  • Cumulative Probability Distribution10 minutes
  • Bernoulli Distribution9 minutes
  • Gaussian Distribution8 minutes
4 assignmentsTotal 60 minutes
  • Graded-Probability Essentials for Business Decisions30 minutes
  • Core Probability Concepts10 minutes
  • Advanced Probability Rules10 minutes
  • Distribution Functions Explained10 minutes

This module connects probability theory with mathematical tools such as distributions, matrix operations, and statistical inference, providing analytical skills essential for data-driven modeling.

What's included

10 videos4 assignments

10 videosTotal 96 minutes
  • Geometric Distribution8 minutes
  • Continuous and Normal Distribution10 minutes
  • Mathematical Expression and Computation9 minutes
  • Transpose of Matrix9 minutes
  • Properties of Matrix12 minutes
  • Determinants10 minutes
  • Error Types9 minutes
  • Critical Value Approach9 minutes
  • Right and Left Sided Critical Approach10 minutes
  • P-Value Approach11 minutes
4 assignmentsTotal 60 minutes
  • Graded-Distributions & Mathematical Foundations30 minutes
  • Discrete & Continuous Distributions10 minutes
  • Matrix Mathematics Basics10 minutes
  • Introduction to Statistical Inference10 minutes

This module focuses on hypothesis testing techniques, confidence intervals, and distribution analysis, enabling learners to make statistically sound decisions under uncertainty.

What's included

10 videos4 assignments

10 videosTotal 91 minutes
  • P-Value Approach Continues9 minutes
  • Hypothesis Testing11 minutes
  • Left Tail Test6 minutes
  • Two Tail Test10 minutes
  • Confidence Interval9 minutes
  • Example of Confidence Interval11 minutes
  • Normal and Non Normal Distribution10 minutes
  • Normality Test10 minutes
  • Normality Test Continues10 minutes
  • Determining the Transformation6 minutes
4 assignmentsTotal 60 minutes
  • Graded-Hypothesis Testing & Statistical Reasoning30 minutes
  • P-Values and Hypothesis Testing10 minutes
  • Testing Strategies and Confidence10 minutes
  • Distribution Analysis & Normality10 minutes

This module emphasizes applied statistical testing and relationship analysis, enabling learners to examine associations, validate models, and interpret relationships between variables.

What's included

9 videos4 assignments

9 videosTotal 76 minutes
  • T-Test11 minutes
  • T-Test Continue8 minutes
  • More on T-Test9 minutes
  • Test of Independence11 minutes
  • Example of Test of Independence10 minutes
  • Goodness of Fit Test7 minutes
  • Example of Goodness of Fit Test7 minutes
  • Co-Variance5 minutes
  • Co-Variance Continues8 minutes
4 assignmentsTotal 60 minutes
  • Graded-Statistical Tests & Relationship Analysis30 minutes
  • Mean Comparison Tests10 minutes
  • Association & Model Validation10 minutes
  • Measuring Relationships in Data10 minutes

Instructor

EDUCBA
1,591 Courses326,930 learners

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