This comprehensive statistics course provides the mathematical foundation essential for effective data analysis and interpretation. Participants will progress from basic descriptive statistics to advanced inferential techniques, learning to apply statistical methods to real-world business problems. The curriculum emphasizes practical application over theoretical mathematics, ensuring learners can confidently use statistical tools to draw meaningful conclusions from data. Through hands-on exercises, participants will develop the ability to select appropriate statistical tests and interpret results accurately.
Statistics for Data Analysis: From Descriptive to Inferential
Data Analytics and Business Intelligence
October 25, 2025
Introduction
Objectives
Key learning objectives include:
- Master descriptive statistics for data summarization
- Understand probability distributions and their applications
- Apply hypothesis testing to business problems
- Utilize correlation and regression analysis
- Perform ANOVA and chi-square tests
- Interpret statistical results accurately
- Select appropriate statistical methods for different data types
- Understand statistical significance and practical importance
Target Audience
- Data analysts and scientists
- Business analysts
- Researchers and academics
- Marketing analysts
- Quality assurance professionals
- Financial analysts
- Graduate students in quantitative fields
Methodology
The course combines statistical theory with practical applications using statistical software. Real-world datasets from business contexts provide meaningful practice material. Case studies demonstrate statistical applications across industries, while group activities focus on problem-solving and interpretation. Individual exercises build computational skills, and mini-case studies present statistical challenges for solution. Syndicate discussions explore the implications and limitations of statistical findings.
Personal Impact
- Stronger foundation in statistical concepts and methods
- Improved ability to interpret statistical results
- Enhanced critical thinking about data claims
- Increased confidence in applying statistical techniques
- Better understanding of statistical software outputs
- Developed skills in statistical problem-solving
Organizational Impact
- More rigorous and reliable data analysis
- Improved quality of business insights
- Reduced risk of incorrect conclusions from data
- Standardized statistical approaches across teams
- Enhanced credibility of analytical findings
- Better investment decisions based on statistical evidence
Course Outline
Unit 1: Descriptive Statistics Fundamentals
Data Summarization- Measures of central tendency
- Measures of variability and dispersion
- Data distribution shapes
- Graphical representation of data
Unit 2: Probability Foundations
Probability Concepts- Basic probability rules and theorems
- Conditional probability and Bayes theorem
- Discrete and continuous probability distributions
- Normal distribution and its properties
Unit 3: Sampling and Estimation
Statistical Sampling- Sampling methods and techniques
- Sampling distributions
- Point and interval estimation
- Margin of error and confidence intervals
Unit 4: Hypothesis Testing
Testing Fundamentals- Null and alternative hypotheses
- Type I and Type II errors
- P-values and significance levels
- One-sample and two-sample t-tests
Unit 5: Correlation and Regression
Relationship Analysis- Correlation coefficients and interpretation
- Simple linear regression
- Multiple regression analysis
- Regression diagnostics and validation
Unit 6: Advanced Inferential Techniques
Multivariate Analysis- Analysis of variance (ANOVA)
- Chi-square tests for independence
- Non-parametric tests
- Power analysis and sample size determination
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