Applied Statistical Analysis and Modeling with Python
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Next Start
October 12, 2026
(Offered monthly)Duration
5 Days
ClassroomProgram Fee
$1,250
Exclusive 16% VATOther Dates
November 9, 2026
December 14, 2026
December 14, 2026
Course Description
This professional course provides a practical foundation in statistical analysis and modeling using Python. Participants will learn how to describe and summarize data, work with probability and statistical distributions, perform statistical inference, conduct hypothesis tests, and build statistical models using Python's scientific computing ecosystem.
The curriculum progresses from descriptive statistics and probability to statistical inference, correlation, regression, and advanced statistical modeling. Participants apply with NumPy, Pandas, SciPy, Statsmodels, and Seaborn to perform statistical computations, test hypotheses, fit models, evaluate model assumptions, and interpret statistical results.
Prerequisite note: This course does not teach Python, NumPy, Pandas, Matplotlib, or Seaborn from scratch. Participants are expected to be familiar with these tools; the curriculum focuses on their practical application to data analysis.
Course Outline
This course is structured into 6 core modules, each carefully designed to build progressively on the previous one.
Learning Outcomes
Technical Skills
- Calculate and interpret descriptive statistics.
- Detect and handle outliers and missing values.
- Create professional summary tables and pivot tables from grouped data.
- Export styled tables to HTML, Excel, PDF, and as images.
- Construct and interpret confidence intervals for means and proportions.
- Perform and interpret statistical hypothesis tests.
- Calculate and interpret Pearson and Spearman correlation coefficients.
- Build and interpret simple and multiple linear regression models using Statsmodels.
- Evaluate regression models using R², adjusted R², residuals, and diagnostic methods.
- Perform one-way ANOVA and analyze differences between group means.
- Conduct post-hoc tests and multiple comparisons after ANOVA.
- Build, interpret and evaluate logistic regression models for binary outcomes.
Career Benefits
- Build a strong, practical foundation in statistical analysis using Python's scientific ecosystem.
- Learn to apply statistical methods to real-world datasets and business problems.
- Develop practical skills in statistical inference, hypothesis testing, and model evaluation.
- Gain hands-on experience with NumPy, Pandas, SciPy, Statsmodels, Seaborn, and tabulate.
- Learn to build, interpret, and evaluate statistical models with confidence.
- Master professional table presentation techniques for reports and publications.
- Strengthen quantitative reasoning skills for data-driven professional decision-making.
- Establish a solid foundation for advanced study in data science and machine learning.
Who Should Attend
- Data Analysts — Professionals seeking to strengthen their statistical analysis and reporting capabilities.
- Data Scientists — Practitioners looking to solidify their statistical foundations and modeling skills.
- Researchers & Academics — Individuals applying statistical methods to research data and publishing results.
- Business Analysts — Professionals using statistics to support data-driven business decisions.
- Economists & Social Scientists — Professionals working with quantitative data and statistical models.
- Python Developers — Programmers looking to apply statistical methods and create professional tables in Python.
- Students — Learners developing practical statistical and modeling skills for their careers.
- Data Professionals — Anyone who needs to analyze, model, interpret, and present data effectively.
Prerequisites & Requirements
Prerequisites
- Basic computer literacy and familiarity with file management.
- Basic high school mathematics (algebra, functions) is required.
- General Python programming knowledge is required.
- NumPy, Pandas, and Seaborn knowledge is required.
- Basic algebra and equation-solving skills are recommended (but not mandatory).
- A willingness to work with quantitative data and embrace statistical concepts.
Requirements
- Laptop with at least 2GB RAM (GB recommended for smoother performance).
- All learning facilities are provided on-site, including high-speed internet, projectors, writing materials, and refreshments.
- Administrative rights on your laptop to install R, RStudio, and necessary packages.
- Sufficient free disk space (at least 2GB) for software installation and project files.
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