Data Analysis in Python and AI (Urdu medium) Live Via Zoom

This lesson introduces Python as a powerful programming language for data analysis and artificial intelligence. Students will learn how to install Anaconda, understand its interface, and manage files and working directories essential for reproducible and efficient data analysis workflows.

This module focuses on Exploratory Data Analysis (EDA), a critical step in understanding data structure, patterns, and quality before applying statistical or machine learning models. Learners will explore numerical summaries, data distributions, missing values, and outliers using modern Python-based EDA tools.

This module covers data visualization techniques ranging from basic plots to advanced and interactive visual analytics. Learners will develop the ability to transform raw data into clear, informative, and publication-quality visualizations for data exploration, reporting, and decision-making.

his module introduces core statistical concepts required for data-driven decision making. Learners will develop a solid understanding of descriptive and inferential statistics and apply statistical tests and probability models using Python for real-world data analysis.

This module provides a comprehensive and applied introduction to regression analysis, covering classical, regularized, generalized, and advanced regression frameworks. Learners will gain hands-on experience in building, diagnosing, interpreting, and validating regression models for predictive and inferential analysis across diverse research domains.

This module introduces non-parametric statistical tests used when data do not meet the assumptions of normality or homogeneity of variance. Learners will apply rank-based and distribution-free methods for hypothesis testing using Python, with emphasis on correct test selection and interpretation.

This module introduces multivariate statistical techniques used to analyze complex datasets involving multiple variables simultaneously. Learners will explore dimensionality reduction, pattern discovery, classification, and relationship analysis using advanced multivariate methods implemented in Python.

This module introduces time series analysis techniques used to model, analyze, and forecast data collected over time. Learners will understand temporal patterns, seasonality, and trends, and apply classical and modern forecasting models using Python.

This module introduces specialized and advanced visualization techniques for representing complex relationships, flows, hierarchies, and high-dimensional data. Learners will develop the skills to create visually compelling and analytically powerful graphics for research, reporting, and decision-making.