Important Dates

• Mode: Virtual (Online)

• Seats: Limited (early registration recommended)

Registration Fee

National:

Faculty: 299 INR

Student: 249 INR

International: 15 USD

Perks: 📜 E-Certificate | 🎥 Recordings | 🤝 Expert Interaction

Overview

Essential Statistics for AI & Data Science: One-Week Python Workshop is a fast-paced, practical, and hands-on program designed to equip participants with the core statistical knowledge required to excel in Artificial Intelligence and Data Science.

In today’s AI-driven world, understanding statistics is not optional — it’s fundamental. This workshop bridges the gap between theoretical statistics and real-world AI/ML applications. Through live coding sessions, you will learn how to properly analyze data, evaluate models, perform hypothesis testing, and make statistically sound decisions using Python.

Objectives

By the end of this One-Week Workshop, participants will be able to:

• Understand and apply core statistical concepts essential for AI and Data Science
• Perform exploratory data analysis, visualization, and statistical summarization using Python
• Master probability, probability distributions, and sampling techniques used in machine learning
• Conduct hypothesis testing, confidence intervals, and A/B testing with Python
• Build and evaluate regression models while interpreting statistical results
• Develop statistical thinking for better model building, validation, and decision-making in AI projects
• Confidently work with real-world datasets and implement statistical methods in Data Science pipelines

Targeted Participants

This workshop is ideal for:

• Students & Fresh Graduates pursuing B.Tech, M.Tech, MCA, MSc Data Science, or AI/ML
• Aspiring Data Scientists & AI Engineers who want to strengthen their statistical foundation
• Working Professionals in IT, Analytics, or Software Development looking to transition into Data Science or AI roles
• Python Developers who want to move beyond coding into data-driven decision making
• Researchers & Academicians working in AI, Machine Learning, or Data Analytics
• Business Analysts & Product Managers who deal with data and experimentation (A/B testing)

Day-wise Content Plan

Day 1: Foundations & Descriptive Statistics

• Introduction to the workshop and importance of Statistics in AI/ML
• Types of data (Quantitative & Qualitative)
• Population vs Sample, Parameters vs Statistics
• Measures of Central Tendency: Mean, Median, Mode
• Measures of Dispersion: Variance, Standard Deviation, Range, IQR
• Shape of Data: Skewness and Kurtosis
• Exploratory Data Analysis (EDA) using Python
• Hands-on: Pandas, NumPy, Seaborn visualizations (Histogram, Boxplot, Pairplot)

Day 2: Probability Concepts for AI

• Basic Probability and Rules
• Conditional Probability and Bayes’ Theorem
• Random Variables and Probability Distributions
• Expectation, Variance, and Covariance
• Real-life AI applications of probability
• Hands-on: Probability simulations, Bayes’ Theorem implementation in Python

Day 3: Probability Distributions

• Discrete Distributions: Bernoulli, Binomial, Poisson
• Continuous Distributions: Normal, Uniform, Exponential
• Central Limit Theorem (CLT) and its importance in AI
• QQ Plots and Normality Testing
• Hands-on: scipy.stats library, Distribution fitting, CLT simulation

Day 4: Sampling, Estimation & Confidence Intervals

• Sampling Techniques and Sampling Distribution
• Point Estimation and Interval Estimation
• Confidence Intervals for Mean and Proportion
• Bootstrapping Method
• Hands-on: Confidence Interval calculation, Bootstrapping in Python

Day 5: Hypothesis Testing

• Hypothesis Testing Framework (Null & Alternative Hypothesis)
• p-value, Significance Level, Type I & Type II Errors
• Parametric Tests: One-sample t-test, Two-sample t-test, Paired t-test
• Chi-Square Test, ANOVA
• Hands-on: Hypothesis testing using SciPy on real datasets

Day 6: Correlation, Regression & Model Evaluation

• Correlation Analysis (Pearson & Spearman)
• Simple Linear Regression
• Multiple Linear Regression
• Model Assumptions and Diagnostics
• Interpretation of Regression Results
• Evaluation Metrics (R², Adjusted R², MSE, RMSE)
• Hands-on: Regression using Statsmodels & Scikit-learn

Day 7: Advanced Topics + Capstone Project

• Introduction to Bayesian Statistics
• Bias-Variance Tradeoff
• Statistical aspects of Feature Selection & Dimensionality Reduction
• Statistical Model Validation Techniques
• Capstone Project: Participants work on a real-world dataset (A/B Testing, Predictive Analysis, or Hypothesis-driven project)
• Project Presentations and Feedback
• Closing Remarks & Future Learning Path

Speaker:

Dr. Mehar Chand is an esteemed Professor in the Department of Mathematics at Baba Farid College of Engineering and Technology, Bathinda, India. With a dedication to teaching, he imparts knowledge in various mathematical courses while actively supporting students and the broader interdisciplinary community. He has more than 15 year of teaching UG and PG classes. Under his supervision 3 scholoars awarded Ph.D degree. He has been appointed as a member of the Board of Post Graduate Studies in Mathematics at Punjabi University for the session 2022-2023. His research interests span across several areas, including fractional calculus and its applications, Mathematical Modeling, Numerical Methods, Computational Mathematics, Special functions, Hypergeometric functions, Mathematical Physics, ANN, ML, and DL. Dr. Mehar Chand’s scholarly contributions are significant, with over 70 research papers published in national and international journals, including those indexed in SCI, SCIE, and SCOPUS. Additionally, he has authored 5 Book Chapters published by Springer.Dr. Mehar Chand is an active participant in academic discourse, having delivered more than 50 invited talks, expert talks, Keynotes, and plenary talks at national and international workshops, faculty development programs, and conferences. He has also organized over 50 national and international Training Workshops, FDPs, Webinars, and Seminars.Beyond academia, Dr. Mehar Chand is the Founder and President of the MathTech Thinking Foundation (MTTF), a registered organization under Section 8 of the Companies Act 2013, Ministry of Corporate Affairs, Government of India. MTTF represents an International Scientific Association comprising esteemed experts in Science, Technology, Engineering, and Mathematics (STEM). The foundation’s initiatives include organizing conferences, workshops, symposiums, faculty development programs, and training sessions, while also providing sponsorship and technical support for such events.

Organised by:

MathTech Thinking Foundation, Punjab, India

MathTech Thinking Foundation (Udyam-Registered MSME, Section 8 Company, 12AB) is an internationally recognized organization registered under the Companies Act 2013, Ministry of Corporate Affairs, Government of India. Aligned with the UN Sustainable Development Goals, the foundation advances STEM education and research through innovation, collaboration, and inclusive knowledge sharing.

• Contact & Registration

• WhatsApp: Faiz Ahmed (+91-70877-59319) Mehar Chand (+91-978-092-0053)