Day 1: Data Analytics & Statistical Foundations (Fundamentals)

🔹 Objective: Build a strong foundation in Statistics, Probability, and Data Analytics,

essential for AI & ML.

🔹 Tools Used: Jupyter Notebook, Python (NumPy, Pandas, Matplotlib, Seaborn, SciPy,

StatsModels)

🔹 Session 1: Introduction to Data Science & AI (Theory)

What is Data Science?

Difference between Data Analytics, Data Science, ML, and AI

Role of Data in Decision Making

Applications of AI in Industries (Finance, Healthcare, Manufacturing, NDE, etc.)

🔹 Session 2: Python for Data Analytics (Hands-on)

Jupyter Notebook Basics

Python libraries: NumPy, Pandas, Matplotlib, Seaborn

Reading & Cleaning Data (Handling missing values, duplicates, data types)

Data Manipulation with Pandas (Filtering, Sorting, Grouping)

Hands-on: Exploring a real-world dataset in Jupyter

🔹 Session 3: Mathematics Behind Data Science

Statistics Basics:

Mean, Median, Mode

Variance & Standard Deviation

Percentiles & Quartiles

Probability Basics:

Random Variables

Probability Distributions (Normal, Binomial, Poisson)

Conditional Probability & Bayes’ Theorem

Hands-on: Visualizing Probability Distributions in Jupyter

🔹 Session 4: Exploratory Data Analysis (EDA)●

Data Visualization (Histograms, Box Plots, Scatter Plots, Pair Plots)

Correlation & Covariance

Detecting Outliers, Trends, and Patterns

🔹 Session 5: Introduction to Regression & Hypothesis Testing

●Linear Regression Concept

Hypothesis Testing (p-value, t-test, ANOVA)

Confidence Intervals & Bootstrapping

Hands-on: Implementing Regression in Jupyter

🔹 End of Day 1 - Q&A, Discussion, and Take-home Exercise

NOTE: LAPTOP IS MANDATORY, SINCE HANDS-ON SESSIONS NEEDS PROGRAMMING