Page 11 - Touhpad Ai

4. Data Visualization
(i) Data Visualization using Python Programming.
Using matplotlib and seaborn in Python, and Excel charts; Handling missing values, outliers, and inconsistencies in
data using Python's pandas library and Excel's data cleaning features.
(ii) Data Visualization using Statistical Graphs

Types of Graphs-Bar Graph, Histogram, Scatter plot, Pie graph
(ii) Data Visualization using Statistical Graphs
Types of Graphs-Bar Graph, Histogram, Scatter plot, Pie graph

(iii) Introduction to Dimensionality of Data.
Multi-dimensional data representation and visualization using graphs.
5. Theoretical and Practical Aspects of Data Processing
(i) Introduction to Data Cleaning.

Data cleaning techniques with Pandas; Handling duplicates and Inconsistent data.
(ii) Exploring Kaggle Datasets.
Creating and manipulating Data Frames from Kaggle Datasets.
(iii) Data Transformation and Standardization.

Methods for transforming and standardizing data for analysis.
6. Data Modeling, Simple Linear Regression
(i) Introduction to Data Modeling.

A brief understanding of Types of Data Models (Dimensional, relational and entity relational).
(ii) Regression analysis.
Working of Regression (Dependent and Independent Variables), Types of Regression (in brief).
(iii) Linear Regression Equation.

Least Square Regression Line, Properties of Linear Regression, Regression coefficient, Types of Linear Regression
(in brief).
(iv) Solving Linear Equations.
Applications of Linear Equations in various contexts.
7. Ethical Practices in AI

AI code of ethics- avoiding bias, ensuring privacy of users and their data, and mitigating environmental, importance of
AI ethics –the effects of designing technology to replicate human life.

PAPER II: PRACTICALS – 30 Marks

The practical paper of three hours duration will be evaluated internally by the school. The paper shall consist of three
problem statements from which a candidate has to attempt any one problem statement.

The practical consists of two parts:
1. Planning/Writing Session
2. Examination Session

The total time to be spent on the Planning/Writing Session and the Examination session is three hours. A maximum of 90
minutes is permitted for the Planning/Writing Session and 90 minutes for the Examination session. Candidates are to be
permitted to proceed to the Examination Session only after the 90 minutes of the Planning /Writing Session are over.