WBCHSE HS Artificial Intelligence & Data Science study guide
WBCHSE HS Artificial Intelligence & Data Science · Work through your exam, one topic at a time.
The WBCHSE Higher Secondary Artificial Intelligence and Data Science is a Class XI–XII subject under the West Bengal Council of Higher Secondary Education. It gives school students a practical first layer of data literacy: how data is collected, cleaned, summarised and plotted, and how simple models are trained and evaluated on it. The WBCHSE HS Artificial Intelligence & Data Science study guide has 10 sections and about 2,991 words.
01Introduction to Artificial Intelligence
Introduction to Artificial Intelligence checks how AI is defined, how it differs from conventional computing, and how intelligent agents are described. Expect history milestones, the Turing Test, rational-agent thinking, and PEAS descriptions.
313 words, about 2 min02Python Programming for AI and Data Science
Python Programming for AI and Data Science tests core syntax plus the three libraries named in the syllabus: NumPy for arrays, pandas for tabular data, and Matplotlib for plots. Questions often combine data types, control flow and a short library operation.
304 words, about 2 min03Data Handling, Cleaning and Visualization
Data Handling, Cleaning and Visualization tests whether you can take raw data from common sources, repair quality problems, summarise variables, and choose a chart that answers a question. Missing values and chart selection are frequent exam targets.
339 words, about 2 min04Statistics and Probability Foundations
Statistics and Probability Foundations tests descriptive summaries, common distributions, correlation, sampling methods and basic probability rules. Expect calculation questions and interpretation questions where the wording matters.
311 words, about 2 min05Machine Learning Fundamentals
Machine Learning Fundamentals tests supervised versus unsupervised learning, regression versus classification, clustering, and model evaluation. Confusion-matrix calculations and overfitting are common exam items.
280 words, about 1 min06Neural Networks and Deep Learning
Neural Networks and Deep Learning tests the perceptron, layered networks, activation functions, backpropagation and applications of deep learning. Expect small forward-pass calculations and conceptual questions on training.
282 words, about 1 min07Natural Language Processing
Natural Language Processing tests text preprocessing, bag-of-words representation, sentiment analysis and chatbot or language-application concepts. Expect vocabulary and vector-counting questions plus interpretation of text pipelines.
276 words, about 1 min08Computer Vision and Image Processing
Computer Vision and Image Processing tests how digital images are represented, how features are extracted, how images are classified, and where vision is applied. Small convolution or pixel questions are likely.
294 words, about 1 min09AI Ethics, Safety, Privacy and Societal Impact
AI Ethics, Safety, Privacy and Societal Impact tests bias, data protection, safety, employment effects and social consequences. Answers must connect ethical principles to concrete design choices, not just list slogans.
286 words, about 1 min10Practical Work, Project Development and Case Studies
Practical Work, Project Development and Case Studies tests whether you can run a small end-to-end project on a real dataset: define a problem, clean data, explore it, build a baseline, train and evaluate a model, and communicate results. Project lifecycle: problem definition, data collection, cleaning, exploratory analysis, feature engineering, model training, evaluation, deployment or reporting.
306 words, about 2 min