Data Science Syllabus, Exam Pattern

Data Science Syllabus

Data Science :

1. Foundations of Data Science

i. Introduction to Data Science: Definition, scope, lifecycle

ii. Applications of Data Science (Business, Healthcare, Finance, Agriculture, Social Media, IoT)

iii. Data Science vs Data Analytics vs AI vs ML

iv. Overview of Data-driven decision-making

2. Mathematics and Statistics for Data Science

i. Linear Algebra: Vectors, Matrices, Eigenvalues/Eigenvectors

ii. Calculus: Differentiation, Partial Derivatives, Optimization

iii. Probability Theory: Random variables, Probability distributions, Bayes’ theorem

Statistics:

i. Descriptive statistics: Mean, Median, Variance, Standard Deviation

ii. Inferential statistics: Hypothesis testing, Confidence intervals, p-value

iii. Correlation and Regression

iv. Time Series Analysis: Trend, seasonality, ARIMA

3. Programming for Data Science

i. Python / R for Data Science

ii. Data Structures and Algorithms for large datasets

iii. Libraries: NumPy, Pandas, SciPy, Matplotlib, Seaborn, Plotly

iv. Working with SQL and NoSQL databases

v. Data Wrangling: Handling missing data, outliers, normalization, feature engineering

4. Data Collection and Management

i. Data sources: Databases, APIs, Web scraping, Sensors

ii. Data Warehousing and ETL processes

iii. Big Data ecosystems: Hadoop, Spark, Hive, HDFS

iv. Cloud-based Data Storage (AWS S3, Google BigQuery, Azure Data Lake)

v. Data Governance, Privacy, and Security

5. Data Visualization and Communication

i. Principles of data visualization

ii. Tools: Tableau, Power BI, Python (Matplotlib, Seaborn, Plotly)

iii. Interactive Dashboards (Dash, Streamlit, Shiny)

iv. Storytelling with Data: Communicating insights effectively

6. Data Science for Data Science

i. Supervised Learning: Regression, Decision Trees, Random Forests, SVM, Neural Networks

ii. Unsupervised Learning: k-Means, Hierarchical Clustering, PCA, t-SNE

Reinforcement Learning basics

i. Model evaluation metrics: Accuracy, Precision, Recall, F1-score, ROC-AUC

ii. Hyperparameter tuning (Grid Search, Random Search, Bayesian Optimization)

7. Deep Learning and Neural Networks

i. Fundamentals of Neural Networks (Perceptron, MLP)

ii. Backpropagation, Gradient Descent

iii. Convolutional Neural Networks (CNNs) for image data

iv. Recurrent Neural Networks (RNNs, LSTMs, GRUs) for sequential data

v. Transfer Learning and Pre-trained Models

vi. Frameworks: TensorFlow, PyTorch, Keras

8. Natural Language Processing (NLP) in Data Science

i. Text preprocessing: Tokenization, Lemmatization, Stop words

ii. Sentiment Analysis and Topic Modeling

iii. Word embeddings: Word2Vec, GloVe, FastText

iv. Transformers and Large Language Models: BERT, GPT

v. Applications: Chatbots, Text summarization, Machine translation

9. Big Data Analytics

i. Characteristics of Big Data (Volume, Velocity, Variety, Veracity, Value)

ii. Distributed computing with Hadoop & Spark

iii. MapReduce framework

iv. Real-time Data Analytics (Kafka, Flink, Storm)

v. Case Studies: Recommendation systems, Fraud detection, Social media analytics

10. Data Science Project Lifecycle

i. Problem formulation & business understanding

ii. Data collection, cleaning, preprocessing

iii. Model building and validation

iv. Deployment (Flask, FastAPI, Docker, Cloud platforms)

v. Monitoring and maintenance

vi. Documentation & Presentation

11. Domain-Specific Applications

i. Data Science in Healthcare (disease prediction, drug discovery)

ii. Data Science in Finance (risk management, fraud detection)

iii. Data Science in Marketing (customer segmentation, recommendation systems)

iv. Data Science in Agriculture (crop prediction, soil monitoring)

v. Smart cities, IoT, and Sustainability applications

12. Ethics, Privacy, and Responsible Data Science

i. Data privacy (GDPR, HIPAA, Indian PDP Bill)

ii. Bias, fairness, and transparency in models

iii. Explainable AI (SHAP, LIME)

iv. Social implications of data-driven systems

13. Tools and Technologies in Data Science

i. Programming: Python, R, SQL, Julia

ii. Big Data Tools: Hadoop, Spark, Hive, Pig

iii. Data Visualization: Tableau, Power BI, D3.js

iv. ML/DL Frameworks: TensorFlow, PyTorch, Scikit-learn, Keras

v. Cloud AI Services: AWS SageMaker, Google Vertex AI, Azure ML

Data Science Exam Pattern

Duration : 60 Minutes

No.of Question : 100

Total Marks : 100

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