A Data Science mock test from Toppersexam.com is a practice paper based on the latest exam pattern and syllabus, designed to help you assess your preparation level.
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To crack the Data Science 2026 exam, understanding the detailed syllabus and exam pattern is mandatory. Aspirants should focus on strategic planning and rigorous time management for each selection phase. Boosting your speed through online mock tests and regular practice is the key to securing a high score in this competitive recruitment. For premium study material and practice sets, follow the expert curated resources on toppersexam.com.
Data Science Free Mock Test
Crack the Data Science Mock Test 2026 with the help of an online Test series or a free mock test. Every sample paper in the Data Science Exam has a designated weight, so do not miss out on any paper. Prepare for the Data Science mock test and check your test scores.
Data Science Syllabus, Exam Pattern
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
Data Science Free Online Test Series
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Data Science Exam Free Online Question Bank
Data Science question answer with solutions are available in Hindi & English. Join MCQ, Important Questions, Question Bank, questions and answers for Free Data Science exam preparation.
FAQs
Data Science Free Mock Test Frequently Asked Questions (FAQ's)
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