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Big Data Scientist Syllabus

Big Data Fundamentals and Architecture

Covers the core concepts of big data, distributed computing frameworks, storage systems, and cloud-native big data platforms.

22%
Weight
22
Questions
22
Marks

Big Data Fundamentals

  • The 5 Vs of Big Data (Volume, Velocity, Variety, Veracity, Value)
  • Batch vs stream processing
  • Data lake vs data warehouse vs data lakehouse
  • Lambda and Kappa architectures

Distributed Computing Frameworks

  • Apache Hadoop ecosystem (HDFS, MapReduce, YARN)
  • Apache Spark (RDDs, DataFrames, Spark SQL, Spark Streaming)
  • Apache Kafka for real-time streaming
  • Apache Hive and HBase

Cloud Big Data Platforms

  • AWS big data services (EMR, Kinesis, Redshift, Glue, Athena)
  • Azure big data services (HDInsight, Synapse Analytics, Data Factory, Event Hubs)
  • GCP big data services (BigQuery, Dataflow, Pub/Sub, Dataproc)
  • Cloud storage for big data (S3, ADLS, GCS)

Machine Learning and AI for Big Data

Covers supervised and unsupervised learning algorithms, deep learning, MLOps, and applying ML at scale on distributed data platforms.

25%
Weight
25
Questions
25
Marks

Supervised Learning

  • Regression (linear, logistic, ridge, lasso)
  • Classification (decision trees, random forests, gradient boosting, XGBoost)
  • Support Vector Machines
  • Ensemble methods and model stacking

Unsupervised Learning

  • Clustering (K-Means, DBSCAN, hierarchical clustering)
  • Dimensionality reduction (PCA, t-SNE, UMAP)
  • Anomaly detection
  • Association rule mining

Deep Learning

  • Neural network architectures (feedforward, CNN, RNN, LSTM)
  • Transformer models and attention mechanisms
  • Transfer learning and fine-tuning
  • Deep learning frameworks (TensorFlow, PyTorch)

MLOps and Model Lifecycle

  • Model training pipelines
  • Feature engineering and feature stores
  • Model versioning and experiment tracking (MLflow)
  • Model deployment (batch, real-time, edge)
  • Model monitoring and drift detection

Data Engineering and Pipelines

Covers ETL/ELT processes, data pipeline design, data integration, orchestration, and data storage technologies.

22%
Weight
22
Questions
22
Marks

ETL and ELT Processes

  • ETL vs ELT trade-offs
  • Data extraction from APIs, databases, streams
  • Data transformation (cleansing, deduplication, normalization)
  • Data loading strategies (full load, incremental, CDC)

Pipeline Orchestration

  • Apache Airflow (DAGs, operators, sensors)
  • Cloud workflow services (AWS Step Functions, Azure Data Factory, GCP Cloud Composer)
  • Pipeline monitoring and alerting

Data Storage Technologies

  • Columnar storage formats (Parquet, ORC, Avro)
  • NoSQL databases (MongoDB, Cassandra, DynamoDB, Redis)
  • Time-series databases
  • Graph databases (Neo4j)
  • Data catalog and metadata management

Advanced Analytics and Visualization

Covers statistical analysis, time-series analysis, NLP, graph analytics, and data visualization best practices.

16%
Weight
16
Questions
16
Marks

Statistical Analysis

  • Descriptive statistics
  • Hypothesis testing (t-test, chi-square, ANOVA)
  • Bayesian inference basics
  • Sampling methods

Specialized Analytics

  • Time-series forecasting (ARIMA, SARIMA, Prophet)
  • Natural Language Processing (text classification, NER, sentiment analysis)
  • Graph analytics and network analysis
  • Recommendation systems (collaborative and content-based filtering)

Data Visualization

  • Visualization best practices
  • BI tools (Tableau, Power BI, Looker)
  • Python visualization (Matplotlib, Seaborn, Plotly)
  • Dashboard design for stakeholder communication

Data Governance, Ethics, and Security

Covers data quality management, privacy regulations, responsible AI, data security, and governance frameworks.

15%
Weight
15
Questions
15
Marks

Data Quality and Governance

  • Data quality dimensions (accuracy, completeness, consistency, timeliness)
  • Data lineage and provenance
  • Data catalog and metadata management
  • Master data management (MDM)

Privacy and Regulation

  • GDPR requirements for big data systems
  • CCPA and global data privacy laws
  • Data anonymization and pseudonymization techniques
  • Right to erasure in big data systems

Responsible AI and Security

  • AI bias and fairness metrics
  • Model explainability (SHAP, LIME)
  • Data security (encryption at rest and in transit)
  • Access control and data masking
  • Ethical considerations in data science

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Simulate real exam conditions with timed practice tests

  • Exam-like environment
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Practice Tests

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Visual learning tools to connect and remember concepts

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  • Topic relationships

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  • Topic-wise performance analysis
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Learning Progress

Big Data Fundamentals and Architecture 85%
Machine Learning and AI for Big Data 92%

Practice Test Scores

95%
Latest Score
Above passing threshold

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