Data science analyst -For Advanced level

Data Science Analyst
(Expert Level)
1. Advanced Machine Learning

• Ensemble Learning: Techniques like Bagging, Boosting
(AdaBoost, XGBoost, LightGBM), and Stacking to improve model
performance.
• Deep Learning:
o Neural Networks (ANN, CNN, RNN, LSTMs) for complex
data patterns.
o Transfer Learning: Using pre-trained models for tasks.
o Autoencoders & GANs: Unsupervised learning for feature
extraction and synthetic data generation.
• Reinforcement Learning:
o Reward-based learning techniques.
o Q-learning and Deep Q-Networks (DQN).


2. Big Data Technologies

• Hadoop & Spark: Distributed data processing frameworks.
• Distributed Computing: Managing large-scale computations
across multiple nodes.
• NoSQL Databases: MongoDB, Cassandra for handling
unstructured data.

3. Advanced Data Processing

• ETL Pipelines: Extract, Transform, Load processes for data
ingestion.
• Data Warehousing: Cloud-based solutions like Snowflake,
Redshift, BigQuery.
• Real-time Data Processing: Apache Kafka, Spark Streaming.