Requires a combination of foundational knowledge, technical skills, and domain expertise
Entry Level
aspiring data scientists must develop a foundation in mathematics and statistics
probability theory
linear algebra
calculus
machine learning algorithms
proficiency in programming languages such as Python or R (widely used for data manipulation, statistical analysis and model development
Understand databases and SQL for data extraction
querying
familiar with exploratory data analysis, data preprocessing techniques, basic machine learning algorithms (linear regression, logistic regression, and decision trees)
expertise in machine learning, artificial intelligence, big data technologies
ensemble learning
deep learning with neural networks
unsupervised learning (Clustering and anomaly detection)
Knowledge of cloud computing platforms
AWS
Azure
Google Cloud
Big data processing tools for large-scale datasets
Apache Spark
Hadoop
Mid-Level
model evaluation techniques
hyperparameter tuning
feature engineering to optimize predictive performance
version control (Git)
containerization (Docker)
CI/CD pipelines
Expert
strategic understanding of business and industry-specific applications
to design end-to-end machine learning pipelines that provide real-world value
deep reinforcement decision-making problems
expected to lead cross-functional teams, mentor junior data scientists, and communicate insights to non-technical stakeholders through compelling storytelling and visualization
stay updated with the latest research in AI, evolving algorithms and technique
contribute to open-source projects or academic research further distinguish expert-level scientists
