Data science is often associated with AI and machine learning, but data scientists also perform essential human-focused tasks.
1. Conversation with Clients
Level of Automation: Impossible
A key task of a data scientist is direct client communication. This helps eliminate miscommunication and align projects with expectations.
Currently, no machine can fully replicate human conversation or understand a client’s ideas, goals, and preferences.
2. Data Preparation and Cleaning
Level of Automation: Partially Automated
Data preparation and cleaning consumes about 80% of a data scientist’s time. This stage involves:
- Joining different types of data
- Removing errors
- Gaining access to data
Many repetitive cleaning tasks can be automated using heuristics.
- Examine date distributions to check for weekends, holidays, or recurring events
- Review manually entered category columns and correct errors
- Inspect numerical columns to ensure values are within reasonable ranges and flag anomalies
Tools for Automating Data Cleaning
Data scientists can speed up cleaning tasks with tools like:
- DORA
- DataCleaner
- Pretty Pandas
- Tabulate
- Scrubadub
- Arrow
- Beautifier
- FTFY
3. Data Exploration and Feature Engineering
Level of Automation: Partially Automated, High Potential
After cleaning data, data scientists explore it and perform feature engineering. This step has two components:
- Data Exploration: Understanding the data patterns
- Feature Engineering: Applying domain knowledge to create meaningful features
Many aspects of exploration can be automated using libraries like NumPy and Pandas for analysis and Seaborn, Matplotlib, or Plotly for visualization.
4. Modeling
Level of Automation: Fully Automated
Modeling transforms clean, structured data into actionable results. Modeling includes:
- Model construction
- Validation
- Hyperparameter optimization
Tools for Automating Modeling
Popular automated modeling tools include:
- Run:AI
- AutoKeras
- Auto-WEKA
- DataRobot Automated Machine Learning
- H2O AutoML
- MLBox
- auto-sklearn
These tools allow data scientists to quickly train, validate, and optimize models with minimal manual intervention.
Conclusion
Automation in data science is increasingly feasible and widely adopted. It helps data scientists reduce errors, clean data more efficiently, and streamline the modeling process.