Today, as we have plenty of tools available for automation, the expectations to deliver fast and excellent result are increasing. So a data scientist needs to be adaptive of these tools and meet the growing demand for fast and reliable data analytics. So today I present you with 5 tricks to boost productivity and become better at rapid deliveries.
Trick Number 1: Leverage Automated Data Cleaning Tools
Cleaning and preprocessing data often takes up the majority of a data scientist’s time. Automating repetitive tasks can save hours. It is possible to streamline your data cleaning processes to be faster and less prone to manual errors.
Tools: Use libraries like Pandas Profiling, D-Tale, or Great Expectations to quickly analyze, validate, and clean datasets.
Bonus Tip: Write reusable scripts for common tasks (e.g., handling missing values or outliers) instead of rewriting code each time.
Trick Number 2: Master Jupyter Notebook Productivity Shortcuts
Jupyter Notebooks are the go-to tool for experimentation, but navigating them efficiently can make a huge difference. It is unlikely that in your career as a data scientist you will go without Jupyter Notebooks so the more you know about the platform and how to use it, the better for your productivity. These shortcuts also decrease the amount of frustration with having to endlessly click buttons and go to menus.
Examples:
• Use Shift + Enter to run cells and move to the next.
• Use Ctrl + Shift + – to split cells.
• Use %timeit and %prun magic commands for performance tuning.
• Learn all the other keyboard shortcuts that you need for your daily operations.
Bonus Tip: Use extensions like Jupyter Notebook Extensions or JupyterLab for enhanced functionality.
Trick Number 3: Adopt Version Control for Code and Data
Tracking changes in your code and data pipelines ensures reproducibility and avoids accidental loss of work. Write now version control is such a fundamental part of writing code that it should be mastered well.
Tools:
• Use Git and GitHub/GitLab for version control of code.
• For versioning datasets, tools like DVC (Data Version Control) or Pachyderm can be invaluable.
Bonus Tip: Use Git hooks to automate tasks like formatting or testing before commits.
Trick Number 4. Use Pre-built Pipelines and Frameworks
Reinventing the wheel for common tasks (e.g., model training or deployment) wastes time. If you are a beginner or intermediate data scientist, it is possible that you are not familiar with all the available tools and libraries that you can use on a daily basis. Getting a glipse of these which we have shortlisted for you can save you lots of time and make you less prone to mistakes.
Tools:
• Use frameworks like Scikit-learn Pipelines, TensorFlow Extended (TFX), or MLflow for building modular, end-to-end workflows.
• For ETL, explore tools like Apache Airflow or Prefect.
Bonus Tip: Always modularize your code so that each component (data processing, feature engineering, model evaluation) can be reused.
Trick Number 5. Embrace Visualization for Quick Insights
Every analysis should start with visual analysis for the data scientist to grasp the data distribution more thoroughly. Visualizations make it easier to spot trends, anomalies, and relationships early in your analysis.
Tools:
• Use anything that can plot big sets of data. Use libraries like Matplotlib for initial analysis and information, and Seaborn, Plotly, or Altair to create interactive, publication-ready visuals.
• Use dashboards like Streamlit, Dash, or Tableau for live, shareable insights.
Bonus Tip: Set up templates for your most-used visualization types to save setup time.
All in all, it is important to keep up with the trends and provide reliable analysis as fast as possible. These listed tools can help you speed up your process and improve your workflow.