Sport Data Science

Tech Stack

Here’s a comprehensive list of core and advanced tech stacks to refresh your skills and align with recent trends in the field.

1. Programming Languages

• Python: Core language; review libraries for data processing, machine learning, and visualization.
• R: (if relevant to your projects) Especially useful for statistics and specialized analysis.
• SQL: Essential for data extraction, querying, and database management.

2. Data Processing & Manipulation

• Pandas and NumPy: For data manipulation and numerical operations.
• Dask: For large-scale data processing when Pandas is not sufficient.
• PySpark: For distributed data processing in Hadoop and Spark environments.

3. Data Visualization

• Matplotlib and Seaborn: For static and detailed visualizations.
• Plotly and Altair: For interactive visualizations.
• Tableau or Power BI: Review if you need high-level business analytics and dashboards.

4. Machine Learning & Statistical Modeling

• Scikit-Learn: Core library for classical machine learning models and data preprocessing.
• XGBoost, LightGBM, and CatBoost: For advanced boosting methods.
• Statsmodels: For statistical models and hypothesis testing.
• TensorFlow and PyTorch: Essential for deep learning, with PyTorch becoming especially popular.