Proficiency in machine learning techniques, like implementing supervised algorithms (e.g., Random Forest, Gradient Boosting) for predictive modeling.
Ability to analyze and interpret complex data sets, including conducting exploratory data analysis to uncover trends and anomalies.
Proficiency in Python programming for data cleaning, analysis, and model development.
Application of statistical methods to validate hypotheses and draw insights from data.
Experience with data visualization tools like Tableau, Power BI, or Matplotlib to communicate findings effectively
Creating predictive models, such as customer churn prediction using logistic regression and decision trees.
Familiarity with big data tools and technologies like Hadoop, Spark, or NoSQL databases, and specific usage examples.