Foundations of analytical thinking for aspiring Data Analysts
  • Identify patterns, trends, and anomalies in datasets using tools like pandas and matplotlib
  • Apply exploratory data analysis techniques such as aggregation and visualizations (e.g., histograms, scatter plots, boxplots)
  • Formulate analytical questions and explore them using structured data


Data preparation and workflow efficiency
  • Prepare data with pandas, including reading files, handling missing values, and working with date formats
  • Streamline analysis using techniques such as boolean masking and table merging
  • Apply structured workflows using SQL, APIs, and web data extraction to enhance clarity and repeatability


Essential skills for the modern Data Analyst
  • Apply analytical methods in practice-based projects like taxi trip analysis and churn rate evaluation
  • Work with real datasets from sources such as Kaggle while considering dataset licensing
  • Collaborate using structured workflows (e.g., Data Analytics Workflow, Git, Kanban) in team-based data projects