Data Analyst Professional Certificate

Analyse data with spreadsheets, Python and SQL, back your conclusions with statistics, and present them in charts, dashboards and stories.

Clean, explore and chart data in spreadsheets and with pandas, query databases with SQL in DataGrip, and use statistics to tell real effects from noise. Then visualise data well in Python, build dashboards and tell stories with them, turn analyses into Streamlit apps, analyse product data with SQL, and use AI in business intelligence. Each course is hands-on, with datasets to work through and a project your instructors review.

Prepares you for

Data analyst, Business analyst, Reporting analyst, Product analyst, BI analyst

9 courses in this Professional Certificate

  • Spreadsheet Data Analysis: Formulas, Pivot Tables and Charts: Clean, look up, summarise and chart data in Excel, Google Sheets or LibreOffice Calc, then build what-if models with validation, scenario tables and Goal Seek. (Beginner, about 14 hours)
  • Data Analysis with Python and pandas: Load, clean, explore, combine and chart real-world data in Jupyter notebooks with pandas and matplotlib, and turn what you find into a clear, honest story. (Beginner, about 11 hours)
  • SQL for Data Analysis with DataGrip: Answer real business questions with SQL: filter, summarise, join and rank a shop's sales data in DataGrip, then export the results to share. (Beginner, about 10 hours)
  • Statistics for Data Science and AI: Describe data, reason with probability, and test ideas with confidence intervals, hypothesis tests, regression and A/B tests, in Python with pandas and SciPy. (Beginner, about 10 hours)
  • Data Visualisation with Matplotlib, Seaborn and Plotly: Choose the right chart, build it precisely with Matplotlib, explore data with seaborn, share interactive charts with Plotly, and make every figure clear, accessible and ready to publish. (Beginner, about 12 hours)
  • Data Storytelling and Dashboard Design: Turn analysis into a clear message for a real audience: choose and declutter charts, build a narrative that leads to a decision, and design honest, accessible KPI dashboards. (Beginner, about 13 hours)
  • Interactive Data Apps with Streamlit: Turn Python analysis into interactive web apps: widgets, layouts, charts, caching, session state, forms and multipage navigation, tested with AppTest and ready to deploy. (Intermediate, about 16 hours)
  • Product Analytics with SQL: Funnels, Cohorts and Retention: Turn raw product events into metrics a team can trust: active users, sessions, funnels, retention cohorts, attribution and experiment readouts, all in SQL. (Intermediate, about 14 hours)
  • AI-Powered Business Intelligence Fundamentals: Add AI to business intelligence: judge data readiness, model data for BI, build KPIs and forecasts, let people ask questions in plain language, and find the drivers and anomalies behind the numbers. (Intermediate, about 17 hours)