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Guided learning journeys that build knowledge step by step.
category · Learning · slug · learning · 7812 Paths
7812 Paths · page 377 / 782
This learning path equips data analysts with the essential concepts and methods of inferential statistics to make data-driven business decisions. Starting from descriptive statistics, it covers sampling, confidence intervals, hypothesis testing, and key parametric and non-parametric tests, culminating in correlation and regression basics. Each step builds on the previous, ensuring a solid understanding of how to draw conclusions from sample data and quantify uncertainty.
This learning path guides business analysts through the core competencies of Power BI, from foundational data concepts to advanced data modeling and interactive report publishing. You will learn to transform data with Power Query, build a robust star schema, write DAX measures, and share insights via the Power BI Service.
This learning path guides business intelligence professionals from foundational data concepts and Tableau basics through advanced dashboard and story creation. It covers data connections, worksheet design, various chart types, calculated fields, and interactive dashboards, culminating in a capstone project. The path emphasizes practical application and real-world BI scenarios.
This learning path equips analysts with the skills to create clear, impactful visualizations that communicate data-driven insights. It covers visual perception principles, chart selection, design best practices, dashboard creation, and storytelling, with practical applications in Tableau and Power BI.
This learning path equips business analysts with the skills to apply data analytics to solve business problems and support decision-making. It covers data analysis basics, business intelligence, KPI identification, dashboard design, business reporting, stakeholder communication, and business case development, culminating in a capstone project.
This learning path equips data analysts with the essential concepts and techniques of descriptive statistics to understand data distributions and central tendencies. Starting with foundational math and data types, it progresses through measures of central tendency, variability, and distribution shape, culminating in practical applications like frequency analysis and summary tables. The path emphasizes conceptual understanding and hands-on practice to prepare learners for real-world data analysis.
This learning path equips data analysts with the knowledge and skills to clean and prepare data for accurate business analysis. It covers data quality assessment, handling missing values, deduplication, standardization, formatting, ETL basics, and data validation, with foundational SQL and Python concepts.
A systematic learning path for analysts to master Python fundamentals, NumPy, and Pandas for data cleaning and basic analysis, using Jupyter notebooks as the primary environment.
A beginner-friendly path for analysts to learn SQL for extracting, filtering, and aggregating data. It covers core querying, joins, aggregation, subqueries, window functions, date manipulation, and basic optimization, with a focus on business analysis applications.
This learning path equips business analysts with essential Excel skills for data cleaning, analysis, and reporting. Starting from foundational spreadsheet concepts, it progresses through formulas, data management, and advanced tools like PivotTables and Power Query, culminating in interactive dashboards and What-If analysis.