Build practical data quality and debugging skills that help you investigate issues, clean datasets, and validate reporting outputs with confidence. In this course, you’ll develop hands-on experience used in roles such as data analyst, reporting analyst, business analyst, operations analyst, and data quality analyst. You’ll practice profiling datasets, summarizing data characteristics with queries and programming techniques, identifying common quality issues, and tracing likely sources of problems that can affect reporting and analysis.

This is a non-traditional, skill-based learning experience organized around real workplace tasks instead of a fixed lecture sequence. It’s designed to reflect responsibilities you may see in job descriptions, from handling missing values and removing duplicate records to validating report outputs and debugging SQL logic when data discrepancies appear. You can personalize your path based on what you already know, focus on the skills you need most, and skip content when it’s not necessary. The course curates high-quality lessons from expert instructors, selecting the strongest content for each skill so you can build practical, career-relevant data quality experience. By the end, you’ll be able to profile datasets using basic queries and programming techniques, identify likely data quality issues through routine checks, address missing values and duplicate records, perform validation checks on report data, and debug SQL queries to resolve logical errors and reporting discrepancies. This course is a strong fit if you already have basic experience with datasets, spreadsheets, SQL, or reporting and analysis workflows.

Strengthen your core data analysis skills by learning how to summarize, profile, and explore datasets in ways that support real business questions. In this course, you’ll build practical experience used in roles such as data analyst, business analyst, reporting analyst, operations analyst, and marketing analyst. You’ll work with common analysis techniques to examine distributions, summarize categorical and numeric data, compare variables, and support segmentation through filtering and exploratory visuals.

This is a non-traditional, skill-based learning experience organized around real workplace tasks instead of a fixed lecture sequence. It’s designed to reflect responsibilities you may see in job descriptions, from profiling datasets and building summary tables to exploring variable relationships and helping stakeholders investigate data by segment. You can personalize your path based on what you already know, focus on the skills you need most, and skip content when it’s not necessary. The course curates high-quality lessons from expert instructors, selecting the strongest content for each skill so you can build practical, career-relevant data analysis experience. By the end, you’ll be able to use aggregation, cross-tabulation, frequency analysis, and measures of central tendency and dispersion to summarize data, apply charts and graphical methods to understand distributions and relationships, and use filtering and correlation techniques to support segmentation and exploratory analysis. This course is a strong fit if you already have basic experience with spreadsheets, data analysis, or working with tables and charts.