Most of the real-world projects that I checked at H2kinfosys, In which processing and cleaning data take up 60–70% percent of an analyst’s time because if you haven’t cleaned your data, or the quality is poor then most likely result will be misleading. Re-concilliating missing values, removing duplicates, fixing nonsensical data and normalization of formats is vital before any analysis gestates. Best practices are source data verification, consistent naming conventions, documented transformation steps and the use of tools such as Python (Pandas) or SQL for structured cleaning processes. Routine data audits and automated quality regulation can also cut long-term errors. Some of these basics can be mastered by applying them to real world dataset,an Data analysis course online in real scenarios Many, however apply the fundamentals and they are able get much better at it.