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Every company now runs on data — the ones that turn it into decisions win. Data analysis is the practical craft of pulling data, cleaning it, spotting what matters, and telling the story to people who can act on it.
What is Data Analysis?
Data analysis is the process of inspecting, cleaning, transforming, and modelling data to surface useful information. It sits between raw data pipelines and business decisions — combining SQL for pulling data, Excel or Python for shaping it, and BI tools like Power BI or Tableau for communicating findings.
Why learn Data Analysis?
Data analyst is one of the highest-demand entry points into tech and business. The skills are transferable across every industry, and they compound — analysts often grow into data scientists, product managers, or analytics leaders.
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Data Analysis — frequently asked questions
SQL + Excel first — they cover 80% of real-world analyst work. Add Power BI or Tableau for dashboarding, then Python once you outgrow the spreadsheet.
Basic descriptive statistics (mean, median, distributions, correlation) and an understanding of statistical significance cover most analyst roles. Deeper statistics is more relevant for data-scientist roles.
Analysts focus on describing what happened and why, using SQL, Excel, and BI tools. Data scientists lean on ML to predict what will happen. Analyst is often the on-ramp; many data scientists start as analysts.
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