Real data is messy. It has missing values, duplicate rows, inconsistent formats, and outliers that can quietly wreck a model. Experienced data scientists spend more time on this stage than on modelling, and it is where good analysis is won or lost.
You'll start with data cleaning: finding and handling missing values, fixing data types, removing duplicates, and deciding what to do with outliers. Then you'll move to exploratory data analysis (EDA): summarising columns, checking distributions, spotting correlations, and asking the questions that shape which features are worth keeping or building.
With a clean, well-understood dataset, you'll be ready for the next module, Data Visualization & Storytelling, where you'll turn what you found into charts other people can read.
Working through a realistic sales dataset: merging files, cleaning columns, fixing types, and answering business questions with pandas.
A full EDA walkthrough covering data shape, missing values, distributions, relationships between features, and how to spot problems early.
A longer course tying together NumPy, pandas, Matplotlib, and Seaborn through several complete analyses. Dip in for the sections you need.