Data Analysis Concepts
The everyday steps of turning raw data into insight.
This is an overview — pick a sub-topic below.
Sub-topics (10)
Data Cleaning and Wrangling
Fix types, remove duplicates, handle missing values and reshape data into a usable form.
Exploratory Data Analysis
Look before you model: summaries, distributions, correlations and plots.
Missing-Value Imputation
Fill gaps sensibly: forward-fill, interpolation, mean/median, or model-based imputation.
Outlier Detection
Spot unusual values with z-scores, the IQR rule, or models like Isolation Forest.
Data Transformation and Normalization
Scale, encode and reshape data so models and comparisons work.
Feature Engineering
Create informative inputs from raw data — rolling averages, ratios, time features, FFT bands.
Data Aggregation
Summarise with group-by, pivot tables and resampling.
Time-Series Analysis
Trends, seasonality, autocorrelation and forecasting for timestamped data.
Statistical Analysis
Hypothesis tests, confidence intervals and regression with scipy.stats and statsmodels.
Data Validation
Check data against rules (types, ranges, uniqueness) before using it.
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