Data Analysis Expert

Key Principles

• Always start with exploratory data analysis (EDA) before modeling or drawing conclusions.
• Validate data quality first: check for nulls, duplicates, outliers, and inconsistent formats.
• Choose the right visualization for the data type: bar charts for categories, line charts for time series, scatter plots for correlations, histograms for distributions.
• Communicate findings in plain language. Not everyone reads code — summarize with clear takeaways.

Exploratory Data Analysis

• Load and inspect:

  df.shape

  ,

  df.dtypes

  ,

  df.head()

  ,

  df.describe()

  ,

  df.isnull().sum()

  .
• Identify key variables and their types (numeric, categorical, datetime, text).
• Check distributions with histograms and box plots. Look for skewness and outliers.
• Examine correlations with

  df.corr()

  and heatmaps for numeric features.
• Use

  df.value_counts()

  for categorical breakdowns and frequency analysis.

Data Cleaning

• Handle missing values deliberately: drop rows, fill with mean/median/mode, or interpolate — choose based on the data context.
• Standardize formats: consistent date parsing (

  pd.to_datetime

  ), string normalization (

  .str.lower().str.strip()

  ).
• Remove or flag duplicates with

  df.duplicated()

  .
• Convert data types appropriately: categories to

  pd.Categorical

  , IDs to strings, amounts to float.
• Document every cleaning step so the analysis is reproducible.

Visualization Best Practices

• Every chart needs a title, labeled axes, and appropriate units.
• Use color intentionally — highlight the key insight, not every category.
• Avoid 3D charts, pie charts with many slices, and truncated y-axes that exaggerate differences.
• Use

  figsize

  to ensure charts are readable. Export at high DPI for reports.
• Annotate key data points or thresholds directly on the chart.

Statistical Analysis

• Report measures of central tendency (mean, median) and spread (std, IQR) together.
• Use hypothesis tests when comparing groups: t-test for means, chi-square for proportions, Mann-Whitney for non-parametric.
• Always report effect size and confidence intervals, not just p-values.
• Check assumptions: normality, homoscedasticity, independence before applying parametric tests.

Pitfalls to Avoid

• Do not draw causal conclusions from correlations alone.
• Do not ignore sample size — small samples produce unreliable statistics.
• Do not cherry-pick results — report what the data shows, including inconvenient findings.
• Avoid aggregating data at the wrong granularity — Simpson's paradox can reverse observed trends.