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Workflow & Data

Data science lifecycle Tabular dataset Target variable Feature matrix Train validation test split Data dictionary Reproducible random seed Notebook versus script Data provenance Experiment tracking

NumPy

NumPy array creation Array shape Array dtype Array slicing Boolean masking Broadcasting Vectorized arithmetic Aggregation reshape Matrix multiplication

pandas

DataFrame creation Read CSV Select columns Filter rows sort_values groupby merge pivot_table set_index Missing values

Cleaning

Drop duplicates Fill missing numeric values Fill missing categorical values Parse dates String cleanup Outlier detection IQR Type conversion Category dtype Rename columns Validate ranges

EDA & Statistics

Descriptive statistics Mean median mode Variance standard deviation Quantiles Correlation Frequency table Crosstab Histogram Box plot Scatter plot

Visualization

Matplotlib line chart Matplotlib bar chart Matplotlib labels Multiple series Seaborn concept without dependency Plot distribution Plot correlation matrix Time-series plot Save figure Accessible chart labels

Feature Engineering

One-hot encoding Ordinal encoding Standard scaling Min max scaling Log transform Polynomial features Date parts Text length feature Interaction feature Pipeline concept

Modeling & Evaluation

Linear regression Logistic regression Decision tree concept K nearest neighbors K means clustering Confusion matrix Precision recall Cross validation Baseline model Model persistence

Data Science Foundations / Cleaning

Parse dates

Learn Parse dates in Data Science Foundations with a focused explanation, a topic-specific example, edge guidance, and a mastery check.

12 min read • Intermediate

data-science-034.py

python

import pandas as pd
raw=pd.Series(["2026-09-01","2026-09-14","bad"])
print(pd.to_datetime(raw,errors="coerce"))

What to watch

Content source & attribution

Original Syntaxis Content · Original Syntaxis Content

Lesson quizzes

Parse dates Mastery Check →

Fill missing categorical values