What is Data Science?

Data Science Life Cycle

Applications in real-world domains

NumPy

Arrays, operations, broadcasting, indexing

Pandas

Series, DataFrames, indexing, filtering, grouping

Matplotlib

Basic plotting, line/bar charts

Seaborn

Statistical plots, styling, correlation heatmaps

Handling missing data

NaN, imputation methods

Dropping/Filtering rows and columns

Handling duplicates

Data type conversions

Feature scaling

MinMax, StandardScaler

Encoding categorical variables

Label, One-hot

Binning, log transforms, datetime handling

Combining and merging datasets

Statistical Measures

Mean, Median, Mode, Variance, Std Dev, Skewness, Kurtosis

Value counts, groupby summaries

Basic plots

Histograms, box plots, violin plots

Advanced plots

Pair plots, scatter plots, bar charts

Correlation matrices and heatmaps

Titanic Dataset Survival Analysis

Iris Dataset Classification Overview

COVID/Crime/Sales Data Trend Visualization

Reading from CSV, Excel, JSON

Scraping data

BeautifulSoup basics & advanced

API calls using requests

Probability distributions

Bayes Theorem basics

Outlier detection methods

IQR, Z-score

Train-test split

Using sklearn

Feature selection

Understanding bias-variance