Bestseller
Bestseller
A practical Data Science course can be structured around Python, SQL, statistics, data analysis, visualization, machine learning, and real-world projects. These are common components in current Data Science curricula.
Python for Data Science Python basics, functions, OOP, file handling
NumPy & PandasArrays, DataFrames, data manipulation, data cleaning
StatisticsProbability, descriptive statistics, hypothesis testing
SQL & DatabaseMySQL, queries, joins, subqueries, aggregation
Data AnalysisEDA, data preprocessing, feature engineering
Data VisualizationMatplotlib, Seaborn, Plotly
Power BIData modeling, dashboards, reports
Machine LearningRegression, classification, clustering, model evaluation
Deep LearningNeural networks, TensorFlow/Keras basics
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