DATA SCIENCE BASIC

Python or R Programming – Data manipulation, scripting, visualization.

Statistics & Probability – Descriptive stats, hypothesis testing, distributions.

Data Wrangling & Cleaning – Handling missing values, outliers, and transformations.

Exploratory Data Analysis (EDA) – Visualizations using tools like Matplotlib, Seaborn.

Machine Learning – Supervised and unsupervised learning with Scikit-learn or TensorFlow.

SQL & Databases – Querying and managing data.

Big Data Technologies – Introduction to tools like Hadoop, Spark.

Data Visualization – Tools like Power BI, Tableau, Plotly.

Model Evaluation & Deployment – Cross-validation, overfitting, APIs, cloud deployment.

Capstone Projects – Real-world problems using datasets from Kaggle or other sources.